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ORCIDEric P. Xing
Eric Poe Xing
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- affiliation: Carnegie Mellon University, School of Computer Science, Pittsburgh, PA, USA
- affiliation: Petuum Inc., Pittsburgh, PA, USA
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2020 – today
- 2025
[i272]Zhengzhong Liu, Bowen Tan, Hongyi Wang, Willie Neiswanger, Tianhua Tao, Haonan Li, Fajri Koto, Yuqi Wang, Suqi Sun, Omkar Pangarkar, Richard Fan, Yi Gu, Victor Miller, Liqun Ma, Liping Tang, Nikhil Ranjan, Yonghao Zhuang, Guowei He, Renxi Wang, Mingkai Deng, Robin Algayres, Yuanzhi Li, Zhiqiang Shen, Preslav Nakov, Eric P. Xing:
LLM360 K2: Building a 65B 360-Open-Source Large Language Model from Scratch. CoRR abs/2501.07124 (2025)- [i271]Lingjing Kong, Guangyi Chen, Petar Stojanov, Haoxuan Li, Eric P. Xing, Kun Zhang:
Towards Understanding Extrapolation: a Causal Lens. CoRR abs/2501.09163 (2025) - [i270]Wenhao Zheng, Yixiao Chen, Weitong Zhang, Souvik Kundu, Yun Li, Zhengzhong Liu, Eric P. Xing, Hongyi Wang, Huaxiu Yao:
CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing. CoRR abs/2502.01976 (2025) - [i269]Afonso Lourenço, João Gama, Eric P. Xing, Goreti Marreiros:
In-context learning of evolving data streams with tabular foundational models. CoRR abs/2502.16840 (2025) - 2024
- [j80]Nanqing Dong
, Michael Kampffmeyer, Haoyang Su, Eric P. Xing:
An exploratory study of self-supervised pre-training on partially supervised multi-label classification on chest X-ray images. Appl. Soft Comput. 163: 111855 (2024) - [j79]Ding Bai, Caleb N. Ellington, Shentong Mo, Le Song, Eric P. Xing:
AttentionPert: accurately modeling multiplexed genetic perturbations with multi-scale effects. Bioinform. 40(Supplement_1): i453-i461 (2024) - [j78]Gongjie Zhang, Zhipeng Luo, Jiaxing Huang, Shijian Lu, Eric P. Xing:
Semantic-Aligned Matching for Enhanced DETR Convergence and Multi-Scale Feature Fusion. Int. J. Comput. Vis. 132(8): 2825-2844 (2024) - [j77]Caleb N. Ellington, Benjamin J. Lengerich, Wesley Lo, Aaron Alvarez, Andrea Rubbi, Manolis Kellis, Eric P. Xing:
Contextualized: Heterogeneous Modeling Toolbox. J. Open Source Softw. 9(97): 6469 (2024) - [j76]Nanqing Dong, Zhipeng Wang, Jiahao Sun, Michael Kampffmeyer, William J. Knottenbelt, Eric P. Xing:
Defending Against Poisoning Attacks in Federated Learning With Blockchain. IEEE Trans. Artif. Intell. 5(7): 3743-3756 (2024) - [j75]Hanlin Zhang, Shuai Lin, Weiyang Liu, Pan Zhou, Jian Tang, Xiaodan Liang, Eric P. Xing:
Iterative Graph Self-Distillation. IEEE Trans. Knowl. Data Eng. 36(3): 1161-1169 (2024) - [j74]Shuai Lin, Chen Liu, Pan Zhou, Zi-Yuan Hu, Shuojia Wang, Ruihui Zhao, Yefeng Zheng, Liang Lin, Eric P. Xing, Xiaodan Liang:
Prototypical Graph Contrastive Learning. IEEE Trans. Neural Networks Learn. Syst. 35(2): 2747-2758 (2024) - [c381]Hanoona Abdul Rasheed, Muhammad Maaz, Sahal Shaji Mullappilly, Abdelrahman M. Shaker, Salman H. Khan, Hisham Cholakkal, Rao Muhammad Anwer, Eric P. Xing, Ming-Hsuan Yang, Fahad Shahbaz Khan:
GLaMM: Pixel Grounding Large Multimodal Model. CVPR 2024: 13009-13018 - [c380]Adilbek Karmanov, Dayan Guan, Shijian Lu, Abdulmotaleb El-Saddik, Eric P. Xing:
Efficient Test-Time Adaptation of Vision-Language Models. CVPR 2024: 14162-14171 - [c379]Jiahui Zhang, Fangneng Zhan, Muyu Xu, Shijian Lu, Eric P. Xing:
FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization. CVPR 2024: 21424-21433 - [c378]Han Guo, William Brandon, Radostin Cholakov, Jonathan Ragan-Kelley, Eric P. Xing, Yoon Kim:
Fast Matrix Multiplications for Lookup Table-Quantized LLMs. EMNLP (Findings) 2024: 12419-12433 - [c377]Somanshu Singla, Zhen Wang, Tianyang Liu, Abdullah Ashfaq, Zhiting Hu, Eric P. Xing:
Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models. EMNLP 2024: 21889-21909 - [c376]Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric P. Xing, Mikhail Yurochkin:
Fusing Models with Complementary Expertise. ICLR 2024 - [c375]Han Guo, Philip Greengard, Eric P. Xing, Yoon Kim:
LQ-LoRA: Low-rank plus Quantized Matrix Decomposition for Efficient Language Model Finetuning. ICLR 2024 - [c374]Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric P. Xing, Zhiting Hu:
PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization. ICLR 2024 - [c373]Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Tianle Li, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zhuohan Li, Zi Lin, Eric P. Xing, Joseph E. Gonzalez, Ion Stoica, Hao Zhang:
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset. ICLR 2024 - [c372]Jannik Deuschel, Caleb Ellington, Yingtao Luo, Benjamin J. Lengerich, Pascal Friederich, Eric P. Xing:
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning. ICML 2024 - [c371]Yue Huang, Lichao Sun, Haoran Wang, Siyuan Wu, Qihui Zhang, Yuan Li, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Hanchi Sun, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric P. Xing, Furong Huang, Hao Liu, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, Joaquin Vanschoren, John C. Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yong Chen, Yue Zhao:
Position: TrustLLM: Trustworthiness in Large Language Models. ICML 2024 - [c370]Guangyi Liu, Yu Wang, Zeyu Feng, Qiyu Wu, Liping Tang, Yuan Gao, Zhen Li, Shuguang Cui, Julian J. McAuley, Zichao Yang, Eric P. Xing, Zhiting Hu:
Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding. ICML 2024 - [c369]Song Bian, Dacheng Li, Hongyi Wang, Eric P. Xing, Shivaram Venkataraman:
Does Compressing Activations Help Model Parallel Training? MLSys 2024 - [c368]Bowen Tan, Yun Zhu, Lijuan Liu, Hongyi Wang, Yonghao Zhuang, Jindong Chen, Eric P. Xing, Zhiting Hu:
RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs. NAACL (Demonstrations) 2024: 137-147 - [c367]Yifan Zhang, Hanlin Zhang, Li Li, Eric P. Xing:
Evaluating Step-by-Step Reasoning through Symbolic Verification. NAACL-HLT (Findings) 2024: 2984-3002 - [c366]Hanlin Zhang, Yifan Zhang, Yaodong Yu, Dhruv Madeka, Dean P. Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade:
A Study on the Calibration of In-context Learning. NAACL-HLT 2024: 6118-6136 - [c365]Aviv Bick, Kevin Y. Li, Eric P. Xing, J. Zico Kolter, Albert Gu:
Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models. NeurIPS 2024 - [c364]Lingjing Kong, Guangyi Chen, Biwei Huang, Eric P. Xing, Yuejie Chi, Kun Zhang:
Learning Discrete Concepts in Latent Hierarchical Models. NeurIPS 2024 - [c363]Lingjing Kong, Guangyi Chen, Petar Stojanov, Haoxuan Li, Eric P. Xing, Kun Zhang:
Towards Understanding Extrapolation: a Causal Lens. NeurIPS 2024 - [c362]Sukmin Yun, Haokun Lin, Rusiru Thushara, Mohammad Qazim Bhat, Yongxin Wang, Zutao Jiang, Mingkai Deng, Jinhong Wang, Tianhua Tao, Junbo Li, Haonan Li, Preslav Nakov, Timothy Baldwin, Zhengzhong Liu, Eric P. Xing, Xiaodan Liang, Zhiqiang Shen:
Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs. NeurIPS 2024 - [i268]Jiaxing Huang, Kai Jiang, Jingyi Zhang, Han Qiu, Lewei Lu, Shijian Lu, Eric P. Xing:
Learning to Prompt Segment Anything Models. CoRR abs/2401.04651 (2024) - [i267]Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric P. Xing, Furong Huang, Hao Liu, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, John C. Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yue Zhao:
TrustLLM: Trustworthiness in Large Language Models. CoRR abs/2401.05561 (2024) - [i266]Loka Li, Guangyi Chen, Yusheng Su, Zhenhao Chen, Yixuan Zhang, Eric P. Xing, Kun Zhang:
Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models. CoRR abs/2402.12563 (2024) - [i265]Hanqi Yan, Lingjing Kong, Lin Gui, Yuejie Chi, Eric P. Xing, Yulan He, Kun Zhang:
Counterfactual Generation with Identifiability Guarantees. CoRR abs/2402.15309 (2024) - [i264]Omkar Thawakar, Ashmal Vayani, Salman H. Khan, Hisham Cholakkal, Rao Muhammad Anwer, Michael Felsberg, Tim Baldwin, Eric P. Xing, Fahad Shahbaz Khan:
MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT. CoRR abs/2402.16840 (2024) - [i263]Zhenting Qi, Hanlin Zhang, Eric P. Xing, Sham M. Kakade, Himabindu Lakkaraju:
Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems. CoRR abs/2402.17840 (2024) - [i262]Guangyi Liu, Yu Wang, Zeyu Feng, Qiyu Wu, Liping Tang, Yuan Gao, Zhen Li, Shuguang Cui, Julian J. McAuley, Eric P. Xing, Zichao Yang, Zhiting Hu:
Generating, Reconstructing, and Representing Discrete and Continuous Data: Generalized Diffusion with Learnable Encoding-Decoding. CoRR abs/2402.19009 (2024) - [i261]Jiahui Zhang, Fangneng Zhan, Muyu Xu, Shijian Lu, Eric P. Xing:
FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization. CoRR abs/2403.06908 (2024) - [i260]Adilbek Karmanov, Dayan Guan, Shijian Lu, Abdulmotaleb El-Saddik, Eric P. Xing:
Efficient Test-Time Adaptation of Vision-Language Models. CoRR abs/2403.18293 (2024) - [i259]Longfei Yun, Yonghao Zhuang, Yao Fu, Eric P. Xing, Hao Zhang:
Toward Inference-optimal Mixture-of-Expert Large Language Models. CoRR abs/2404.02852 (2024) - [i258]Sang Keun Choe, Hwijeen Ahn, Juhan Bae, Kewen Zhao, Minsoo Kang, Youngseog Chung, Adithya Pratapa, Willie Neiswanger, Emma Strubell, Teruko Mitamura, Jeff G. Schneider, Eduard H. Hovy, Roger B. Grosse, Eric P. Xing:
What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions. CoRR abs/2405.13954 (2024) - [i257]Lingjing Kong, Guangyi Chen, Biwei Huang, Eric P. Xing, Yuejie Chi, Kun Zhang:
Learning Discrete Concepts in Latent Hierarchical Models. CoRR abs/2406.00519 (2024) - [i256]Jiannan Xiang, Guangyi Liu, Yi Gu, Qiyue Gao, Yuting Ning, Yuheng Zha, Zeyu Feng, Tianhua Tao, Shibo Hao, Yemin Shi, Zhengzhong Liu, Eric P. Xing, Zhiting Hu:
Pandora: Towards General World Model with Natural Language Actions and Video States. CoRR abs/2406.09455 (2024) - [i255]Sukmin Yun, Haokun Lin, Rusiru Thushara, Mohammad Qazim Bhat, Yongxin Wang, Zutao Jiang, Mingkai Deng, Jinhong Wang, Tianhua Tao, Junbo Li, Haonan Li, Preslav Nakov, Timothy Baldwin, Zhengzhong Liu, Eric P. Xing, Xiaodan Liang, Zhiqiang Shen:
Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs. CoRR abs/2406.20098 (2024) - [i254]Han Guo, William Brandon, Radostin Cholakov, Jonathan Ragan-Kelley, Eric P. Xing, Yoon Kim:
Fast Matrix Multiplications for Lookup Table-Quantized LLMs. CoRR abs/2407.10960 (2024) - [i253]Aviv Bick, Kevin Y. Li, Eric P. Xing, J. Zico Kolter, Albert Gu:
Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models. CoRR abs/2408.10189 (2024) - [i252]Guokan Shang, Hadi Abdine, Yousef Khoubrane, Amr Mohamed, Yassine Abbahaddou, Sofiane Ennadir, Imane Momayiz, Xuguang Ren, Eric Moulines, Preslav Nakov, Michalis Vazirgiannis, Eric P. Xing:
Atlas-Chat: Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect. CoRR abs/2409.17912 (2024) - [i251]Abdelmajid Essofi, Ridwan Salahuddeen, Munachiso Nwadike, Elnura Zhalieva, Kun Zhang, Eric P. Xing, Willie Neiswanger, Qirong Ho:
Reducing Hyperparameter Tuning Costs in ML, Vision and Language Model Training Pipelines via Memoization-Awareness. CoRR abs/2411.03731 (2024) - [i250]Tianhua Tao, Junbo Li, Bowen Tan, Hongyi Wang, William Marshall, Bhargav Kanakiya, Joel Hestness, Natalia Vassilieva, Zhiqiang Shen, Eric P. Xing, Zhengzhong Liu:
Crystal: Illuminating LLM Abilities on Language and Code. CoRR abs/2411.04156 (2024) - [i249]Shehan Munasinghe, Hanan Gani, Wenqi Zhu, Jiale Cao, Eric P. Xing, Fahad Shahbaz Khan, Salman Khan:
VideoGLaMM: A Large Multimodal Model for Pixel-Level Visual Grounding in Videos. CoRR abs/2411.04923 (2024) - [i248]Yuewen Sun, Lingjing Kong, Guangyi Chen, Loka Li, Gongxu Luo, Zijian Li, Yixuan Zhang, Yujia Zheng, Mengyue Yang, Petar Stojanov, Eran Segal, Eric P. Xing, Kun Zhang:
Causal Representation Learning from Multimodal Biological Observations. CoRR abs/2411.06518 (2024) - [i247]Somanshu Singla, Zhen Wang, Tianyang Liu, Abdullah Ashfaq, Zhiting Hu, Eric P. Xing:
Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models. CoRR abs/2411.08733 (2024) - [i246]Ethan Wu, Caleb Ellington, Benjamin J. Lengerich, Eric P. Xing:
Patient-Specific Models of Treatment Effects Explain Heterogeneity in Tuberculosis. CoRR abs/2411.10645 (2024) - [i245]Andrew T. McNutt, Abhinav Adduri, Caleb N. Ellington, Monica T. Dayao, Eric P. Xing, Hosein Mohimani, David Ryan Koes:
SPRINT Enables Interpretable and Ultra-Fast Virtual Screening against Thousands of Proteomes. CoRR abs/2411.15418 (2024) - [i244]Le Song, Eran Segal, Eric P. Xing:
Toward AI-Driven Digital Organism: Multiscale Foundation Models for Predicting, Simulating and Programming Biology at All Levels. CoRR abs/2412.06993 (2024) - 2023
- [j73]Gongjie Zhang, Zhipeng Luo, Kaiwen Cui, Shijian Lu, Eric P. Xing:
Meta-DETR: Image-Level Few-Shot Detection With Inter-Class Correlation Exploitation. IEEE Trans. Pattern Anal. Mach. Intell. 45(11): 12832-12843 (2023) - [j72]Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Shijian Lu, Lingjie Liu, Adam Kortylewski, Christian Theobalt, Eric P. Xing:
Multimodal Image Synthesis and Editing: The Generative AI Era. IEEE Trans. Pattern Anal. Mach. Intell. 45(12): 15098-15119 (2023) - [j71]Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu, Eric P. Xing:
Federated Partially Supervised Learning With Limited Decentralized Medical Images. IEEE Trans. Medical Imaging 42(7): 1944-1954 (2023) - [j70]Yifan Zhang, Hanlin Zhang, Zachary Chase Lipton, Li Erran Li, Eric P. Xing:
Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation. Trans. Mach. Learn. Res. 2023 (2023) - [c361]Hanlin Zhang, Jiani Huang, Ziyang Li, Mayur Naik, Eric P. Xing:
Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming. ACL (Findings) 2023: 3062-3077 - [c360]Shibo Hao, Bowen Tan, Kaiwen Tang, Bin Ni, Xiyan Shao, Hengzhe Zhang, Eric P. Xing, Zhiting Hu:
BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models. ACL (Findings) 2023: 5000-5015 - [c359]Kaiwen Cui, Yingchen Yu, Fangneng Zhan, Shengcai Liao, Shijian Lu, Eric P. Xing:
KD-DLGAN: Data Limited Image Generation via Knowledge Distillation. CVPR 2023: 3872-3882 - [c358]Lingjing Kong, Martin Q. Ma, Guangyi Chen, Eric P. Xing, Yuejie Chi, Louis-Philippe Morency, Kun Zhang:
Understanding Masked Autoencoders via Hierarchical Latent Variable Models. CVPR 2023: 7918-7928 - [c357]Kunhao Liu, Fangneng Zhan, Yiwen Chen, Jiahui Zhang, Yingchen Yu, Abdulmotaleb El-Saddik, Shijian Lu, Eric P. Xing:
StyleRF: Zero-Shot 3D Style Transfer of Neural Radiance Fields. CVPR 2023: 8338-8348 - [c356]Aoran Xiao, Jiaxing Huang, Weihao Xuan, Ruijie Ren, Kangcheng Liu, Dayan Guan, Abdulmotaleb El-Saddik, Shijian Lu, Eric P. Xing:
3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds. CVPR 2023: 9382-9392 - [c355]Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric P. Xing:
Betty: An Automatic Differentiation Library for Multilevel Optimization. ICLR 2023 - [c354]Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim, Eric P. Xing:
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. ICLR 2023 - [c353]Dacheng Li, Hongyi Wang, Rulin Shao, Han Guo, Eric P. Xing, Hao Zhang:
MPCFORMER: Fast, Performant and Provate Transformer Inference with MPC. ICLR 2023 - [c352]Hongyi Wang, Saurabh Agarwal, Pongsakorn U.-Chupala, Yoshiki Tanaka, Eric P. Xing, Dimitris Papailiopoulos:
Cuttlefish: Low-Rank Model Training without All the Tuning. MLSys 2023 - [c351]Yonghao Zhuang, Lianmin Zheng, Zhuohan Li, Eric P. Xing, Qirong Ho, Joseph Gonzalez, Ion Stoica, Hao Zhang, Hexu Zhao:
On Optimizing the Communication of Model Parallelism. MLSys 2023 - [c350]Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger, Pengtao Xie, Emma Strubell, Eric P. Xing:
Making Scalable Meta Learning Practical. NeurIPS 2023 - [c349]Lingjing Kong, Biwei Huang, Feng Xie, Eric P. Xing, Yuejie Chi, Kun Zhang:
Identification of Nonlinear Latent Hierarchical Models. NeurIPS 2023 - [c348]Junbo Li, Ang Li, Chong Tian, Qirong Ho, Eric P. Xing, Hongyi Wang:
FedNAR: Federated Optimization with Normalized Annealing Regularization. NeurIPS 2023 - [c347]Kunhao Liu, Fangneng Zhan, Jiahui Zhang, Muyu Xu, Yingchen Yu, Abdulmotaleb El-Saddik, Christian Theobalt, Eric P. Xing, Shijian Lu:
Weakly Supervised 3D Open-vocabulary Segmentation. NeurIPS 2023 - [c346]Xiangchen Song, Weiran Yao, Yewen Fan, Xinshuai Dong, Guangyi Chen, Juan Carlos Niebles, Eric P. Xing, Kun Zhang:
Temporally Disentangled Representation Learning under Unknown Nonstationarity. NeurIPS 2023 - [c345]Bowen Tan, Yun Zhu, Lijuan Liu, Eric P. Xing, Zhiting Hu, Jindong Chen:
Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer. NeurIPS 2023 - [c344]Hanqi Yan, Lingjing Kong, Lin Gui, Yuejie Chi, Eric P. Xing, Yulan He, Kun Zhang:
Counterfactual Generation with Identifiability Guarantees. NeurIPS 2023 - [c343]Zeyuan Yin, Eric P. Xing, Zhiqiang Shen:
Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective. NeurIPS 2023 - [c342]Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, Ion Stoica:
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. NeurIPS 2023 - [p1]Bowen Tan, Shibo Hao, Eric P. Xing, Zhiting Hu:
Neural-Symbolic Interaction and Co-Evolving. Compendium of Neurosymbolic Artificial Intelligence 2023: 125-152 - [i243]Song Bian, Dacheng Li, Hongyi Wang, Eric P. Xing, Shivaram Venkataraman:
Does compressing activations help model parallel training? CoRR abs/2301.02654 (2023) - [i242]Han Guo, Philip Greengard, Hongyi Wang, Andrew Gelman, Yoon Kim, Eric P. Xing:
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. CoRR abs/2302.04228 (2023) - [i241]Kai Zhang, Yutong Dai, Hongyi Wang, Eric P. Xing, Xun Chen, Lichao Sun:
Memory-adaptive Depth-wise Heterogenous Federated Learning. CoRR abs/2303.04887 (2023) - [i240]Kunhao Liu, Fangneng Zhan, Yiwen Chen, Jiahui Zhang, Yingchen Yu, Abdulmotaleb El-Saddik, Shijian Lu, Eric P. Xing:
StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields. CoRR abs/2303.10598 (2023) - [i239]Kaiwen Cui, Yingchen Yu, Fangneng Zhan, Shengcai Liao, Shijian Lu, Eric P. Xing:
KD-DLGAN: Data Limited Image Generation via Knowledge Distillation. CoRR abs/2303.17158 (2023) - [i238]Aoran Xiao, Jiaxing Huang, Weihao Xuan, Ruijie Ren, Kangcheng Liu, Dayan Guan, Abdulmotaleb El-Saddik, Shijian Lu, Eric P. Xing:
3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds. CoRR abs/2304.00690 (2023) - [i237]Hongyi Wang, Saurabh Agarwal, Pongsakorn U.-Chupala, Yoshiki Tanaka, Eric P. Xing, Dimitris S. Papailiopoulos:
Cuttlefish: Low-Rank Model Training without All the Tuning. CoRR abs/2305.02538 (2023) - [i236]Hanlin Zhang, Jiani Huang, Ziyang Li, Mayur Naik, Eric P. Xing:
Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming. CoRR abs/2305.03742 (2023) - [i235]Kunhao Liu, Fangneng Zhan, Jiahui Zhang, Muyu Xu, Yingchen Yu, Abdulmotaleb El-Saddik, Christian Theobalt, Eric P. Xing, Shijian Lu:
3D Open-vocabulary Segmentation with Foundation Models. CoRR abs/2305.14093 (2023) - [i234]Lingjing Kong, Martin Q. Ma, Guangyi Chen, Eric P. Xing, Yuejie Chi, Louis-Philippe Morency, Kun Zhang:
Understanding Masked Autoencoders via Hierarchical Latent Variable Models. CoRR abs/2306.04898 (2023) - [i233]Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, Ion Stoica:
Judging LLM-as-a-judge with MT-Bench and Chatbot Arena. CoRR abs/2306.05685 (2023) - [i232]Lingjing Kong, Biwei Huang, Feng Xie, Eric P. Xing, Yuejie Chi, Kun Zhang:
Identification of Nonlinear Latent Hierarchical Models. CoRR abs/2306.07916 (2023) - [i231]Arnav Chavan, Zhuang Liu, Deepak K. Gupta, Eric P. Xing, Zhiqiang Shen:
One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning. CoRR abs/2306.07967 (2023) - [i230]Zeyuan Yin, Eric P. Xing, Zhiqiang Shen:
Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective. CoRR abs/2306.13092 (2023) - [i229]Nanqing Dong, Zhipeng Wang, Jiahao Sun, Michael Kampffmeyer, Yizhe Wen, Shuoying Zhang, William J. Knottenbelt, Eric P. Xing:
Defending Against Malicious Behaviors in Federated Learning with Blockchain. CoRR abs/2307.00543 (2023) - [i228]Neha Sengupta, Sunil Kumar Sahu, Bokang Jia, Satheesh Katipomu, Haonan Li, Fajri Koto, Osama Mohammed Afzal, Samta Kamboj, Onkar Pandit, Rahul Pal, Lalit Pradhan, Zain Muhammad Mujahid, Massa Baali, Alham Fikri Aji, Zhengzhong Liu, Andy Hock, Andrew Feldman, Jonathan Lee, Andrew Jackson, Preslav Nakov, Timothy Baldwin, Eric P. Xing:
Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models. CoRR abs/2308.16149 (2023) - [i227]Zhiqiang Shen, Tianhua Tao, Liqun Ma, Willie Neiswanger, Zhengzhong Liu, Hongyi Wang, Bowen Tan, Joel Hestness, Natalia Vassilieva, Daria Soboleva, Eric P. Xing:
SlimPajama-DC: Understanding Data Combinations for LLM Training. CoRR abs/2309.10818 (2023) - [i226]Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Tianle Li, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zhuohan Li, Zi Lin, Eric P. Xing, Joseph E. Gonzalez, Ion Stoica, Hao Zhang:
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset. CoRR abs/2309.11998 (2023) - [i225]Hongyi Wang, Felipe Maia Polo, Yuekai Sun, Souvik Kundu, Eric P. Xing, Mikhail Yurochkin:
Fusing Models with Complementary Expertise. CoRR abs/2310.01542 (2023) - [i224]Junbo Li, Ang Li, Chong Tian, Qirong Ho, Eric P. Xing, Hongyi Wang:
FedNAR: Federated Optimization with Normalized Annealing Regularization. CoRR abs/2310.03163 (2023) - [i223]Dacheng Li, Rulin Shao, Anze Xie, Eric P. Xing, Joseph E. Gonzalez, Ion Stoica, Xuezhe Ma, Hao Zhang:
LightSeq: Sequence Level Parallelism for Distributed Training of Long Context Transformers. CoRR abs/2310.03294 (2023) - [i222]Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger, Pengtao Xie, Emma Strubell, Eric P. Xing:
Making Scalable Meta Learning Practical. CoRR abs/2310.05674 (2023) - [i221]Jannik Deuschel, Caleb N. Ellington, Benjamin J. Lengerich, Yingtao Luo, Pascal Friederich, Eric P. Xing:
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning. CoRR abs/2310.07918 (2023) - [i220]Benjamin J. Lengerich, Caleb N. Ellington, Andrea Rubbi, Manolis Kellis, Eric P. Xing:
Contextualized Machine Learning. CoRR abs/2310.11340 (2023) - [i219]Bowen Tan, Yun Zhu, Lijuan Liu, Hongyi Wang, Yonghao Zhuang, Jindong Chen, Eric P. Xing, Zhiting Hu:
Redco: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs. CoRR abs/2310.16355 (2023) - [i218]Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric P. Xing, Zhiting Hu:
PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization. CoRR abs/2310.16427 (2023) - [i217]Xiangchen Song, Weiran Yao, Yewen Fan, Xinshuai Dong, Guangyi Chen, Juan Carlos Niebles, Eric P. Xing, Kun Zhang:
Temporally Disentangled Representation Learning under Unknown Nonstationarity. CoRR abs/2310.18615 (2023) - [i216]Hanoona Abdul Rasheed, Muhammad Maaz, Sahal Shaji Mullappilly, Abdelrahman M. Shaker, Salman H. Khan, Hisham Cholakkal, Rao Muhammad Anwer, Eric P. Xing, Ming-Hsuan Yang, Fahad Shahbaz Khan:
GLaMM: Pixel Grounding Large Multimodal Model. CoRR abs/2311.03356 (2023) - [i215]Bowen Tan, Yun Zhu, Lijuan Liu, Eric P. Xing, Zhiting Hu, Jindong Chen:
Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer. CoRR abs/2311.06720 (2023) - [i214]Yuxin Pei, Pushkar Bhuse, Zhengzhong Liu, Eric P. Xing:
SegMix: A Simple Structure-Aware Data Augmentation Method. CoRR abs/2311.09505 (2023) - [i213]Han Guo, Philip Greengard, Eric P. Xing, Yoon Kim:
LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model Finetuning. CoRR abs/2311.12023 (2023) - [i212]Hanlin Zhang, Yifan Zhang, Yaodong Yu, Dhruv Madeka, Dean P. Foster, Eric P. Xing, Himabindu Lakkaraju, Sham M. Kakade:
A Study on the Calibration of In-context Learning. CoRR abs/2312.04021 (2023) - [i211]Zhengzhong Liu, Aurick Qiao, Willie Neiswanger, Hongyi Wang, Bowen Tan, Tianhua Tao, Junbo Li, Yuqi Wang, Suqi Sun, Omkar Pangarkar, Richard Fan, Yi Gu, Victor Miller, Yonghao Zhuang, Guowei He, Haonan Li, Fajri Koto, Liping Tang, Nikhil Ranjan, Zhiqiang Shen, Xuguang Ren, Roberto Iriondo, Cun Mu, Zhiting Hu, Mark Schulze, Preslav Nakov, Tim Baldwin, Eric P. Xing:
LLM360: Towards Fully Transparent Open-Source LLMs. CoRR abs/2312.06550 (2023) - 2022
- [j69]Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Min Xu, Irina Voiculescu, Eric P. Xing:
Towards robust partially supervised multi-structure medical image segmentation on small-scale data. Appl. Soft Comput. 114: 108074 (2022) - [j68]Haohan Wang, Bryon Aragam, Eric P. Xing:
Trade-offs of Linear Mixed Models in Genome-Wide Association Studies. J. Comput. Biol. 29(3): 233-242 (2022) - [j67]Haohan Wang, Oscar Lopez, Eric P. Xing, Wei Wu:
Kernel Mixed Model for Transcriptome Association Study. J. Comput. Biol. 29(12): 1353-1356 (2022) - [j66]Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu, Eric P. Xing:
Negational symmetry of quantum neural networks for binary pattern classification. Pattern Recognit. 129: 108750 (2022) - [j65]Zeya Wang, Yang Ni, Baoyu Jing, Deqing Wang, Hao Zhang, Eric P. Xing:
DNB: A Joint Learning Framework for Deep Bayesian Nonparametric Clustering. IEEE Trans. Neural Networks Learn. Syst. 33(12): 7610-7620 (2022) - [c341]Zhiqiang Shen, Zechun Liu, Zhuang Liu, Marios Savvides, Trevor Darrell, Eric Poe Xing:
Un-mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning. AAAI 2022: 2216-2224 - [c340]Bhanu Garg, Li Zhang, Pradyumna Sridhara, Ramtin Hosseini, Eric P. Xing, Pengtao Xie:
Learning from Mistakes - a Framework for Neural Architecture Search. AAAI 2022: 10184-10192 - [c339]Benjamin J. Lengerich, Eric P. Xing, Rich Caruana:
Dropout as a Regularizer of Interaction Effects. AISTATS 2022: 7550-7564 - [c338]Arnav Chavan, Zhiqiang Shen, Zhuang Liu, Zechun Liu, Kwang-Ting Cheng, Eric P. Xing:
Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space. CVPR 2022: 4921-4931 - [c337]Zechun Liu, Kwang-Ting Cheng, Dong Huang, Eric P. Xing, Zhiqiang Shen:
Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. CVPR 2022: 4932-4942 - [c336]Hanlin Zhang, Yifan Zhang, Weiyang Liu, Adrian Weller, Bernhard Schölkopf, Eric P. Xing:
Towards Principled Disentanglement for Domain Generalization. CVPR 2022: 8014-8024 - [c335]Zeyi Huang, Haohan Wang, Dong Huang, Yong Jae Lee, Eric P. Xing:
The Two Dimensions of Worst-case Training and Their Integrated Effect for Out-of-domain Generalization. CVPR 2022: 9621-9631 - [c334]Zechun Liu, Zhiqiang Shen, Yun Long, Eric P. Xing, Kwang-Ting Cheng, Chas Leichner:
Data-Free Neural Architecture Search via Recursive Label Calibration. ECCV (24) 2022: 391-406 - [c333]Zhiqiang Shen, Eric P. Xing:
A Fast Knowledge Distillation Framework for Visual Recognition. ECCV (24) 2022: 673-690 - [c332]Zhiqiang Shen, Zechun Liu, Eric P. Xing:
Sliced Recursive Transformer. ECCV (24) 2022: 727-744 - [c331]Jiannan Xiang, Zhengzhong Liu, Yucheng Zhou, Eric P. Xing, Zhiting Hu:
ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models. EMNLP (Findings) 2022: 1886-1899 - [c330]Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P. Xing, Zhiting Hu:
RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning. EMNLP 2022: 3369-3391 - [c329]Han Guo, Bowen Tan, Zhengzhong Liu, Eric P. Xing, Zhiting Hu:
Efficient (Soft) Q-Learning for Text Generation with Limited Good Data. EMNLP (Findings) 2022: 6969-6991 - [c328]Xijie Huang, Zhiqiang Shen, Shichao Li, Zechun Liu, Xianghong Hu, Jeffry Wicaksana, Eric P. Xing, Kwang-Ting Cheng:
SDQ: Stochastic Differentiable Quantization with Mixed Precision. ICML 2022: 9295-9309 - [c327]Haohan Wang, Zeyi Huang, Xindi Wu, Eric P. Xing:
Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation. KDD 2022: 1846-1856 - [c326]Jiaxing Huang, Kaiwen Cui, Dayan Guan, Aoran Xiao, Fangneng Zhan, Shijian Lu, Shengcai Liao, Eric P. Xing:
Masked Generative Adversarial Networks are Data-Efficient Generation Learners. NeurIPS 2022 - [c325]Dacheng Li, Hongyi Wang, Eric P. Xing, Hao Zhang:
AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness. NeurIPS 2022 - [c324]Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric P. Xing, Kangwook Lee, Dimitris S. Papailiopoulos:
Rare Gems: Finding Lottery Tickets at Initialization. NeurIPS 2022 - [c323]Lianmin Zheng, Zhuohan Li, Hao Zhang, Yonghao Zhuang, Zhifeng Chen, Yanping Huang, Yida Wang, Yuanzhong Xu, Danyang Zhuo, Eric P. Xing, Joseph E. Gonzalez, Ion Stoica:
Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning. OSDI 2022: 559-578 - [c322]Haohan Wang, Oscar L. Lopez, Wei Wu, Eric P. Xing:
Gene Set Priorization Guided by Regulatory Networks with p-values through Kernel Mixed Model. RECOMB 2022: 107-125 - [c321]Haohan Wang, Zeyi Huang, Hanlin Zhang, Yong Jae Lee, Eric P. Xing:
Toward learning human-aligned cross-domain robust models by countering misaligned features. UAI 2022: 2075-2084 - [i210]Arnav Chavan, Zhiqiang Shen, Zhuang Liu, Zechun Liu, Kwang-Ting Cheng, Eric P. Xing:
Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space. CoRR abs/2201.00814 (2022) - [i209]Liu Ziyin, Hanlin Zhang, Xiangming Meng, Yuting Lu, Eric P. Xing, Masahito Ueda:
Stochastic Neural Networks with Infinite Width are Deterministic. CoRR abs/2201.12724 (2022) - [i208]Yifan Zhang, Hanlin Zhang, Zachary C. Lipton, Li Erran Li, Eric P. Xing:
Can Transformers be Strong Treatment Effect Estimators? CoRR abs/2202.01336 (2022) - [i207]Zeyi Huang, Haohan Wang, Dong Huang, Yong Jae Lee, Eric P. Xing:
The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization. CoRR abs/2204.04384 (2022) - [i206]Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P. Xing, Zhiting Hu:
RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning. CoRR abs/2205.12548 (2022) - [i205]Haohan Wang, Zeyi Huang, Xindi Wu, Eric P. Xing:
Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation. CoRR abs/2206.01909 (2022) - [i204]Xijie Huang, Zhiqiang Shen, Shichao Li, Zechun Liu, Xianghong Hu, Jeffry Wicaksana, Eric P. Xing, Kwang-Ting Cheng:
SDQ: Stochastic Differentiable Quantization with Mixed Precision. CoRR abs/2206.04459 (2022) - [i203]Shibo Hao, Bowen Tan, Kaiwen Tang, Hengzhe Zhang, Eric P. Xing, Zhiting Hu:
BertNet: Harvesting Knowledge Graphs from Pretrained Language Models. CoRR abs/2206.14268 (2022) - [i202]Sang Keun Choe, Willie Neiswanger, Pengtao Xie, Eric P. Xing:
Betty: An Automatic Differentiation Library for Multilevel Optimization. CoRR abs/2207.02849 (2022) - [i201]Yifan Zhong, Haohan Wang, Eric P. Xing:
MRCLens: an MRC Dataset Bias Detection Toolkit. CoRR abs/2207.08943 (2022) - [i200]Chonghan Chen, Haohan Wang, Leyang Hu, Yuhao Zhang, Shuguang Lyu, Jingcheng Wu, Xinnuo Li, Linjing Sun, Eric P. Xing:
Robustar: Interactive Toolbox Supporting Precise Data Annotation for Robust Vision Learning. CoRR abs/2207.08944 (2022) - [i199]Gongjie Zhang, Zhipeng Luo, Yingchen Yu, Jiaxing Huang, Kaiwen Cui, Shijian Lu, Eric P. Xing:
Semantic-Aligned Matching for Enhanced DETR Convergence and Multi-Scale Feature Fusion. CoRR abs/2207.14172 (2022) - [i198]Gongjie Zhang, Zhipeng Luo, Kaiwen Cui, Shijian Lu, Eric P. Xing:
Meta-DETR: Image-Level Few-Shot Detection with Inter-Class Correlation Exploitation. CoRR abs/2208.00219 (2022) - [i197]Jiannan Xiang, Zhengzhong Liu, Yucheng Zhou, Eric P. Xing, Zhiting Hu:
ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models. CoRR abs/2210.04325 (2022) - [i196]Dacheng Li, Hongyi Wang, Eric P. Xing, Hao Zhang:
AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness. CoRR abs/2210.07297 (2022) - [i195]Kirill Vishniakov, Eric P. Xing, Zhiqiang Shen:
MixMask: Revisiting Masked Siamese Self-supervised Learning in Asymmetric Distance. CoRR abs/2210.11456 (2022) - [i194]Dacheng Li, Rulin Shao, Hongyi Wang, Han Guo, Eric P. Xing, Hao Zhang:
MPCFormer: fast, performant and private Transformer inference with MPC. CoRR abs/2211.01452 (2022) - [i193]Yonghao Zhuang, Hexu Zhao, Lianmin Zheng, Zhuohan Li, Eric P. Xing, Qirong Ho, Joseph E. Gonzalez, Ion Stoica, Hao Zhang:
On Optimizing the Communication of Model Parallelism. CoRR abs/2211.05322 (2022) - [i192]Minh-Long Luu, Zeyi Huang, Eric P. Xing, Yong Jae Lee, Haohan Wang:
Expeditious Saliency-guided Mix-up through Random Gradient Thresholding. CoRR abs/2212.04875 (2022) - [i191]Hanlin Zhang, Yifan Zhang, Li Erran Li, Eric P. Xing:
The Impact of Symbolic Representations on In-context Learning for Few-shot Reasoning. CoRR abs/2212.08686 (2022) - 2021
- [j64]Xuefeng Du, Haohan Wang, Zhenxi Zhu, Xiangrui Zeng, Yi-Wei Chang, Jing Zhang, Eric P. Xing, Min Xu:
Active learning to classify macromolecular structures in situ for less supervision in cryo-electron tomography. Bioinform. 37(16): 2340-2346 (2021) - [j63]Haohan Wang, Fen Pei, Michael M. Vanyukov, Ivet Bahar, Wei Wu, Eric P. Xing:
Coupled mixed model for joint genetic analysis of complex disorders with two independently collected data sets. BMC Bioinform. 22(1): 50 (2021) - [j62]Songwei Ge, Haohan Wang, Amir Alavi, Eric P. Xing, Ziv Bar-Joseph:
Supervised Adversarial Alignment of Single-Cell RNA-seq Data. J. Comput. Biol. 28(5): 501-513 (2021) - [c320]Seo-Jin Bang, Pengtao Xie, Heewook Lee, Wei Wu, Eric P. Xing:
Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach. AAAI 2021: 11396-11404 - [c319]Jiaqi Chen, Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu, Eric P. Xing, Liang Lin:
GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning. ACL/IJCNLP (Findings) 2021: 513-523 - [c318]Xuehai He, Zhuo Cai, Wenlan Wei, Yichen Zhang, Luntian Mou, Eric P. Xing, Pengtao Xie:
Towards Visual Question Answering on Pathology Images. ACL/IJCNLP (2) 2021: 708-718 - [c317]Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric P. Xing, Pengtao Xie:
On the Generation of Medical Dialogs for COVID-19. ACL/IJCNLP (2) 2021: 886-896 - [c316]Maruan Al-Shedivat, Liam Li, Eric P. Xing, Ameet Talwalkar:
On Data Efficiency of Meta-learning. AISTATS 2021: 1369-1377 - [c315]Huaxiu Yao, Yingxin Wu, Maruan Al-Shedivat, Eric P. Xing:
Knowledge-Aware Meta-learning for Low-Resource Text Classification. EMNLP (1) 2021: 1814-1821 - [c314]Mingkai Deng, Bowen Tan, Zhengzhong Liu, Eric P. Xing, Zhiting Hu:
Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation. EMNLP (1) 2021: 7580-7605 - [c313]Maruan Al-Shedivat, Jennifer Gillenwater, Eric P. Xing, Afshin Rostamizadeh:
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms. ICLR 2021 - [c312]Benedikt Boecking, Willie Neiswanger, Eric P. Xing, Artur Dubrawski:
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling. ICLR 2021 - [c311]Bowen Tan, Zichao Yang, Maruan Al-Shedivat, Eric P. Xing, Zhiting Hu:
Progressive Generation of Long Text with Pretrained Language Models. NAACL-HLT 2021: 4313-4324 - [c310]Xinshi Chen, Haoran Sun, Caleb Ellington, Eric P. Xing, Le Song:
Multi-task Learning of Order-Consistent Causal Graphs. NeurIPS 2021: 11083-11095 - [c309]Aurick Qiao, Sang Keun Choe, Suhas Jayaram Subramanya, Willie Neiswanger, Qirong Ho, Hao Zhang, Gregory R. Ganger, Eric P. Xing:
Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning. OSDI 2021 - [i190]Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic, Siddha Ganju, Dava Newman, Sujoy Ganguly, Danny Lange, Atilim Günes Baydin, Amit Sharma, Adam Gibson, Yarin Gal, Eric P. Xing, Chris Mattmann, James Parr:
Technology Readiness Levels for Machine Learning Systems. CoRR abs/2101.03989 (2021) - [i189]Maruan Al-Shedivat, Liam Li, Eric Poe Xing, Ameet Talwalkar:
On Data Efficiency of Meta-learning. CoRR abs/2102.00127 (2021) - [i188]Xuefeng Du, Haohan Wang, Zhenxi Zhu, Xiangrui Zeng, Yi-Wei Chang, Jing Zhang, Eric Poe Xing, Min Xu:
Active Learning to Classify Macromolecular Structures in situ for Less Supervision in Cryo-Electron Tomography. CoRR abs/2102.12040 (2021) - [i187]Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Xiaodan Liang, Teruko Mitamura, Eric P. Xing, Zhiting Hu:
A Data-Centric Framework for Composable NLP Workflows. CoRR abs/2103.01834 (2021) - [i186]Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu, Eric P. Xing:
Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification. CoRR abs/2105.09580 (2021) - [i185]Jiaqi Chen, Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu, Eric P. Xing, Liang Lin:
GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning. CoRR abs/2105.14517 (2021) - [i184]Han Guo, Bowen Tan, Zhengzhong Liu, Eric P. Xing, Zhiting Hu:
Text Generation with Efficient (Soft) Q-Learning. CoRR abs/2106.07704 (2021) - [i183]Yuxin Xiao, Eric P. Xing, Willie Neiswanger:
Amortized Auto-Tuning: Cost-Efficient Transfer Optimization for Hyperparameter Recommendation. CoRR abs/2106.09179 (2021) - [i182]Shuai Lin, Pan Zhou, Zi-Yuan Hu, Shuojia Wang, Ruihui Zhao, Yefeng Zheng, Liang Lin, Eric P. Xing, Xiaodan Liang:
Prototypical Graph Contrastive Learning. CoRR abs/2106.09645 (2021) - [i181]Zhiting Hu, Eric P. Xing:
Panoramic Learning with A Standardized Machine Learning Formalism. CoRR abs/2108.07783 (2021) - [i180]Huaxiu Yao, Yingxin Wu, Maruan Al-Shedivat, Eric P. Xing:
Knowledge-Aware Meta-learning for Low-Resource Text Classification. CoRR abs/2109.04707 (2021) - [i179]Mingkai Deng, Bowen Tan, Zhengzhong Liu, Eric P. Xing, Zhiting Hu:
Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation. CoRR abs/2109.06379 (2021) - [i178]Zhaoming Qin, Nanqing Dong, Eric P. Xing, Junwei Cao:
Cooperative Multi-Agent Actor-Critic for Privacy-Preserving Load Scheduling in a Residential Microgrid. CoRR abs/2110.02784 (2021) - [i177]Shentong Mo, Xi Fu, Chenyang Hong, Yizhen Chen, Yuxuan Zheng, Xiangru Tang, Zhiqiang Shen, Eric P. Xing, Yanyan Lan:
Multi-modal Self-supervised Pre-training for Regulatory Genome Across Cell Types. CoRR abs/2110.05231 (2021) - [i176]Benjamin J. Lengerich, Caleb Ellington, Bryon Aragam, Eric P. Xing, Manolis Kellis:
NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters. CoRR abs/2111.01104 (2021) - [i175]Xinshi Chen, Haoran Sun, Caleb Ellington, Eric P. Xing, Le Song:
Multi-task Learning of Order-Consistent Causal Graphs. CoRR abs/2111.02545 (2021) - [i174]Haohan Wang, Bryon Aragam, Eric P. Xing:
Tradeoffs of Linear Mixed Models in Genome-wide Association Studies. CoRR abs/2111.03739 (2021) - [i173]Haohan Wang, Zeyi Huang, Hanlin Zhang, Eric Poe Xing:
Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features. CoRR abs/2111.03740 (2021) - [i172]Zhiqiang Shen, Zechun Liu, Eric P. Xing:
Sliced Recursive Transformer. CoRR abs/2111.05297 (2021) - [i171]Bhanu Garg, Li Zhang, Pradyumna Sridhara, Ramtin Hosseini, Eric P. Xing, Pengtao Xie:
Learning from Mistakes - A Framework for Neural Architecture Search. CoRR abs/2111.06353 (2021) - [i170]Hanlin Zhang, Yifan Zhang, Weiyang Liu, Adrian Weller, Bernhard Schölkopf, Eric P. Xing:
Towards Principled Disentanglement for Domain Generalization. CoRR abs/2111.13839 (2021) - [i169]Zechun Liu, Kwang-Ting Cheng, Dong Huang, Eric P. Xing, Zhiqiang Shen:
Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. CoRR abs/2111.14826 (2021) - [i168]Zhiqiang Shen, Eric P. Xing:
A Fast Knowledge Distillation Framework for Visual Recognition. CoRR abs/2112.01528 (2021) - [i167]Zechun Liu, Zhiqiang Shen, Yun Long, Eric P. Xing, Kwang-Ting Cheng, Chas Leichner:
Data-Free Neural Architecture Search via Recursive Label Calibration. CoRR abs/2112.02086 (2021) - 2020
- [j61]Shreya Kadambi, Zeya Wang, Eric P. Xing:
WGAN domain adaptation for the joint optic disc-and-cup segmentation in fundus images. Int. J. Comput. Assist. Radiol. Surg. 15(7): 1205-1213 (2020) - [j60]Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. J. Mach. Learn. Res. 21: 81:1-81:27 (2020) - [j59]Maruan Al-Shedivat, Avinava Dubey, Eric P. Xing:
Contextual Explanation Networks. J. Mach. Learn. Res. 21: 194:1-194:44 (2020) - [j58]Kevin Tran, Willie Neiswanger, Junwoong Yoon, Qingyang Zhang, Eric P. Xing, Zachary W. Ulissi:
Methods for comparing uncertainty quantifications for material property predictions. Mach. Learn. Sci. Technol. 1(2): 25006 (2020) - [j57]Yumin Zheng, Haohan Wang, Yang Zhang, Xin Gao, Eric P. Xing, Min Xu:
Poly(A)-DG: A deep-learning-based domain generalization method to identify cross-species Poly(A) signal without prior knowledge from target species. PLoS Comput. Biol. 16(11): 1008297 (2020) - [j56]Yujia Zhang, Xiaodan Liang, Dingwen Zhang, Min Tan, Eric P. Xing:
Unsupervised object-level video summarization with online motion auto-encoder. Pattern Recognit. Lett. 130: 376-385 (2020) - [c308]Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing:
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. AISTATS 2020: 3393-3403 - [c307]Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
Learning Sparse Nonparametric DAGs. AISTATS 2020: 3414-3425 - [c306]Kumar Avinava Dubey, Michael Minyi Zhang, Eric P. Xing, Sinead Williamson:
Distributed, partially collapsed MCMC for Bayesian Nonparametrics. AISTATS 2020: 3685-3695 - [c305]Haohan Wang, Xindi Wu, Zeyi Huang, Eric P. Xing:
High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks. CVPR 2020: 8681-8691 - [c304]Zeya Wang, Baoyu Jing, Yang Ni, Nanqing Dong, Pengtao Xie, Eric P. Xing:
Adversarial Domain Adaptation Being Aware of Class Relationships. ECAI 2020: 1579-1586 - [c303]Zeyi Huang, Haohan Wang, Eric P. Xing, Dong Huang:
Self-challenging Improves Cross-Domain Generalization. ECCV (2) 2020: 124-140 - [c302]Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Xiaodan Liang, Teruko Mitamura, Eric P. Xing, Zhiting Hu:
A Data-Centric Framework for Composable NLP Workflows. EMNLP (Demos) 2020: 197-204 - [c301]Shuai Lin, Wentao Wang, Zichao Yang, Xiaodan Liang, Frank F. Xu, Eric P. Xing, Zhiting Hu:
Record-to-Text Generation with Style Imitation. EMNLP (Findings) 2020: 1589-1598 - [c300]Bowen Tan, Lianhui Qin, Eric P. Xing, Zhiting Hu:
Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach. EMNLP (1) 2020: 6301-6309 - [c299]Congzheng Song, Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, Eric P. Xing:
Generalized Zero-Shot Text Classification for ICD Coding. IJCAI 2020: 4018-4024 - [c298]Zhiting Hu, Eric P. Xing:
Learning from All Types of Experiences: A Unifying Machine Learning Perspective. KDD 2020: 3531-3532 - [c297]Hao Zhang, Yuan Li, Zhijie Deng, Xiaodan Liang, Lawrence Carin, Eric P. Xing:
AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning. NeurIPS 2020 - [c296]Gregory Plumb, Maruan Al-Shedivat, Ángel Alexander Cabrera, Adam Perer, Eric P. Xing, Ameet Talwalkar:
Regularizing Black-box Models for Improved Interpretability. NeurIPS 2020 - [c295]Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu:
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting. NeurIPS 2020 - [c294]Songwei Ge, Haohan Wang, Amir Alavi, Eric P. Xing, Ziv Bar-Joseph:
Supervised Adversarial Alignment of Single-Cell RNA-seq Data. RECOMB 2020: 72-87 - [i166]Avinava Dubey, Michael Minyi Zhang, Eric P. Xing, Sinead A. Williamson:
Distributed, partially collapsed MCMC for Bayesian Nonparametrics. CoRR abs/2001.05591 (2020) - [i165]Xuehai He, Yichen Zhang, Luntian Mou, Eric P. Xing, Pengtao Xie:
PathVQA: 30000+ Questions for Medical Visual Question Answering. CoRR abs/2003.10286 (2020) - [i164]Emmanouil Antonios Platanios, Maruan Al-Shedivat, Eric P. Xing, Tom M. Mitchell:
Learning from Imperfect Annotations. CoRR abs/2004.03473 (2020) - [i163]Baoyu Jing, Zeya Wang, Eric P. Xing:
Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports. CoRR abs/2004.12274 (2020) - [i162]Wenmian Yang, Guangtao Zeng, Bowen Tan, Zeqian Ju, Subrato Chakravorty, Xuehai He, Shu Chen, Xingyi Yang, Qingyang Wu, Zhou Yu, Eric P. Xing, Pengtao Xie:
On the Generation of Medical Dialogues for COVID-19. CoRR abs/2005.05442 (2020) - [i161]Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu:
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting. CoRR abs/2006.06900 (2020) - [i160]Xingyi Yang, Nandiraju Gireesh, Eric P. Xing, Pengtao Xie:
XRayGAN: Consistency-preserving Generation of X-ray Images from Radiology Reports. CoRR abs/2006.10552 (2020) - [i159]Bowen Tan, Zichao Yang, Maruan Al-Shedivat, Eric P. Xing, Zhiting Hu:
Progressive Generation of Long Text. CoRR abs/2006.15720 (2020) - [i158]Benjamin J. Lengerich, Eric P. Xing, Rich Caruana:
On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks. CoRR abs/2007.00823 (2020) - [i157]Zeyi Huang, Haohan Wang, Eric P. Xing, Dong Huang:
Self-Challenging Improves Cross-Domain Generalization. CoRR abs/2007.02454 (2020) - [i156]Aurick Qiao, Willie Neiswanger, Qirong Ho, Hao Zhang, Gregory R. Ganger, Eric P. Xing:
Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning. CoRR abs/2008.12260 (2020) - [i155]Maruan Al-Shedivat, Jennifer Gillenwater, Eric P. Xing, Afshin Rostamizadeh:
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms. CoRR abs/2010.05273 (2020) - [i154]Bowen Tan, Lianhui Qin, Eric P. Xing, Zhiting Hu:
Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach. CoRR abs/2010.06792 (2020) - [i153]Haohan Wang, Peiyan Zhang, Eric P. Xing:
Word Shape Matters: Robust Machine Translation with Visual Embedding. CoRR abs/2010.09997 (2020) - [i152]Xuehai He, Zhuo Cai, Wenlan Wei, Yichen Zhang, Luntian Mou, Eric P. Xing, Pengtao Xie:
Pathological Visual Question Answering. CoRR abs/2010.12435 (2020) - [i151]Hanlin Zhang, Shuai Lin, Weiyang Liu, Pan Zhou, Jian Tang, Xiaodan Liang, Eric P. Xing:
Iterative Graph Self-Distillation. CoRR abs/2010.12609 (2020) - [i150]Haohan Wang, Zeyi Huang, Xindi Wu, Eric P. Xing:
Squared 𝓁2 Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations. CoRR abs/2011.13052 (2020) - [i149]Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Min Xu, Irina Voiculescu, Eric P. Xing:
Towards Robust Medical Image Segmentation on Small-Scale Data with Incomplete Labels. CoRR abs/2011.14164 (2020) - [i148]Benedikt Boecking, Willie Neiswanger, Eric Poe Xing, Artur Dubrawski:
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling. CoRR abs/2012.06046 (2020) - [i147]Hongbo Zou, Guangjing Chen, Pengtao Xie, Sean Chen, Yongtian He, Hochih Huang, Zheng Nie, Hongbao Zhang, Tristan Bala, Kazi Tulip, Yuqi Wang, Shenlin Qin, Eric P. Xing:
Validate and Enable Machine Learning in Industrial AI. CoRR abs/2012.09610 (2020)
2010 – 2019
- 2019
- [j55]Haohan Wang, Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing:
Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data. Bioinform. 35(7): 1181-1187 (2019) - [j54]Haohan Wang, Tianwei Yue, Jingkang Yang, Wei Wu, Eric P. Xing:
Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies. BMC Bioinform. 20-S(23): 656 (2019) - [j53]Mrinmaya Sachan, Avinava Dubey, Eduard H. Hovy, Tom M. Mitchell, Dan Roth, Eric P. Xing:
Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks. Comput. Linguistics 45(4): 627-665 (2019) - [j52]Yujia Zhang, Michael Kampffmeyer, Xiaodan Liang, Dingwen Zhang, Min Tan, Eric P. Xing:
Dilated temporal relational adversarial network for generic video summarization. Multim. Tools Appl. 78(24): 35237-35261 (2019) - [j51]Michael Kampffmeyer, Nanqing Dong, Xiaodan Liang, Yujia Zhang, Eric P. Xing:
ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentation. IEEE Trans. Image Process. 28(5): 2518-2529 (2019) - [c293]Christy Y. Li, Xiaodan Liang, Zhiting Hu, Eric P. Xing:
Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation. AAAI 2019: 6666-6673 - [c292]Haohan Wang, Da Sun, Eric P. Xing:
What if We Simply Swap the Two Text Fragments? A Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks. AAAI 2019: 7136-7143 - [c291]Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wanrong Zhu, Devendra Singh Sachan, Eric P. Xing:
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation. ACL (3) 2019: 159-164 - [c290]Jianheng Tang, Tiancheng Zhao, Chenyan Xiong, Xiaodan Liang, Eric P. Xing, Zhiting Hu:
Target-Guided Open-Domain Conversation. ACL (1) 2019: 5624-5634 - [c289]Baoyu Jing, Zeya Wang, Eric P. Xing:
Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-ray Reports. ACL (1) 2019: 6570-6580 - [c288]Xindi Wu, Yijun Mao, Haohan Wang, Xiangrui Zeng, Xin Gao, Eric P. Xing, Min Xu:
Regularized Adversarial Training (RAT) for Robust Cellular Electron Cryo Tomograms Classification. BIBM 2019: 1-6 - [c287]Haohan Wang, Changpeng Lu, Wei Wu, Eric P. Xing:
Graph-structured Sparse Mixed Models for Genetic Association with Confounding Factors Correction. BIBM 2019: 298-302 - [c286]Haohan Wang, Yibing Wei, Mengxin Cao, Ming Xu, Wei Wu, Eric P. Xing:
Deep Inductive Matrix Completion for Biomedical Interaction Prediction. BIBM 2019: 520-527 - [c285]Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, Eric P. Xing:
Rethinking Knowledge Graph Propagation for Zero-Shot Learning. CVPR 2019: 11487-11496 - [c284]Jinliang Wei, Garth A. Gibson, Phillip B. Gibbons, Eric P. Xing:
Automating Dependence-Aware Parallelization of Machine Learning Training on Distributed Shared Memory. EuroSys 2019: 42:1-42:17 - [c283]Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, Eric P. Xing:
Toward Understanding the Impact of Staleness in Distributed Machine Learning. ICLR (Poster) 2019 - [c282]Bowen Tan, Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing:
Connecting the Dots Between MLE and RL for Sequence Generation. DeepRLStructPred@ICLR 2019 - [c281]Haohan Wang, Zexue He, Zachary C. Lipton, Eric P. Xing:
Learning Robust Representations by Projecting Superficial Statistics Out. ICLR 2019 - [c280]Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing:
AutoLoss: Learning Discrete Schedule for Alternate Optimization. ICLR (Poster) 2019 - [c279]Aurick Qiao, Bryon Aragam, Bingjing Zhang, Eric P. Xing:
Fault Tolerance in Iterative-Convergent Machine Learning. ICML 2019: 5220-5230 - [c278]Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan:
Theoretically Principled Trade-off between Robustness and Accuracy. ICML 2019: 7472-7482 - [c277]Zeya Wang, Nanqing Dong, Sean D. Rosario, Min Xu, Pengtao Xie, Eric P. Xing:
Ellipse Detection of Optic Disc-and-Cup Boundary in Fundus Images. ISBI 2019: 601-604 - [c276]Nanqing Dong, Min Xu, Xiaodan Liang, Yiliang Jiang, Wei Dai, Eric P. Xing:
Neural Architecture Search for Adversarial Medical Image Segmentation. MICCAI (6) 2019: 828-836 - [c275]Keyang Xu, Mike Lam, Jingzhi Pang, Xin Gao, Charlotte Band, Piyush Mathur, Frank Papay, Ashish K. Khanna, Jacek B. Cywinski, Kamal Maheshwari, Pengtao Xie, Eric P. Xing:
Multimodal Machine Learning for Automated ICD Coding. MLHC 2019: 197-215 - [c274]Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing:
Learning Sample-Specific Models with Low-Rank Personalized Regression. NeurIPS 2019: 3570-3580 - [c273]Haohan Wang, Songwei Ge, Zachary C. Lipton, Eric P. Xing:
Learning Robust Global Representations by Penalizing Local Predictive Power. NeurIPS 2019: 10506-10518 - [c272]Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric P. Xing, Clark Glymour:
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering. NeurIPS 2019: 13510-13521 - [c271]Zhiting Hu, Bowen Tan, Ruslan Salakhutdinov, Tom M. Mitchell, Eric P. Xing:
Learning Data Manipulation for Augmentation and Weighting. NeurIPS 2019: 15738-15749 - [c270]Haohan Wang, Zhenglin Wu, Eric P. Xing:
Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications. PSB 2019: 54-65 - [c269]Haohan Wang, Xiang Liu, Yifeng Tao, Wenting Ye, Qiao Jin, William W. Cohen, Eric P. Xing:
Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning. PSB 2019: 112-123 - [c268]Jin Kyu Kim, Abutalib Aghayev, Garth A. Gibson, Eric P. Xing:
STRADS-AP: Simplifying Distributed Machine Learning Programming without Introducing a New Programming Model. USENIX ATC 2019: 207-222 - [i146]Wanrong Zhu, Zhiting Hu, Eric P. Xing:
Text Infilling. CoRR abs/1901.00158 (2019) - [i145]Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan:
Theoretically Principled Trade-off between Robustness and Accuracy. CoRR abs/1901.08573 (2019) - [i144]Wentao Wang, Zhiting Hu, Zichao Yang, Haoran Shi, Frank F. Xu, Eric P. Xing:
Toward Unsupervised Text Content Manipulation. CoRR abs/1901.09501 (2019) - [i143]Willie Neiswanger, Kirthevasan Kandasamy, Barnabás Póczos, Jeff Schneider, Eric P. Xing:
ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization. CoRR abs/1901.11515 (2019) - [i142]Gregory Plumb, Maruan Al-Shedivat, Eric P. Xing, Ameet Talwalkar:
Regularizing Black-box Models for Improved Interpretability. CoRR abs/1902.06787 (2019) - [i141]Seo-Jin Bang, Pengtao Xie, Wei Wu, Eric P. Xing:
Explaining a black-box using Deep Variational Information Bottleneck Approach. CoRR abs/1902.06918 (2019) - [i140]Haohan Wang, Zexue He, Zachary C. Lipton, Eric P. Xing:
Learning Robust Representations by Projecting Superficial Statistics Out. CoRR abs/1903.06256 (2019) - [i139]Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R. Collins, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. CoRR abs/1903.06694 (2019) - [i138]Christy Y. Li, Xiaodan Liang, Zhiting Hu, Eric P. Xing:
Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation. CoRR abs/1903.10122 (2019) - [i137]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i136]Jianheng Tang, Tiancheng Zhao, Chenyan Xiong, Xiaodan Liang, Eric P. Xing, Zhiting Hu:
Target-Guided Open-Domain Conversation. CoRR abs/1905.11553 (2019) - [i135]Zeya Wang, Baoyu Jing, Yang Ni, Nanqing Dong, Pengtao Xie, Eric P. Xing:
Adversarial Domain Adaptation Being Aware of Class Relationships. CoRR abs/1905.11931 (2019) - [i134]Haohan Wang, Xindi Wu, Pengcheng Yin, Eric P. Xing:
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks. CoRR abs/1905.13545 (2019) - [i133]Haohan Wang, Songwei Ge, Eric P. Xing, Zachary C. Lipton:
Learning Robust Global Representations by Penalizing Local Predictive Power. CoRR abs/1905.13549 (2019) - [i132]Gregory Plumb, Maruan Al-Shedivat, Eric P. Xing, Ameet Talwalkar:
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version). CoRR abs/1906.01431 (2019) - [i131]Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric P. Xing, Sergey Levine, Ruslan Salakhutdinov:
Efficient Exploration via State Marginal Matching. CoRR abs/1906.05274 (2019) - [i130]Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabás Póczos, Jeff Schneider, Eric P. Xing:
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations. CoRR abs/1908.01425 (2019) - [i129]Congzheng Song, Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, Eric P. Xing:
Generalized Zero-shot ICD Coding. CoRR abs/1909.13154 (2019) - [i128]Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
Learning Sparse Nonparametric DAGs. CoRR abs/1909.13189 (2019) - [i127]Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing:
Learning Sample-Specific Models with Low-Rank Personalized Regression. CoRR abs/1910.06939 (2019) - [i126]Zhiting Hu, Bowen Tan, Ruslan Salakhutdinov, Tom M. Mitchell, Eric P. Xing:
Learning Data Manipulation for Augmentation and Weighting. CoRR abs/1910.12795 (2019) - 2018
- [j50]Benjamin J. Lengerich, Bryon Aragam, Eric P. Xing:
Personalized regression enables sample-specific pan-cancer analysis. Bioinform. 34(13): i178-i186 (2018) - [j49]Yi Zhou, Yingbin Liang, Yaoliang Yu, Wei Dai, Eric P. Xing:
Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters. J. Mach. Learn. Res. 19: 19:1-19:32 (2018) - [j48]Seunghak Lee, Nico Görnitz, Eric P. Xing, David Heckerman, Christoph Lippert:
Ensembles of Lasso Screening Rules. IEEE Trans. Pattern Anal. Mach. Intell. 40(12): 2841-2852 (2018) - [c267]Pengtao Xie, Haoran Shi, Ming Zhang, Eric P. Xing:
A Neural Architecture for Automated ICD Coding. ACL (1) 2018: 1066-1076 - [c266]Baoyu Jing, Pengtao Xie, Eric P. Xing:
On the Automatic Generation of Medical Imaging Reports. ACL (1) 2018: 2577-2586 - [c265]Peilun Li, Xiaodan Liang, Daoyuan Jia, Eric P. Xing:
Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaption. BMVC 2018: 73 - [c264]Nanqing Dong, Eric P. Xing:
Few-Shot Semantic Segmentation with Prototype Learning. BMVC 2018: 79 - [c263]Kaiwen Wang, Xiangrui Zeng, Xiaodan Liang, Zhiguang Huo, Eric P. Xing, Min Xu:
Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching. BMVC 2018: 130 - [c262]Yujia Zhang, Michael Kampffmeyer, Xiaodan Liang, Min Tan, Eric P. Xing:
Query-Conditioned Three-Player Adversarial Network for Video Summarization. BMVC 2018: 288 - [c261]Pengtao Xie, Jin Kyu Kim, Qirong Ho, Yaoliang Yu, Eric P. Xing:
Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design. SoCC 2018: 1-13 - [c260]Xiaodan Liang, Hongfei Zhou, Eric P. Xing:
Dynamic-Structured Semantic Propagation Network. CVPR 2018: 752-761 - [c259]Luona Yang, Xiaodan Liang, Tairui Wang, Eric P. Xing:
Real-to-Virtual Domain Unification for End-to-End Autonomous Driving. ECCV (4) 2018: 553-570 - [c258]Xiaodan Liang, Hao Zhang, Liang Lin, Eric P. Xing:
Generative Semantic Manipulation with Mask-Contrasting GAN. ECCV (13) 2018: 574-590 - [c257]Xiaodan Liang, Tairui Wang, Luona Yang, Eric P. Xing:
CIRL: Controllable Imitative Reinforcement Learning for Vision-Based Self-driving. ECCV (7) 2018: 604-620 - [c256]Zeya Wang, Nanqing Dong, Wei Dai, Sean D. Rosario, Eric P. Xing:
Classification of Breast Cancer Histopathological Images using Convolutional Neural Networks with Hierarchical Loss and Global Pooling. ICIAR 2018: 745-753 - [c255]Chang Liu, Xiangrui Zeng, Ruogu Lin, Xiaodan Liang, Zachary Freyberg, Eric P. Xing, Min Xu:
Deep Learning Based Supervised Semantic Segmentation of Electron Cryo-Subtomograms. ICIP 2018: 1578-1582 - [c254]Jakob N. Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktäschel, Eric P. Xing, Shimon Whiteson:
DiCE: The Infinitely Differentiable Monte-Carlo Estimator. ICLR (Workshop) 2018 - [c253]Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing:
On Unifying Deep Generative Models. ICLR (Poster) 2018 - [c252]Jakob N. Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktäschel, Eric P. Xing, Shimon Whiteson:
DiCE: The Infinitely Differentiable Monte Carlo Estimator. ICML 2018: 1524-1533 - [c251]Lisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Eric P. Xing, Ruslan Salakhutdinov:
Gated Path Planning Networks. ICML 2018: 2953-2961 - [c250]Junier B. Oliva, Avinava Dubey, Manzil Zaheer, Barnabás Póczos, Ruslan Salakhutdinov, Eric P. Xing, Jeff Schneider:
Transformation Autoregressive Networks. ICML 2018: 3895-3904 - [c249]Pengtao Xie, Wei Wu, Yichen Zhu, Eric P. Xing:
Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis. ICML 2018: 5399-5408 - [c248]Pengtao Xie, Hongbao Zhang, Yichen Zhu, Eric P. Xing:
Nonoverlap-Promoting Variable Selection. ICML 2018: 5409-5418 - [c247]Mrinmaya Sachan, Eric P. Xing:
Parsing to Programs: A Framework for Situated QA. KDD 2018: 2140-2149 - [c246]Eric P. Xing:
SysML: On System and Algorithm Co-design for Practical Machine Learning. KDD 2018: 2880 - [c245]Wei Dai, Nanqing Dong, Zeya Wang, Xiaodan Liang, Hao Zhang, Eric P. Xing:
SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-Rays. DLMIA/ML-CDS@MICCAI 2018: 263-273 - [c244]Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Zeya Wang, Wei Dai, Eric P. Xing:
Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-Slide Images. DLMIA/ML-CDS@MICCAI 2018: 317-325 - [c243]Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Zeya Wang, Wei Dai, Eric P. Xing:
Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio. MICCAI (2) 2018: 544-552 - [c242]Devendra Singh Sachan, Pengtao Xie, Mrinmaya Sachan, Eric P. Xing:
Effective Use of Bidirectional Language Modeling for Transfer Learning in Biomedical Named Entity Recognition. MLHC 2018: 383-402 - [c241]Mrinmaya Sachan, Eric P. Xing:
Self-Training for Jointly Learning to Ask and Answer Questions. NAACL-HLT 2018: 629-640 - [c240]Mrinmaya Sachan, Kumar Avinava Dubey, Tom M. Mitchell, Dan Roth, Eric P. Xing:
Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems. NeurIPS 2018: 140-151 - [c239]Yuan Li, Xiaodan Liang, Zhiting Hu, Eric P. Xing:
Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation. NeurIPS 2018: 1537-1547 - [c238]Xiaodan Liang, Zhiting Hu, Hao Zhang, Liang Lin, Eric P. Xing:
Symbolic Graph Reasoning Meets Convolutions. NeurIPS 2018: 1858-1868 - [c237]Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Neural Architecture Search with Bayesian Optimisation and Optimal Transport. NeurIPS 2018: 2020-2029 - [c236]Zichao Yang, Zhiting Hu, Chris Dyer, Eric P. Xing, Taylor Berg-Kirkpatrick:
Unsupervised Text Style Transfer using Language Models as Discriminators. NeurIPS 2018: 7298-7309 - [c235]Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models. NeurIPS 2018: 9344-9354 - [c234]Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
DAGs with NO TEARS: Continuous Optimization for Structure Learning. NeurIPS 2018: 9492-9503 - [c233]Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Lianhui Qin, Xiaodan Liang, Haoye Dong, Eric P. Xing:
Deep Generative Models with Learnable Knowledge Constraints. NeurIPS 2018: 10522-10533 - [c232]Nanqing Dong, Eric P. Xing:
Domain Adaption in One-Shot Learning. ECML/PKDD (1) 2018: 573-588 - [c231]Aurick Qiao, Abutalib Aghayev, Weiren Yu, Haoyang Chen, Qirong Ho, Garth A. Gibson, Eric P. Xing:
Litz: Elastic Framework for High-Performance Distributed Machine Learning. USENIX ATC 2018: 631-644 - [c230]Shizhen Xu, Hao Zhang, Graham Neubig, Wei Dai, Jin Kyu Kim, Zhijie Deng, Qirong Ho, Guangwen Yang, Eric P. Xing:
Cavs: An Efficient Runtime System for Dynamic Neural Networks. USENIX ATC 2018: 937-950 - [i125]Yujia Zhang, Xiaodan Liang, Dingwen Zhang, Min Tan, Eric P. Xing:
Unsupervised Object-Level Video Summarization with Online Motion Auto-Encoder. CoRR abs/1801.00543 (2018) - [i124]Peilun Li, Xiaodan Liang, Daoyuan Jia, Eric P. Xing:
Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaption. CoRR abs/1801.01726 (2018) - [i123]Luona Yang, Xiaodan Liang, Eric P. Xing:
Unsupervised Real-to-Virtual Domain Unification for End-to-End Highway Driving. CoRR abs/1801.03458 (2018) - [i122]Maruan Al-Shedivat, Avinava Dubey, Eric P. Xing:
The Intriguing Properties of Model Explanations. CoRR abs/1801.09808 (2018) - [i121]Maruan Al-Shedivat, Avinava Dubey, Eric P. Xing:
Personalized Survival Prediction with Contextual Explanation Networks. CoRR abs/1801.09810 (2018) - [i120]Chang Liu, Xiangrui Zeng, Ruogu Lin, Xiaodan Liang, Zachary Freyberg, Eric P. Xing, Min Xu:
Deep learning based supervised semantic segmentation of Electron Cryo-Subtomograms. CoRR abs/1802.04087 (2018) - [i119]Bryon Aragam, Chen Dan, Pradeep Ravikumar, Eric P. Xing:
Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering. CoRR abs/1802.04397 (2018) - [i118]Jakob N. Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktäschel, Eric P. Xing, Shimon Whiteson:
DiCE: The Infinitely Differentiable Monte-Carlo Estimator. CoRR abs/1802.05098 (2018) - [i117]Pengtao Xie, Wei Wu, Yichen Zhu, Eric P. Xing:
Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis. CoRR abs/1802.06014 (2018) - [i116]Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabás Póczos, Eric P. Xing:
Neural Architecture Search with Bayesian Optimisation and Optimal Transport. CoRR abs/1802.07191 (2018) - [i115]Xun Zheng, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
DAGs with NO TEARS: Smooth Optimization for Structure Learning. CoRR abs/1803.01422 (2018) - [i114]Xiaodan Liang, Hongfei Zhou, Eric P. Xing:
Dynamic-structured Semantic Propagation Network. CoRR abs/1803.06067 (2018) - [i113]Zhenglin Wu, Haohan Wang, Mingze Cao, Yin Chen, Eric P. Xing:
Fair Deep Learning Prediction for Healthcare Applications with Confounder Filtering. CoRR abs/1803.07276 (2018) - [i112]Michael Kampffmeyer, Nanqing Dong, Xiaodan Liang, Yujia Zhang, Eric P. Xing:
ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentation. CoRR abs/1804.07836 (2018) - [i111]Yujia Zhang, Michael Kampffmeyer, Xiaodan Liang, Dingwen Zhang, Min Tan, Eric P. Xing:
DTR-GAN: Dilated Temporal Relational Adversarial Network for Video Summarization. CoRR abs/1804.11228 (2018) - [i110]Kaiwen Wang, Xiangrui Zeng, Xiaodan Liang, Zhiguang Huo, Eric P. Xing, Min Xu:
Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching. CoRR abs/1805.04634 (2018) - [i109]Christy Y. Li, Xiaodan Liang, Zhiting Hu, Eric P. Xing:
Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation. CoRR abs/1805.08298 (2018) - [i108]Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, Eric P. Xing:
Rethinking Knowledge Graph Propagation for Zero-Shot Learning. CoRR abs/1805.11724 (2018) - [i107]Zichao Yang, Zhiting Hu, Chris Dyer, Eric P. Xing, Taylor Berg-Kirkpatrick:
Unsupervised Text Style Transfer using Language Models as Discriminators. CoRR abs/1805.11749 (2018) - [i106]Lisa Lee, Emilio Parisotto, Devendra Singh Chaplot, Eric P. Xing, Ruslan Salakhutdinov:
Gated Path Planning Networks. CoRR abs/1806.06408 (2018) - [i105]Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Xiaodan Liang, Lianhui Qin, Haoye Dong, Eric P. Xing:
Deep Generative Models with Learnable Knowledge Constraints. CoRR abs/1806.09764 (2018) - [i104]Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Zeya Wang, Wei Dai, Eric P. Xing:
Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio. CoRR abs/1807.03434 (2018) - [i103]Rajesh Chidambaram, Michael Kampffmeyer, Willie Neiswanger, Xiaodan Liang, Thomas Lachmann, Eric P. Xing:
Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful. CoRR abs/1807.03711 (2018) - [i102]Xiaodan Liang, Tairui Wang, Luona Yang, Eric P. Xing:
CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving. CoRR abs/1807.03776 (2018) - [i101]Yujia Zhang, Michael Kampffmeyer, Xiaodan Liang, Min Tan, Eric P. Xing:
Query-Conditioned Three-Player Adversarial Network for Video Summarization. CoRR abs/1807.06677 (2018) - [i100]Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Zeya Wang, Wei Dai, Eric P. Xing:
Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images. CoRR abs/1807.11113 (2018) - [i99]Hongbao Zhang, Pengtao Xie, Eric P. Xing:
Missing Value Imputation Based on Deep Generative Models. CoRR abs/1808.01684 (2018) - [i98]Micol Marchetti-Bowick, Benjamin J. Lengerich, Ankur P. Parikh, Eric P. Xing:
Hybrid Subspace Learning for High-Dimensional Data. CoRR abs/1808.01687 (2018) - [i97]Zhiting Hu, Haoran Shi, Zichao Yang, Bowen Tan, Tiancheng Zhao, Junxian He, Wentao Wang, Xingjiang Yu, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wanrong Zhu, Devendra Singh Sachan, Eric P. Xing:
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation. CoRR abs/1809.00794 (2018) - [i96]Haohan Wang, Da Sun, Eric P. Xing:
What If We Simply Swap the Two Text Fragments? A Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks. CoRR abs/1809.02719 (2018) - [i95]Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing:
Sample Complexity of Nonparametric Semi-Supervised Learning. CoRR abs/1809.03073 (2018) - [i94]Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing:
AutoLoss: Learning Discrete Schedules for Alternate Optimization. CoRR abs/1810.02442 (2018) - [i93]Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, Eric P. Xing:
Toward Understanding the Impact of Staleness in Distributed Machine Learning. CoRR abs/1810.03264 (2018) - [i92]Aurick Qiao, Bryon Aragam, Bingjing Zhang, Eric P. Xing:
Fault Tolerance in Iterative-Convergent Machine Learning. CoRR abs/1810.07354 (2018) - [i91]Keyang Xu, Mike Lam, Jingzhi Pang, Xin Gao, Charlotte Band, Piyush Mathur, Frank Papay, Ashish K. Khanna, Jacek B. Cywinski, Kamal Maheshwari, Pengtao Xie, Eric P. Xing:
Multimodal Machine Learning for Automated ICD Coding. CoRR abs/1810.13348 (2018) - [i90]Mrinmaya Sachan, Kumar Avinava Dubey, Eduard H. Hovy, Tom M. Mitchell, Dan Roth, Eric P. Xing:
Discourse in Multimedia: A Case Study in Information Extraction. CoRR abs/1811.05546 (2018) - [i89]Maruan Al-Shedivat, Lisa Lee, Ruslan Salakhutdinov, Eric P. Xing:
On the Complexity of Exploration in Goal-Driven Navigation. CoRR abs/1811.06889 (2018) - [i88]Hongyang Zhang, Susu Xu, Jiantao Jiao, Pengtao Xie, Ruslan Salakhutdinov, Eric P. Xing:
Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures. CoRR abs/1811.08010 (2018) - [i87]Xiangan Liu, Keyang Xu, Pengtao Xie, Eric P. Xing:
Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records. CoRR abs/1811.08040 (2018) - [i86]Bowen Tan, Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing:
Connecting the Dots Between MLE and RL for Sequence Generation. CoRR abs/1811.09740 (2018) - 2017
- [j47]Min Xu, Xiaoqi Chai, Hariank Muthakana, Xiaodan Liang, Ge Yang, Tzviya Zeev-Ben-Mordehai, Eric P. Xing:
Deep learning-based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms. Bioinform. 33(14): i13-i22 (2017) - [j46]Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu, Eric P. Xing:
Learning Scalable Deep Kernels with Recurrent Structure. J. Mach. Learn. Res. 18: 82:1-82:37 (2017) - [j45]Xiaojun Chang, Yaoliang Yu, Yi Yang, Eric P. Xing:
Semantic Pooling for Complex Event Analysis in Untrimmed Videos. IEEE Trans. Pattern Anal. Mach. Intell. 39(8): 1617-1632 (2017) - [c229]Lianhui Qin, Zhisong Zhang, Hai Zhao, Zhiting Hu, Eric P. Xing:
Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification. ACL (1) 2017: 1006-1017 - [c228]Pengtao Xie, Eric P. Xing:
A Constituent-Centric Neural Architecture for Reading Comprehension. ACL (1) 2017: 1405-1414 - [c227]Haohan Wang, Xiang Liu, Yunpeng Xiao, Ming Xu, Eric P. Xing:
Multiplex confounding factor correction for genomic association mapping with squared sparse linear mixed model. BIBM 2017: 194-201 - [c226]Haohan Wang, Bryon Aragam, Eric P. Xing:
Variable selection in heterogeneous datasets: A truncated-rank sparse linear mixed model with applications to genome-wide association studies. BIBM 2017: 431-438 - [c225]Xiaodan Liang, Liang Lin, Xiaohui Shen, Jiashi Feng, Shuicheng Yan, Eric P. Xing:
Interpretable Structure-Evolving LSTM. CVPR 2017: 2175-2184 - [c224]Xiaodan Liang, Lisa Lee, Eric P. Xing:
Deep Variation-Structured Reinforcement Learning for Visual Relationship and Attribute Detection. CVPR 2017: 4408-4417 - [c223]Marc T. Law, Yaoliang Yu, Raquel Urtasun, Richard S. Zemel, Eric P. Xing:
Efficient Multiple Instance Metric Learning Using Weakly Supervised Data. CVPR 2017: 5948-5956 - [c222]Mrinmaya Sachan, Avinava Dubey, Eric P. Xing:
From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems. EMNLP 2017: 773-784 - [c221]Pengtao Xie, Ruslan Salakhutdinov, Luntian Mou, Eric P. Xing:
Deep Determinantal Point Process for Large-Scale Multi-label Classification. ICCV 2017: 473-482 - [c220]Xiaodan Liang, Lisa Lee, Wei Dai, Eric P. Xing:
Dual Motion GAN for Future-Flow Embedded Video Prediction. ICCV 2017: 1762-1770 - [c219]Xiaodan Liang, Zhiting Hu, Hao Zhang, Chuang Gan, Eric P. Xing:
Recurrent Topic-Transition GAN for Visual Paragraph Generation. ICCV 2017: 3382-3391 - [c218]Prasoon Goyal, Zhiting Hu, Xiaodan Liang, Chenyu Wang, Eric P. Xing, Carnegie Mellon:
Nonparametric Variational Auto-Encoders for Hierarchical Representation Learning. ICCV 2017: 5104-5112 - [c217]Haohan Wang, Aaksha Meghawat, Louis-Philippe Morency, Eric P. Xing:
Select-additive learning: Improving generalization in multimodal sentiment analysis. ICME 2017: 949-954 - [c216]Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing:
Toward Controlled Generation of Text. ICML 2017: 1587-1596 - [c215]Willie Neiswanger, Eric P. Xing:
Post-Inference Prior Swapping. ICML 2017: 2594-2602 - [c214]Pengtao Xie, Yuntian Deng, Yi Zhou, Abhimanu Kumar, Yaoliang Yu, James Zou, Eric P. Xing:
Learning Latent Space Models with Angular Constraints. ICML 2017: 3799-3810 - [c213]Pengtao Xie, Aarti Singh, Eric P. Xing:
Uncorrelation and Evenness: a New Diversity-Promoting Regularizer. ICML 2017: 3811-3820 - [c212]Junxian He, Zhiting Hu, Taylor Berg-Kirkpatrick, Ying Huang, Eric P. Xing:
Efficient Correlated Topic Modeling with Topic Embedding. KDD 2017: 225-233 - [c211]Ian En-Hsu Yen, Xiangru Huang, Wei Dai, Pradeep Ravikumar, Inderjit S. Dhillon, Eric P. Xing:
PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification. KDD 2017: 545-553 - [c210]Kai Zhang, Chuanren Liu, Jie Zhang, Hui Xiong, Eric P. Xing, Jieping Ye:
Randomization or Condensation?: Linear-Cost Matrix Sketching Via Cascaded Compression Sampling. KDD 2017: 615-623 - [c209]Zhijie Deng, Hao Zhang, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu, Eric P. Xing:
Structured Generative Adversarial Networks. NIPS 2017: 3899-3909 - [c208]Mrinmaya Sachan, Eric P. Xing:
Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks. *SEM 2017: 251-261 - [c207]Pengtao Xie, Barnabás Póczos, Eric P. Xing:
Near-Orthogonality Regularization in Kernel Methods. UAI 2017 - [c206]Hao Zhang, Zeyu Zheng, Shizhen Xu, Wei Dai, Qirong Ho, Xiaodan Liang, Zhiting Hu, Jinliang Wei, Pengtao Xie, Eric P. Xing:
Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters. USENIX ATC 2017: 181-193 - [i85]Haohan Wang, Bhiksha Raj, Eric P. Xing:
On the Origin of Deep Learning. CoRR abs/1702.07800 (2017) - [i84]Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, Eric P. Xing:
Controllable Text Generation. CoRR abs/1703.00955 (2017) - [i83]Xiaodan Liang, Lisa Lee, Eric P. Xing:
Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection. CoRR abs/1703.03054 (2017) - [i82]Xiaodan Liang, Liang Lin, Xiaohui Shen, Jiashi Feng, Shuicheng Yan, Eric P. Xing:
Interpretable Structure-Evolving LSTM. CoRR abs/1703.03055 (2017) - [i81]Xiaodan Liang, Zhiting Hu, Hao Zhang, Chuang Gan, Eric P. Xing:
Recurrent Topic-Transition GAN for Visual Paragraph Generation. CoRR abs/1703.07022 (2017) - [i80]Prasoon Goyal, Zhiting Hu, Xiaodan Liang, Chenyu Wang, Eric P. Xing:
Nonparametric Variational Auto-encoders for Hierarchical Representation Learning. CoRR abs/1703.07027 (2017) - [i79]Hao Wang, Xiaodan Liang, Hao Zhang, Dit-Yan Yeung, Eric P. Xing:
ZM-Net: Real-time Zero-shot Image Manipulation Network. CoRR abs/1703.07255 (2017) - [i78]Wei Dai, Joseph Doyle, Xiaodan Liang, Hao Zhang, Nanqing Dong, Yuan Li, Eric P. Xing:
SCAN: Structure Correcting Adversarial Network for Chest X-rays Organ Segmentation. CoRR abs/1703.08770 (2017) - [i77]Lianhui Qin, Zhisong Zhang, Hai Zhao, Zhiting Hu, Eric P. Xing:
Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification. CoRR abs/1704.00217 (2017) - [i76]Maruan Al-Shedivat, Avinava Dubey, Eric P. Xing:
Contextual Explanation Networks. CoRR abs/1705.10301 (2017) - [i75]Junier B. Oliva, Kumar Avinava Dubey, Barnabás Póczos, Eric P. Xing, Jeff G. Schneider:
Recurrent Estimation of Distributions. CoRR abs/1705.10750 (2017) - [i74]Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing:
On Unifying Deep Generative Models. CoRR abs/1706.00550 (2017) - [i73]Hao Zhang, Zeyu Zheng, Shizhen Xu, Wei Dai, Qirong Ho, Xiaodan Liang, Zhiting Hu, Jinliang Wei, Pengtao Xie, Eric P. Xing:
Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters. CoRR abs/1706.03292 (2017) - [i72]Junxian He, Zhiting Hu, Taylor Berg-Kirkpatrick, Ying Huang, Eric P. Xing:
Efficient Correlated Topic Modeling with Topic Embedding. CoRR abs/1707.00206 (2017) - [i71]Benjamin J. Lengerich, Sandeep Konam, Eric P. Xing, Stephanie Rosenthal, Manuela M. Veloso:
Visual Explanations for Convolutional Neural Networks via Input Resampling. CoRR abs/1707.09641 (2017) - [i70]Xiaodan Liang, Lisa Lee, Wei Dai, Eric P. Xing:
Dual Motion GAN for Future-Flow Embedded Video Prediction. CoRR abs/1708.00284 (2017) - [i69]Xiaodan Liang, Hao Zhang, Eric P. Xing:
Generative Semantic Manipulation with Contrasting GAN. CoRR abs/1708.00315 (2017) - [i68]Zhijie Deng, Hao Zhang, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu, Eric P. Xing:
Structured Generative Adversarial Networks. CoRR abs/1711.00889 (2017) - [i67]Yuan Yang, Pengtao Xie, Xin Gao, Carol Cheng, Christy Y. Li, Hongbao Zhang, Eric P. Xing:
Predicting Discharge Medications at Admission Time Based on Deep Learning. CoRR abs/1711.01386 (2017) - [i66]Haoran Shi, Pengtao Xie, Zhiting Hu, Ming Zhang, Eric P. Xing:
Towards Automated ICD Coding Using Deep Learning. CoRR abs/1711.04075 (2017) - [i65]Wenting Ye, Xiang Liu, Haohan Wang, Eric P. Xing:
A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction. CoRR abs/1711.04162 (2017) - [i64]Shiyue Zhang, Pengtao Xie, Dong Wang, Eric P. Xing:
Medical Diagnosis From Laboratory Tests by Combining Generative and Discriminative Learning. CoRR abs/1711.04329 (2017) - [i63]Alexander Terenin, Eric P. Xing:
Techniques for proving Asynchronous Convergence results for Markov Chain Monte Carlo methods. CoRR abs/1711.06719 (2017) - [i62]Devendra Singh Sachan, Pengtao Xie, Eric P. Xing:
Effective Use of Bidirectional Language Modeling for Medical Named Entity Recognition. CoRR abs/1711.07908 (2017) - [i61]Baoyu Jing, Pengtao Xie, Eric P. Xing:
On the Automatic Generation of Medical Imaging Reports. CoRR abs/1711.08195 (2017) - [i60]Pengtao Xie, Jun Zhu, Eric P. Xing:
Diversity-Promoting Bayesian Learning of Latent Variable Models. CoRR abs/1711.08770 (2017) - [i59]Pengtao Xie, Hongbao Zhang, Eric P. Xing:
Learning Less-Overlapping Representations. CoRR abs/1711.09300 (2017) - [i58]Xun Zheng, Manzil Zaheer, Amr Ahmed, Yuan Wang, Eric P. Xing, Alexander J. Smola:
State Space LSTM Models with Particle MCMC Inference. CoRR abs/1711.11179 (2017) - [i57]Christy Y. Li, Dimitris Konomis, Graham Neubig, Pengtao Xie, Carol Cheng, Eric P. Xing:
Convolutional Neural Networks for Medical Diagnosis from Admission Notes. CoRR abs/1712.02768 (2017) - [i56]Hao Zhang, Shizhen Xu, Graham Neubig, Wei Dai, Qirong Ho, Guangwen Yang, Eric P. Xing:
Cavs: A Vertex-centric Programming Interface for Dynamic Neural Networks. CoRR abs/1712.04048 (2017) - [i55]George Philipp, Seunghak Lee, Eric P. Xing:
Stability Selection for Structured Variable Selection. CoRR abs/1712.04688 (2017) - 2016
- [j44]Seunghak Lee, Soonho Kong, Eric P. Xing:
A network-driven approach for genome-wide association mapping. Bioinform. 32(12): 164-173 (2016) - [j43]Micol Marchetti-Bowick, Junming Yin, Judie A. Howrylak, Eric P. Xing:
A time-varying group sparse additive model for genome-wide association studies of dynamic complex traits. Bioinform. 32(19): 2903-2910 (2016) - [j42]Bin Zhao, Eric P. Xing:
Sparse Output Coding for Scalable Visual Recognition. Int. J. Comput. Vis. 119(1): 60-75 (2016) - [j41]Seunghak Lee, Aurélie C. Lozano, Prabhanjan Kambadur, Eric P. Xing:
An Efficient Nonlinear Regression Approach for Genome-wide Detection of Marginal and Interacting Genetic Variations. J. Comput. Biol. 23(5): 372-389 (2016) - [j40]Qirong Ho, Junming Yin, Eric P. Xing:
Latent Space Inference of Internet-Scale Networks. J. Mach. Learn. Res. 17: 78:1-78:41 (2016) - [j39]Zhen Guo, Zhongfei (Mark) Zhang, Eric P. Xing, Christos Faloutsos:
Multimodal Data Mining in a Multimedia Database Based on Structured Max Margin Learning. ACM Trans. Knowl. Discov. Data 10(3): 23:1-23:30 (2016) - [j38]Weiguang Wang, Yingbin Liang, Eric P. Xing, Lixin Shen:
Nonparametric Decentralized Detection and Sparse Sensor Selection Via Weighted Kernel. IEEE Trans. Signal Process. 64(2): 306-321 (2016) - [c205]Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard H. Hovy, Eric P. Xing:
Harnessing Deep Neural Networks with Logic Rules. ACL (1) 2016 - [c204]Mrinmaya Sachan, Kumar Avinava Dubey, Eric P. Xing:
Science Question Answering using Instructional Materials. ACL (2) 2016 - [c203]Mrinmaya Sachan, Eric P. Xing:
Easy Questions First? A Case Study on Curriculum Learning for Question Answering. ACL (1) 2016 - [c202]Mrinmaya Sachan, Eric P. Xing:
Machine Comprehension using Rich Semantic Representations. ACL (2) 2016 - [c201]Hao Zhang, Zhiting Hu, Yuntian Deng, Mrinmaya Sachan, Zhicheng Yan, Eric P. Xing:
Learning Concept Taxonomies from Multi-modal Data. ACL (1) 2016 - [c200]Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing:
Deep Kernel Learning. AISTATS 2016: 370-378 - [c199]Yi Zhou, Yaoliang Yu, Wei Dai, Yingbin Liang, Eric P. Xing:
On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System. AISTATS 2016: 713-722 - [c198]William Herlands, Andrew Gordon Wilson, Hannes Nickisch, Seth R. Flaxman, Daniel B. Neill, Wilbert Van Panhuis, Eric P. Xing:
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces. AISTATS 2016: 1013-1021 - [c197]Junier B. Oliva, Avinava Dubey, Andrew Gordon Wilson, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing:
Bayesian Nonparametric Kernel-Learning. AISTATS 2016: 1078-1086 - [c196]Hao Cheng, Yaoliang Yu, Xinhua Zhang, Eric P. Xing, Dale Schuurmans:
Scalable and Sound Low-Rank Tensor Learning. AISTATS 2016: 1114-1123 - [c195]Aaron Harlap, Henggang Cui, Wei Dai, Jinliang Wei, Gregory R. Ganger, Phillip B. Gibbons, Garth A. Gibson, Eric P. Xing:
Addressing the straggler problem for iterative convergent parallel ML. SoCC 2016: 98-111 - [c194]Jimeng Sun, Jim Rehg, Suchi Saria, Hua Xu, Eric P. Xing:
Advanced Machine Learning for Healthcare. CRI 2016 - [c193]Xiaojun Chang, Yaoliang Yu, Yi Yang, Eric P. Xing:
They are Not Equally Reliable: Semantic Event Search Using Differentiated Concept Classifiers. CVPR 2016: 1884-1893 - [c192]Marc T. Law, Yaoliang Yu, Matthieu Cord, Eric P. Xing:
Closed-Form Training of Mahalanobis Distance for Supervised Clustering. CVPR 2016: 3909-3917 - [c191]Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric P. Xing:
Deep Neural Networks with Massive Learned Knowledge. EMNLP 2016: 1670-1679 - [c190]Henggang Cui, Hao Zhang, Gregory R. Ganger, Phillip B. Gibbons, Eric P. Xing:
GeePS: scalable deep learning on distributed GPUs with a GPU-specialized parameter server. EuroSys 2016: 4:1-4:16 - [c189]Jin Kyu Kim, Qirong Ho, Seunghak Lee, Xun Zheng, Wei Dai, Garth A. Gibson, Eric P. Xing:
STRADS: a distributed framework for scheduled model parallel machine learning. EuroSys 2016: 5:1-5:16 - [c188]Pengtao Xie, Jun Zhu, Eric P. Xing:
Diversity-Promoting Bayesian Learning of Latent Variable Models. ICML 2016: 59-68 - [c187]Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric P. Xing:
Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms. ICML 2016: 1548-1557 - [c186]Zhiting Hu, Gang Luo, Mrinmaya Sachan, Eric P. Xing, Zaiqing Nie:
Grounding Topic Models with Knowledge Bases. IJCAI 2016: 1578-1584 - [c185]Yinyan Tan, Zhe Fan, Guilin Li, Fangshan Wang, Zhengbing Li, Shikai Liu, Qiuling Pan, Eric P. Xing, Qirong Ho:
Scalable Time-Decaying Adaptive Prediction Algorithm. KDD 2016: 617-626 - [c184]Kumar Avinava Dubey, Sashank J. Reddi, Sinead A. Williamson, Barnabás Póczos, Alexander J. Smola, Eric P. Xing:
Variance Reduction in Stochastic Gradient Langevin Dynamics. NIPS 2016: 1154-1162 - [c183]Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing:
Stochastic Variational Deep Kernel Learning. NIPS 2016: 2586-2594 - [c182]Kirthevasan Kandasamy, Maruan Al-Shedivat, Eric P. Xing:
Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices. NIPS 2016: 2865-2873 - [c181]Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yaoliang Yu, Eric P. Xing:
Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting. UAI 2016 - [i54]Mrinmaya Sachan, Avinava Dubey, Eric P. Xing:
Science Question Answering using Instructional Materials. CoRR abs/1602.04375 (2016) - [i53]Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard H. Hovy, Eric P. Xing:
Harnessing Deep Neural Networks with Logic Rules. CoRR abs/1603.06318 (2016) - [i52]Willie Neiswanger, Eric P. Xing:
Prior Swapping for Data-Independent Inference. CoRR abs/1606.00787 (2016) - [i51]Hao Zhang, Zhiting Hu, Yuntian Deng, Mrinmaya Sachan, Zhicheng Yan, Eric P. Xing:
Learning Concept Taxonomies from Multi-modal Data. CoRR abs/1606.09239 (2016) - [i50]Kai Zhang, Chuanren Liu, Jie Zhang, Hui Xiong, Eric P. Xing, Jieping Ye:
Seeing the Forest from the Trees in Two Looks: Matrix Sketching by Cascaded Bilateral Sampling. CoRR abs/1607.07395 (2016) - [i49]Haohan Wang, Aaksha Meghawat, Louis-Philippe Morency, Eric P. Xing:
Select-Additive Learning: Improving Cross-individual Generalization in Multimodal Sentiment Analysis. CoRR abs/1609.05244 (2016) - [i48]Kirthevasan Kandasamy, Maruan Al-Shedivat, Eric P. Xing:
Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices. CoRR abs/1609.06390 (2016) - [i47]Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu, Eric P. Xing:
Learning Scalable Deep Kernels with Recurrent Structure. CoRR abs/1610.08936 (2016) - [i46]Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing:
Stochastic Variational Deep Kernel Learning. CoRR abs/1611.00336 (2016) - [i45]Jingkang Yang, Haohan Wang, Jun Zhu, Eric P. Xing:
SeDMiD for Confusion Detection: Uncovering Mind State from Time Series Brain Wave Data. CoRR abs/1611.10252 (2016) - 2015
- [j37]Xuefeng Wang, Eric P. Xing, Daniel J. Schaid:
Kernel methods for large-scale genomic data analysis. Briefings Bioinform. 16(2): 183-192 (2015) - [j36]André F. T. Martins, Mário A. T. Figueiredo, Pedro M. Q. Aguiar, Noah A. Smith, Eric P. Xing:
AD3: alternating directions dual decomposition for MAP inference in graphical models. J. Mach. Learn. Res. 16: 495-545 (2015) - [j35]Eric P. Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, Yaoliang Yu:
Petuum: A New Platform for Distributed Machine Learning on Big Data. IEEE Trans. Big Data 1(2): 49-67 (2015) - [j34]Weiguang Wang, Yingbin Liang, Eric P. Xing:
Collective Support Recovery for Multi-Design Multi-Response Linear Regression. IEEE Trans. Inf. Theory 61(1): 513-534 (2015) - [c180]Wei Dai, Abhimanu Kumar, Jinliang Wei, Qirong Ho, Garth A. Gibson, Eric P. Xing:
High-Performance Distributed ML at Scale through Parameter Server Consistency Models. AAAI 2015: 79-87 - [c179]Pengtao Xie, Yulong Pei, Yuan Xie, Eric P. Xing:
Mining User Interests from Personal Photos. AAAI 2015: 1896-1902 - [c178]Pengtao Xie, Eric P. Xing:
Integrating Image Clustering and Codebook Learning. AAAI 2015: 1903-1909 - [c177]Mrinmaya Sachan, Kumar Avinava Dubey, Eric P. Xing, Matthew Richardson:
Learning Answer-Entailing Structures for Machine Comprehension. ACL (1) 2015: 239-249 - [c176]Zhiting Hu, Poyao Huang, Yuntian Deng, Yingkai Gao, Eric P. Xing:
Entity Hierarchy Embedding. ACL (1) 2015: 1292-1300 - [c175]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Hy Trac, Shirley Ho, Jeff G. Schneider:
Fast Function to Function Regression. AISTATS 2015 - [c174]Yaoliang Yu, Xun Zheng, Micol Marchetti-Bowick, Eric P. Xing:
Minimizing Nonconvex Non-Separable Functions. AISTATS 2015 - [c173]Eric P. Xing:
Toward personalized pan-omic association analysis under complex structures and big data. BIBM 2015: 4 - [c172]Jinliang Wei, Wei Dai, Aurick Qiao, Qirong Ho, Henggang Cui, Gregory R. Ganger, Phillip B. Gibbons, Garth A. Gibson, Eric P. Xing:
Managed communication and consistency for fast data-parallel iterative analytics. SoCC 2015: 381-394 - [c171]Xiaojun Chang, Yi Yang, Eric P. Xing, Yaoliang Yu:
Complex Event Detection using Semantic Saliency and Nearly-Isotonic SVM. ICML 2015: 1348-1357 - [c170]Zhiting Hu, Qirong Ho, Avinava Dubey, Eric P. Xing:
Large-scale Distributed Dependent Nonparametric Trees. ICML 2015: 1651-1659 - [c169]Mrinmaya Sachan, Eduard H. Hovy, Eric P. Xing:
An Active Learning Approach to Coreference Resolution. IJCAI 2015: 1312-1318 - [c168]Xiaojun Chang, Yi Yang, Alexander G. Hauptmann, Eric P. Xing, Yaoliang Yu:
Semantic Concept Discovery for Large-Scale Zero-Shot Event Detection. IJCAI 2015: 2234-2240 - [c167]Pengtao Xie, Yuntian Deng, Eric P. Xing:
Diversifying Restricted Boltzmann Machine for Document Modeling. KDD 2015: 1315-1324 - [c166]Eric P. Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, Yaoliang Yu:
Petuum: A New Platform for Distributed Machine Learning on Big Data. KDD 2015: 1335-1344 - [c165]Hao Zhang, Gunhee Kim, Eric P. Xing:
Dynamic Topic Modeling for Monitoring Market Competition from Online Text and Image Data. KDD 2015: 1425-1434 - [c164]Xun Zheng, Yaoliang Yu, Eric P. Xing:
Linear Time Samplers for Supervised Topic Models using Compositional Proposals. KDD 2015: 1523-1532 - [c163]Pengtao Xie, Diyi Yang, Eric P. Xing:
Incorporating Word Correlation Knowledge into Topic Modeling. HLT-NAACL 2015: 725-734 - [c162]Andrew Gordon Wilson, Christoph Dann, Christopher G. Lucas, Eric P. Xing:
The Human Kernel. NIPS 2015: 2854-2862 - [c161]Seunghak Lee, Aurélie C. Lozano, Prabhanjan Kambadur, Eric P. Xing:
An Efficient Nonlinear Regression Approach for Genome-Wide Detection of Marginal and Interacting Genetic Variations. RECOMB 2015: 167-187 - [c160]Zhiting Hu, Junjie Yao, Bin Cui, Eric P. Xing:
Community Level Diffusion Extraction. SIGMOD Conference 2015: 1555-1569 - [c159]Andrei Z. Broder, Lada A. Adamic, Michael J. Franklin, Maarten de Rijke, Eric P. Xing, Kai Yu:
Big Data: New Paradigm or "Sound and Fury, Signifying Nothing"? WSDM 2015: 5-6 - [c158]Jinhui Yuan, Fei Gao, Qirong Ho, Wei Dai, Jinliang Wei, Xun Zheng, Eric Poe Xing, Tie-Yan Liu, Wei-Ying Ma:
LightLDA: Big Topic Models on Modest Computer Clusters. WWW 2015: 1351-1361 - [i44]Willie Neiswanger, Chong Wang, Eric P. Xing:
Embarrassingly Parallel Variational Inference in Nonconjugate Models. CoRR abs/1510.04163 (2015) - [i43]Andrew Gordon Wilson, Christoph Dann, Christopher G. Lucas, Eric P. Xing:
The Human Kernel. CoRR abs/1510.07389 (2015) - [i42]Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing:
Deep Kernel Learning. CoRR abs/1511.02222 (2015) - [i41]Pengtao Xie, Yuntian Deng, Eric P. Xing:
On the Generalization Error Bounds of Neural Networks under Diversity-Inducing Mutual Angular Regularization. CoRR abs/1511.07110 (2015) - [i40]Pengtao Xie, Jin Kyu Kim, Yi Zhou, Qirong Ho, Abhimanu Kumar, Yaoliang Yu, Eric P. Xing:
Distributed Machine Learning via Sufficient Factor Broadcasting. CoRR abs/1511.08486 (2015) - [i39]Abhimanu Kumar, Shriphani Palakodety, Chong Wang, Carolyn P. Rosé, Eric P. Xing, Miaomiao Wen:
Scalable Modeling of Conversational-role based Self-presentation Characteristics in Large Online Forums. CoRR abs/1512.03443 (2015) - [i38]Hao Zhang, Zhiting Hu, Jinliang Wei, Pengtao Xie, Gunhee Kim, Qirong Ho, Eric P. Xing:
Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines. CoRR abs/1512.06216 (2015) - [i37]Pengtao Xie, Yuntian Deng, Eric P. Xing:
Latent Variable Modeling with Diversity-Inducing Mutual Angular Regularization. CoRR abs/1512.07336 (2015) - [i36]Eric P. Xing, Qirong Ho, Pengtao Xie, Wei Dai:
Strategies and Principles of Distributed Machine Learning on Big Data. CoRR abs/1512.09295 (2015) - 2014
- [j33]Seyoung Kim, Eric P. Xing:
Exploiting Genome Structure in Association Analysis. J. Comput. Biol. 21(4): 345-360 (2014) - [j32]Mladen Kolar, Han Liu, Eric P. Xing:
Graph estimation from multi-attribute data. J. Mach. Learn. Res. 15(1): 1713-1750 (2014) - [j31]Jun Zhu, Ning Chen, Eric P. Xing:
Bayesian inference with posterior regularization and applications to infinite latent SVMs. J. Mach. Learn. Res. 15(1): 1799-1847 (2014) - [j30]Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal, Eric P. Xing, Masashi Sugiyama:
High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso. Neural Comput. 26(1): 185-207 (2014) - [j29]Ankur P. Parikh, Ross E. Curtis, Irene Kuhn, Sabine Becker-Weimann, Mina J. Bissell, Eric P. Xing, Wei Wu:
Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm. PLoS Comput. Biol. 10(7) (2014) - [j28]Dani Yogatama, Chong Wang, Bryan R. Routledge, Noah A. Smith, Eric P. Xing:
Dynamic Language Models for Streaming Text. Trans. Assoc. Comput. Linguistics 2: 181-192 (2014) - [c157]Ankur P. Parikh, Shay B. Cohen, Eric P. Xing:
Spectral Unsupervised Parsing with Additive Tree Metrics. ACL (1) 2014: 1062-1072 - [c156]Abhimanu Kumar, Alex Beutel, Qirong Ho, Eric P. Xing:
Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data. AISTATS 2014: 531-539 - [c155]Willie Neiswanger, Frank D. Wood, Eric P. Xing:
The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling. AISTATS 2014: 660-668 - [c154]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing:
Fast Distribution To Real Regression. AISTATS 2014: 706-714 - [c153]Henggang Cui, Alexey Tumanov, Jinliang Wei, Lianghong Xu, Wei Dai, Jesse Haber-Kucharsky, Qirong Ho, Gregory R. Ganger, Phillip B. Gibbons, Garth A. Gibson, Eric P. Xing:
Exploiting iterative-ness for parallel ML computations. SoCC 2014: 5:1-5:14 - [c152]Bin Zhao, Eric P. Xing:
Hierarchical Feature Hashing for Fast Dimensionality Reduction. CVPR 2014: 2051-2058 - [c151]Bin Zhao, Eric P. Xing:
Quasi Real-Time Summarization for Consumer Videos. CVPR 2014: 2513-2520 - [c150]Gunhee Kim, Eric P. Xing:
Reconstructing Storyline Graphs for Image Recommendation from Web Community Photos. CVPR 2014: 3882-3889 - [c149]Gunhee Kim, Leonid Sigal, Eric P. Xing:
Joint Summarization of Large-Scale Collections of Web Images and Videos for Storyline Reconstruction. CVPR 2014: 4225-4232 - [c148]Ankur P. Parikh, Avneesh Saluja, Chris Dyer, Eric P. Xing:
Language Modeling with Power Low Rank Ensembles. EMNLP 2014: 1487-1498 - [c147]Eric P. Xing:
ParLearning Keynote. IPDPS Workshops 2014: 1601 - [c146]Weiguang Wang, Yingbin Liang, Eric P. Xing, Lixin Shen:
Sparse sensor selection for nonparametric decentralized detection via L1 regularization. MLSP 2014: 1-6 - [c145]Kumar Avinava Dubey, Qirong Ho, Sinead A. Williamson, Eric P. Xing:
Dependent nonparametric trees for dynamic hierarchical clustering. NIPS 2014: 1152-1160 - [c144]Seunghak Lee, Jin Kyu Kim, Xun Zheng, Qirong Ho, Garth A. Gibson, Eric P. Xing:
On Model Parallelization and Scheduling Strategies for Distributed Machine Learning. NIPS 2014: 2834-2842 - [c143]Ankur P. Parikh, Wei Wu, Eric P. Xing:
Robust Reverse Engineering of Dynamic Gene Networks Under Sample Size Heterogeneity. Pacific Symposium on Biocomputing 2014: 265-276 - [c142]Alex Beutel, Partha Pratim Talukdar, Abhimanu Kumar, Christos Faloutsos, Evangelos E. Papalexakis, Eric P. Xing:
FlexiFaCT: Scalable Flexible Factorization of Coupled Tensors on Hadoop. SDM 2014: 109-117 - [c141]Kumar Avinava Dubey, Sinead Williamson, Eric P. Xing:
Parallel Markov Chain Monte Carlo for Pitman-Yor Mixture Models. UAI 2014: 142-151 - [c140]Willie Neiswanger, Chong Wang, Eric P. Xing:
Asymptotically Exact, Embarrassingly Parallel MCMC. UAI 2014: 623-632 - [c139]Willie Neiswanger, Chong Wang, Qirong Ho, Eric P. Xing:
Modeling Citation Networks Using Latent Random Offsets. UAI 2014: 633-642 - [c138]Henggang Cui, James Cipar, Qirong Ho, Jin Kyu Kim, Seunghak Lee, Abhimanu Kumar, Jinliang Wei, Wei Dai, Gregory R. Ganger, Phillip B. Gibbons, Garth A. Gibson, Eric P. Xing:
Exploiting Bounded Staleness to Speed Up Big Data Analytics. USENIX ATC 2014: 37-48 - [c137]Mrinmaya Sachan, Avinava Dubey, Shashank Srivastava, Eric P. Xing, Eduard H. Hovy:
Spatial compactness meets topical consistency: jointly modeling links and content for community detection. WSDM 2014: 503-512 - [c136]Gunhee Kim, Eric P. Xing:
Visualizing brand associations from web community photos. WSDM 2014: 623-632 - [r3]Jun Zhu, Eric P. Xing:
Discriminative Training of Mixed Membership Models. Handbook of Mixed Membership Models and Their Applications 2014: 369-393 - [r2]Suyash Shringarpure, Eric P. Xing:
Population Stratification with Mixed Membership Models. Handbook of Mixed Membership Models and Their Applications 2014: 397-416 - [r1]Qirong Ho, Eric P. Xing:
Analyzing Time-Evolving Networks. Handbook of Mixed Membership Models and Their Applications 2014: 489-525 - [i35]Seunghak Lee, Jin Kyu Kim, Xun Zheng, Qirong Ho, Garth A. Gibson, Eric P. Xing:
Primitives for Dynamic Big Model Parallelism. CoRR abs/1406.4580 (2014) - [i34]Pengtao Xie, Eric P. Xing:
CryptGraph: Privacy Preserving Graph Analytics on Encrypted Graph. CoRR abs/1409.5021 (2014) - [i33]Pengtao Xie, Jin Kyu Kim, Eric P. Xing:
Large Scale Distributed Multiclass Logistic Regression. CoRR abs/1409.5705 (2014) - [i32]Seunghak Lee, Eric P. Xing:
Screening Rules for Overlapping Group Lasso. CoRR abs/1410.6880 (2014) - [i31]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Eric P. Xing, Jeff G. Schneider:
Fast Function to Function Regression. CoRR abs/1410.7414 (2014) - [i30]Wei Dai, Abhimanu Kumar, Jinliang Wei, Qirong Ho, Garth A. Gibson, Eric P. Xing:
High-Performance Distributed ML at Scale through Parameter Server Consistency Models. CoRR abs/1410.8043 (2014) - [i29]Xun Zheng, Jin Kyu Kim, Qirong Ho, Eric P. Xing:
Model-Parallel Inference for Big Topic Models. CoRR abs/1411.2305 (2014) - [i28]Jinhui Yuan, Fei Gao, Qirong Ho, Wei Dai, Jinliang Wei, Xun Zheng, Eric P. Xing, Tie-Yan Liu, Wei-Ying Ma:
LightLDA: Big Topic Models on Modest Compute Clusters. CoRR abs/1412.1576 (2014) - [i27]Pengtao Xie, Eric P. Xing:
Large Scale Distributed Distance Metric Learning. CoRR abs/1412.5949 (2014) - [i26]Pengtao Xie, Eric P. Xing:
Cauchy Principal Component Analysis. CoRR abs/1412.6506 (2014) - 2013
- [j27]Kriti Puniyani, Eric P. Xing:
NP-MuScL: Unsupervised Global Prediction of Interaction Networks from Multiple Data Sources. J. Comput. Biol. 20(11): 892-904 (2013) - [j26]Kriti Puniyani, Eric P. Xing:
GINI: From ISH Images to Gene Interaction Networks. PLoS Comput. Biol. 9(10) (2013) - [c135]Weiguang Wang, Yingbin Liang, Eric P. Xing:
Block Regularized Lasso for Multivariate Multi-Response Linear Regression. AISTATS 2013: 608-617 - [c134]Gunhee Kim, Eric P. Xing:
Jointly Aligning and Segmenting Multiple Web Photo Streams for the Inference of Collective Photo Storylines. CVPR 2013: 620-627 - [c133]Bin Zhao, Eric P. Xing:
Sparse Output Coding for Large-Scale Visual Recognition. CVPR 2013: 3350-3357 - [c132]James Cipar, Qirong Ho, Jin Kyu Kim, Seunghak Lee, Gregory R. Ganger, Garth Gibson, Kimberly Keeton, Eric P. Xing:
Solving the Straggler Problem with Bounded Staleness. HotOS 2013 - [c131]Gunhee Kim, Eric P. Xing:
Discovering Pictorial Brand Associations from Large-Scale Online Image Data. ICCV Workshops 2013: 404-411 - [c130]Mladen Kolar, Han Liu, Eric P. Xing:
Markov Network Estimation From Multi-attribute Data. ICML (3) 2013: 73-81 - [c129]Sinead Williamson, Avinava Dubey, Eric P. Xing:
Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models. ICML (1) 2013: 98-106 - [c128]Rajesh Ranganath, Chong Wang, David M. Blei, Eric P. Xing:
An Adaptive Learning Rate for Stochastic Variational Inference. ICML (2) 2013: 298-306 - [c127]Le Song, Mariya Ishteva, Ankur P. Parikh, Eric P. Xing, Haesun Park:
Hierarchical Tensor Decomposition of Latent Tree Graphical Models. ICML (3) 2013: 334-342 - [c126]Pengtao Xie, Eric P. Xing:
Multi-Modal Distance Metric Learning. IJCAI 2013: 1806-1812 - [c125]Amr Ahmed, Eric P. Xing:
Scalable Dynamic Nonparametric Bayesian Models of Content and Users. IJCAI 2013: 3111-3115 - [c124]Mohammad Taha Bahadori, Yan Liu, Eric P. Xing:
Fast structure learning in generalized stochastic processes with latent factors. KDD 2013: 284-292 - [c123]Chong Wang, Xi Chen, Alexander J. Smola, Eric P. Xing:
Variance Reduction for Stochastic Gradient Optimization. NIPS 2013: 181-189 - [c122]Junming Yin, Qirong Ho, Eric P. Xing:
A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks. NIPS 2013: 422-430 - [c121]Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B. Gibbons, Garth A. Gibson, Gregory R. Ganger, Eric P. Xing:
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server. NIPS 2013: 1223-1231 - [c120]Sinead Williamson, Steve N. MacEachern, Eric P. Xing:
Restricting exchangeable nonparametric distributions. NIPS 2013: 2598-2606 - [c119]Kriti Puniyani, Eric P. Xing:
NP-MuScL: Unsupervised Global Prediction of Interaction Networks from Multiple Data Sources. RECOMB 2013: 173-185 - [c118]Avinava Dubey, Ahmed Hefny, Sinead Williamson, Eric P. Xing:
A Nonparametric Mixture Model for Topic Modeling over Time. SDM 2013: 530-538 - [c117]Pengtao Xie, Eric P. Xing:
Integrating Document Clustering and Topic Modeling. UAI 2013 - [c116]Gunhee Kim, Eric P. Xing:
Time-sensitive web image ranking and retrieval via dynamic multi-task regression. WSDM 2013: 163-172 - [i25]Weiguang Wang, Yingbin Liang, Eric P. Xing:
Sharp Threshold for Multivariate Multi-Response Linear Regression via Block Regularized Lasso. CoRR abs/1307.7993 (2013) - [i24]Pengtao Xie, Eric P. Xing:
Integrating Document Clustering and Topic Modeling. CoRR abs/1309.6874 (2013) - [i23]Junier B. Oliva, Willie Neiswanger, Barnabás Póczos, Jeff G. Schneider, Eric P. Xing:
Fast Distribution To Real Regression. CoRR abs/1311.2236 (2013) - [i22]Willie Neiswanger, Chong Wang, Eric P. Xing:
Asymptotically Exact, Embarrassingly Parallel MCMC. CoRR abs/1311.4780 (2013) - [i21]Zhiting Hu, Chong Wang, Junjie Yao, Eric P. Xing, Hongzhi Yin, Bin Cui:
Community Specific Temporal Topic Discovery from Social Media. CoRR abs/1312.0860 (2013) - [i20]Seunghak Lee, Jin Kyu Kim, Qirong Ho, Garth A. Gibson, Eric P. Xing:
Structure-Aware Dynamic Scheduler for Parallel Machine Learning. CoRR abs/1312.5766 (2013) - [i19]Ankur P. Parikh, Avneesh Saluja, Chris Dyer, Eric P. Xing:
Language Modeling with Power Low Rank Ensembles. CoRR abs/1312.7077 (2013) - [i18]Wei Dai, Jinliang Wei, Xun Zheng, Jin Kyu Kim, Seunghak Lee, Junming Yin, Qirong Ho, Eric P. Xing:
Petuum: A Framework for Iterative-Convergent Distributed ML. CoRR abs/1312.7651 (2013) - [i17]Jinliang Wei, Wei Dai, Abhimanu Kumar, Xun Zheng, Qirong Ho, Eric P. Xing:
Consistent Bounded-Asynchronous Parameter Servers for Distributed ML. CoRR abs/1312.7869 (2013) - 2012
- [j25]Seunghak Lee, Eric P. Xing:
Leveraging input and output structures for joint mapping of epistatic and marginal eQTLs. Bioinform. 28(12): 137-146 (2012) - [j24]Ross E. Curtis, Jing Xiang, Ankur P. Parikh, Peter Kinnaird, Eric P. Xing:
Enabling dynamic network analysis through visualization in TVNViewer. BMC Bioinform. 13: 204 (2012) - [j23]Jun Zhu, Amr Ahmed, Eric P. Xing:
MedLDA: maximum margin supervised topic models. J. Mach. Learn. Res. 13: 2237-2278 (2012) - [j22]Ning Chen, Jun Zhu, Fuchun Sun, Eric P. Xing:
Large-Margin Predictive Latent Subspace Learning for Multiview Data Analysis. IEEE Trans. Pattern Anal. Mach. Intell. 34(12): 2365-2378 (2012) - [c115]Yu Zhang, Dit-Yan Yeung, Eric P. Xing:
Supervised Probabilistic Robust Embedding with Sparse Noise. AAAI 2012: 1226-1232 - [c114]Li-Jia Li, Jun Zhu, Hao Su, Eric P. Xing, Li Fei-Fei:
Multi-Level Structured Image Coding on High-Dimensional Image Representation. ACCV (2) 2012: 147-161 - [c113]Eric P. Xing:
Topic Models, Latent Space Models, Sparse Coding, and All That: A Systematic Understanding of Probabilistic Semantic Extraction in Large Corpus. ACL (Tutorial Abstracts) 2012: 3 - [c112]Jacob Eisenstein, Duen Horng Chau, Aniket Kittur, Eric P. Xing:
TopicViz: interactive topic exploration in document collections. CHI Extended Abstracts 2012: 2177-2182 - [c111]Gunhee Kim, Eric P. Xing:
On multiple foreground cosegmentation. CVPR 2012: 837-844 - [c110]Kriti Puniyani, Eric P. Xing:
Inferring Gene Interaction Networks from ISH Images via Kernelized Graphical Models. ECCV (6) 2012: 72-85 - [c109]Mladen Kolar, Eric P. Xing:
Consistent Covariance Selection From Data With Missing Values. ICML 2012 - [c108]Junming Yin, Xi Chen, Eric P. Xing:
Group Sparse Additive Models. ICML 2012 - [c107]Jiayao Hu, Yingbin Liang, Eric P. Xing:
Nonparametric decentralized detection based on weighted count kernel. ISIT 2012: 324-328 - [c106]Gunhee Kim, Li Fei-Fei, Eric P. Xing:
Web image prediction using multivariate point processes. KDD 2012: 1068-1076 - [c105]Kosuke Fukumasu, Koji Eguchi, Eric P. Xing:
Symmetric Correspondence Topic Models for Multilingual Text Analysis. NIPS 2012: 1295-1303 - [c104]Qixia Jiang, Jun Zhu, Maosong Sun, Eric P. Xing:
Monte Carlo Methods for Maximum Margin Supervised Topic Models. NIPS 2012: 1601-1609 - [c103]Qirong Ho, Junming Yin, Eric P. Xing:
On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks. NIPS 2012: 2141-2149 - [c102]Ross E. Curtis, Junming Yin, Peter Kinnaird, Eric P. Xing:
Finding Genome-Transcriptome-Phenome Associations with Structured Association Mapping and Visualization in GenAMap. Pacific Symposium on Biocomputing 2012: 327-338 - [c101]Ankur P. Parikh, Le Song, Mariya Ishteva, Gabi Teodoru, Eric P. Xing:
A Spectral Algorithm for Latent Junction Trees. UAI 2012: 675-684 - [c100]Qirong Ho, Jacob Eisenstein, Eric P. Xing:
Document hierarchies from text and links. WWW 2012: 739-748 - [i16]Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing:
Smoothing Proximal Gradient Method for General Structured Sparse Learning. CoRR abs/1202.3708 (2012) - [i15]Jun Zhu, Eric P. Xing:
Sparse Topical Coding. CoRR abs/1202.3778 (2012) - [i14]Amr Ahmed, Eric P. Xing:
Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream. CoRR abs/1203.3463 (2012) - [i13]Junming Yin, Xi Chen, Eric P. Xing:
Group Sparse Additive Models. CoRR abs/1206.4673 (2012) - [i12]Eric P. Xing, Rong Yan, Alexander G. Hauptmann:
Mining Associated Text and Images with Dual-Wing Harmoniums. CoRR abs/1207.1423 (2012) - [i11]Eric P. Xing, Michael I. Jordan, Stuart Russell:
Graph partition strategies for generalized mean field inference. CoRR abs/1207.4156 (2012) - [i10]Jun Zhu, Ning Chen, Eric P. Xing:
Bayesian Inference with Posterior Regularization and Infinite Latent Support Vector Machines. CoRR abs/1210.1766 (2012) - [i9]Ankur P. Parikh, Le Song, Mariya Ishteva, Gabi Teodoru, Eric P. Xing:
A Spectral Algorithm for Latent Junction Trees. CoRR abs/1210.4884 (2012) - [i8]Jacob Eisenstein, Brendan O'Connor, Noah A. Smith, Eric P. Xing:
Mapping the geographical diffusion of new words. CoRR abs/1210.5268 (2012) - [i7]Qirong Ho, Rong Yan, Rajat Raina, Eric P. Xing:
Understanding the Interaction between Interests, Conversations and Friendships in Facebook. CoRR abs/1211.0028 (2012) - [i6]Eric P. Xing, Michael I. Jordan, Stuart Russell:
A Generalized Mean Field Algorithm for Variational Inference in Exponential Families. CoRR abs/1212.2512 (2012) - [i5]André F. T. Martins, Mário A. T. Figueiredo, Pedro M. Q. Aguiar, Noah A. Smith, Eric P. Xing:
Alternating Directions Dual Decomposition. CoRR abs/1212.6550 (2012) - 2011
- [j21]Ankur P. Parikh, Wei Wu, Ross E. Curtis, Eric P. Xing:
TREEGL: reverse engineering tree-evolving gene networks underlying developing biological lineages. Bioinform. 27(13): 196-204 (2011) - [j20]Suyash Shringarpure, Daegun Won, Eric P. Xing:
StructHDP: automatic inference of number of clusters and population structure from admixed genotype data. Bioinform. 27(13): 324-332 (2011) - [j19]Ross E. Curtis, Amos Yuen, Le Song, Anuj Goyal, Eric P. Xing:
TVNViewer: An interactive visualization tool for exploring networks that change over time or space. Bioinform. 27(13): 1880-1881 (2011) - [c99]Jacob Eisenstein, Noah A. Smith, Eric P. Xing:
Discovering Sociolinguistic Associations with Structured Sparsity. ACL 2011: 1365-1374 - [c98]Jiayao Hu, Yingbin Liang, Eric P. Xing:
Nonparametric decision making based on tree-structured information aggregation. Allerton 2011: 1853-1860 - [c97]Ross E. Curtis, Peter Kinnaird, Eric P. Xing:
GenAMap: Visualization strategies for structured association mapping. BioVis 2011: 87-94 - [c96]Bin Zhao, Xiaoxin Yin, Eric P. Xing:
Max margin learning on domain-independent web information extraction. CIKM 2011: 1305-1310 - [c95]Bin Zhao, Li Fei-Fei, Eric P. Xing:
Online detection of unusual events in videos via dynamic sparse coding. CVPR 2011: 3313-3320 - [c94]Jacob Eisenstein, Tae Yano, William W. Cohen, Noah A. Smith, Eric P. Xing:
Structured Databases of Named Entities from Bayesian Nonparametrics. ULNLP@EMNLP 2011: 2-12 - [c93]Gunhee Kim, Eric P. Xing, Li Fei-Fei, Takeo Kanade:
Distributed cosegmentation via submodular optimization on anisotropic diffusion. ICCV 2011: 169-176 - [c92]André F. T. Martins, Mário A. T. Figueiredo, Pedro M. Q. Aguiar, Noah A. Smith, Eric P. Xing:
An Augmented Lagrangian Approach to Constrained MAP Inference. ICML 2011: 169-176 - [c91]Jun Zhu, Ning Chen, Eric P. Xing:
Infinite SVM: a Dirichlet Process Mixture of Large-margin Kernel Machines. ICML 2011: 617-624 - [c90]Jacob Eisenstein, Amr Ahmed, Eric P. Xing:
Sparse Additive Generative Models of Text. ICML 2011: 1041-1048 - [c89]Ankur P. Parikh, Le Song, Eric P. Xing:
A Spectral Algorithm for Latent Tree Graphical Models. ICML 2011: 1065-1072 - [c88]Hetunandan Kamisetty, Eric P. Xing, Christopher James Langmead:
Approximating Correlated Equilibria using Relaxations on the Marginal Polytope. ICML 2011: 1153-1160 - [c87]Eric P. Xing:
Genome-Phenome Association Analysis of Complex Diseases a Structured Sparse Regression Approach - (Keynote Talk). ISBRA 2011: 11 - [c86]Jun Zhu, Ni Lao, Ning Chen, Eric P. Xing:
Conditional topical coding: an efficient topic model conditioned on rich features. KDD 2011: 475-483 - [c85]Bin Zhao, Li Fei-Fei, Eric P. Xing:
Large-Scale Category Structure Aware Image Categorization. NIPS 2011: 1251-1259 - [c84]Jun Zhu, Ning Chen, Eric P. Xing:
Infinite Latent SVM for Classification and Multi-task Learning. NIPS 2011: 1620-1628 - [c83]Le Song, Ankur P. Parikh, Eric P. Xing:
Kernel Embeddings of Latent Tree Graphical Models. NIPS 2011: 2708-2716 - [c82]Xi Chen, Qihang Lin, Seyoung Kim, Jaime G. Carbonell, Eric P. Xing:
Smoothing Proximal Gradient Method for General Structured Sparse Learning. UAI 2011: 105-114 - [c81]Jun Zhu, Eric P. Xing:
Sparse Topical Coding. UAI 2011: 831-838