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2020 – today
- 2023
[j26]Liu Liu
, Ji Liu, Cho-Jui Hsieh, Dacheng Tao:
Stochastically Controlled Compositional Gradient for Composition Problems. IEEE Trans. Neural Networks Learn. Syst. 34(2): 611-622 (2023)- [c184]Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi, Cho-Jui Hsieh:
Training Meta-Surrogate Model for Transferable Adversarial Attack. AAAI 2023: 9516-9524 - [c183]Anaelia Ovalle, Evan Czyzycki, Cho-Jui Hsieh:
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning (Student Abstract). AAAI 2023: 16292-16293 - [c182]Zixuan Ling, Xiaoqing Zheng, Jianhan Xu, Jinshu Lin, Kai-Wei Chang, Cho-Jui Hsieh, Xuanjing Huang:
Enhancing Unsupervised Semantic Parsing with Distributed Contextual Representations. ACL (Findings) 2023: 11454-11465 - [c181]Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, Cho-Jui Hsieh:
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning. CVPR 2023: 16323-16332 - [c180]Neha Prakriya, Yu Yang, Baharan Mirzasoleiman, Cho-Jui Hsieh, Jason Cong:
NeSSA: Near-Storage Data Selection for Accelerated Machine Learning Training. HotStorage 2023: 8-15 - [c179]Andrew Bai, Chih-Kuan Yeh, Neil Y. C. Lin, Pradeep Kumar Ravikumar, Cho-Jui Hsieh:
Concept Gradient: Concept-based Interpretation Without Linear Assumption. ICLR 2023 - [c178]Li-Cheng Lan, Huan Zhang, Cho-Jui Hsieh:
Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories. ICLR 2023 - [c177]Si Si, Felix X. Yu, Ankit Singh Rawat, Cho-Jui Hsieh, Sanjiv Kumar:
Serving Graph Compression for Graph Neural Networks. ICLR 2023 - [c176]Yi Zeng, Zhouxing Shi, Ming Jin, Feiyang Kang, Lingjuan Lyu, Cho-Jui Hsieh, Ruoxi Jia:
Towards Robustness Certification Against Universal Perturbations. ICLR 2023 - [c175]Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang, Olgica Milenkovic, Hsiang-Fu Yu:
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation. ICML 2023: 5616-5630 - [c174]Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh:
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory. ICML 2023: 6565-6590 - [c173]Che-Ping Tsai, Jiong Zhang, Hsiang-Fu Yu, Eli Chien, Cho-Jui Hsieh, Pradeep Kumar Ravikumar:
Representer Point Selection for Explaining Regularized High-dimensional Models. ICML 2023: 34469-34490 - [c172]Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu:
Uncertainty Quantification for Extreme Classification. SIGIR 2023: 1649-1659 - [c171]Patrick H. Chen, Wei-Cheng Chang, Jyun-Yu Jiang, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search. WWW 2023: 3225-3235 - [i158]Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel, Yao Qin:
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data. CoRR abs/2302.01381 (2023) - [i157]Xiangning Chen, Chen Liang, Da Huang, Esteban Real, Kaiyuan Wang, Yao Liu, Hieu Pham, Xuanyi Dong, Thang Luong, Cho-Jui Hsieh, Yifeng Lu, Quoc V. Le:
Symbolic Discovery of Optimization Algorithms. CoRR abs/2302.06675 (2023) - [i156]Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee:
Online Continuous Hyperparameter Optimization for Contextual Bandits. CoRR abs/2302.09440 (2023) - [i155]Yuanhao Xiong, Long Zhao, Boqing Gong, Ming-Hsuan Yang, Florian Schroff, Ting Liu, Cho-Jui Hsieh, Liangzhe Yuan:
Spatiotemporally Discriminative Video-Language Pre-Training with Text Grounding. CoRR abs/2303.16341 (2023) - [i154]Li-Cheng Lan, Huan Zhang, Cho-Jui Hsieh:
Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories. CoRR abs/2304.13424 (2023) - [i153]Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang, Olgica Milenkovic, Hsiang-Fu Yu:
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation. CoRR abs/2305.12349 (2023) - [i152]Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee:
Robust Lipschitz Bandits to Adversarial Corruptions. CoRR abs/2305.18543 (2023) - [i151]Yihan Wang, Jatin Chauhan, Wei Wang, Cho-Jui Hsieh:
Universality and Limitations of Prompt Tuning. CoRR abs/2305.18787 (2023) - [i150]Zhouxing Shi, Yihan Wang, Fan Yin, Xiangning Chen, Kai-Wei Chang, Cho-Jui Hsieh:
Red Teaming Language Model Detectors with Language Models. CoRR abs/2305.19713 (2023) - [i149]Che-Ping Tsai, Jiong Zhang, Eli Chien, Hsiang-Fu Yu, Cho-Jui Hsieh, Pradeep Ravikumar:
Representer Point Selection for Explaining Regularized High-dimensional Models. CoRR abs/2305.20002 (2023) - 2022
- [j25]Yu-Chuan Su, Soravit Changpinyo, Xiangning Chen, Sathish Thoppay, Cho-Jui Hsieh, Lior Shapira, Radu Soricut, Hartwig Adam, Matthew Brown, Ming-Hsuan Yang, Boqing Gong:
2.5D visual relationship detection. Comput. Vis. Image Underst. 224: 103557 (2022) - [j24]Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh:
On the Adversarial Robustness of Vision Transformers. Trans. Mach. Learn. Res. 2022 (2022) - [j23]Hojung Lee, Cho-Jui Hsieh, Jong-Seok Lee:
Local Critic Training for Model-Parallel Learning of Deep Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 33(9): 4424-4436 (2022) - [c170]Jianhan Xu, Cenyuan Zhang, Xiaoqing Zheng, Linyang Li, Cho-Jui Hsieh, Kai-Wei Chang, Xuanjing Huang:
Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples. ACL (Findings) 2022: 1694-1707 - [c169]Fan Yin, Zhouxing Shi, Cho-Jui Hsieh, Kai-Wei Chang:
On the Sensitivity and Stability of Model Interpretations in NLP. ACL (1) 2022: 2631-2647 - [c168]Cenyuan Zhang, Xiang Zhou, Yixin Wan, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh:
Improving the Adversarial Robustness of NLP Models by Information Bottleneck. ACL (Findings) 2022: 3588-3598 - [c167]Qin Ding, Cho-Jui Hsieh, James Sharpnack:
Robust Stochastic Linear Contextual Bandits Under Adversarial Attacks. AISTATS 2022: 7111-7123 - [c166]Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh:
Robust Text CAPTCHAs Using Adversarial Examples. IEEE Big Data 2022: 1495-1504 - [c165]Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, Yang You:
Towards Efficient and Scalable Sharpness-Aware Minimization. CVPR 2022: 12350-12360 - [c164]Yuanhao Xiong, Cho-Jui Hsieh:
Learning to Learn with Smooth Regularization. ECCV (23) 2022: 550-565 - [c163]Fan Yin, Yao Li, Cho-Jui Hsieh, Kai-Wei Chang:
ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation. EMNLP 2022: 6567-6584 - [c162]Jianhan Xu, Linyang Li, Jiping Zhang, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh, Xuanjing Huang:
Weight Perturbation as Defense against Adversarial Word Substitutions. EMNLP (Findings) 2022: 7054-7063 - [c161]Xiangning Chen, Cho-Jui Hsieh, Boqing Gong:
When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations. ICLR 2022 - [c160]Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, Inderjit S. Dhillon:
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction. ICLR 2022 - [c159]Shoukang Hu, Ruochen Wang, Lanqing Hong, Zhenguo Li, Cho-Jui Hsieh, Jiashi Feng:
Generalizing Few-Shot NAS with Gradient Matching. ICLR 2022 - [c158]Yong Liu, Xiangning Chen, Minhao Cheng, Cho-Jui Hsieh, Yang You:
Concurrent Adversarial Learning for Large-Batch Training. ICLR 2022 - [c157]Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh:
On the Convergence of Certified Robust Training with Interval Bound Propagation. ICLR 2022 - [c156]Yuanhao Xiong, Li-Cheng Lan, Xiangning Chen, Ruochen Wang, Cho-Jui Hsieh:
Learning to Schedule Learning rate with Graph Neural Networks. ICLR 2022 - [c155]Huan Zhang, Shiqi Wang, Kaidi Xu, Yihan Wang, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter:
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks. ICML 2022: 26591-26604 - [c154]Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, Jong-Seok Lee:
Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attacks. ICPR 2022: 1287-1293 - [c153]Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit S. Dhillon, Cho-Jui Hsieh:
CAT: Customized Adversarial Training for Improved Robustness. IJCAI 2022: 673-679 - [c152]Hsiang-Fu Yu, Jiong Zhang, Wei-Cheng Chang, Jyun-Yu Jiang, Wei Li, Cho-Jui Hsieh:
PECOS: Prediction for Enormous and Correlated Output Spaces. KDD 2022: 4848-4849 - [c151]Pin-Yu Chen, Cho-Jui Hsieh, Bo Li, Sijia Liu:
The Fourth Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2022). KDD 2022: 4858-4859 - [c150]Yuanhao Xiong, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Inderjit S. Dhillon:
Extreme Zero-Shot Learning for Extreme Text Classification. NAACL-HLT 2022: 5455-5468 - [c149]Qin Ding, Yue Kang, Yi-Wei Liu, Thomas Chun Man Lee, Cho-Jui Hsieh, James Sharpnack:
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms. NeurIPS 2022 - [c148]Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh:
DC-BENCH: Dataset Condensation Benchmark. NeurIPS 2022 - [c147]Nilesh Gupta, Patrick H. Chen, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces. NeurIPS 2022 - [c146]Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee:
Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems. NeurIPS 2022 - [c145]Li-Cheng Lan, Huan Zhang, Ti-Rong Wu, Meng-Yu Tsai, I-Chen Wu, Cho-Jui Hsieh:
Are AlphaZero-like Agents Robust to Adversarial Perturbations? NeurIPS 2022 - [c144]Yong Liu, Siqi Mai, Minhao Cheng, Xiangning Chen, Cho-Jui Hsieh, Yang You:
Random Sharpness-Aware Minimization. NeurIPS 2022 - [c143]Zhouxing Shi, Yihan Wang, Huan Zhang, J. Zico Kolter, Cho-Jui Hsieh:
Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation. NeurIPS 2022 - [c142]Ruochen Wang, Yuanhao Xiong, Minhao Cheng, Cho-Jui Hsieh:
Efficient Non-Parametric Optimizer Search for Diverse Tasks. NeurIPS 2022 - [c141]Huan Zhang, Shiqi Wang, Kaidi Xu, Linyi Li, Bo Li, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter:
General Cutting Planes for Bound-Propagation-Based Neural Network Verification. NeurIPS 2022 - [c140]Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu:
Relevance under the Iceberg: Reasonable Prediction for Extreme Multi-label Classification. SIGIR 2022: 1870-1874 - [i148]Yong Liu, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, Yang You:
Towards Efficient and Scalable Sharpness-Aware Minimization. CoRR abs/2203.02714 (2022) - [i147]Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh:
On the Convergence of Certified Robust Training with Interval Bound Propagation. CoRR abs/2203.08961 (2022) - [i146]Shoukang Hu, Ruochen Wang, Lanqing Hong, Zhenguo Li, Cho-Jui Hsieh, Jiashi Feng:
Generalizing Few-Shot NAS with Gradient Matching. CoRR abs/2203.15207 (2022) - [i145]Cenyuan Zhang, Xiang Zhou, Yixin Wan, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh:
Improving the Adversarial Robustness of NLP Models by Information Bottleneck. CoRR abs/2206.05511 (2022) - [i144]Patrick H. Chen, Wei-Cheng Chang, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search. CoRR abs/2206.11408 (2022) - [i143]Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh:
DC-BENCH: Dataset Condensation Benchmark. CoRR abs/2207.09639 (2022) - [i142]Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, Cho-Jui Hsieh:
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning. CoRR abs/2207.09653 (2022) - [i141]Huan Zhang, Shiqi Wang, Kaidi Xu, Linyi Li, Bo Li, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter:
General Cutting Planes for Bound-Propagation-Based Neural Network Verification. CoRR abs/2208.05740 (2022) - [i140]Andrew Bai, Chih-Kuan Yeh, Pradeep Ravikumar, Neil Y. C. Lin, Cho-Jui Hsieh:
Concept Gradient: Concept-based Interpretation Without Linear Assumption. CoRR abs/2208.14966 (2022) - [i139]Ruochen Wang, Yuanhao Xiong, Minhao Cheng, Cho-Jui Hsieh:
Efficient Non-Parametric Optimizer Search for Diverse Tasks. CoRR abs/2209.13575 (2022) - [i138]Zhouxing Shi, Yihan Wang, Huan Zhang, J. Zico Kolter, Cho-Jui Hsieh:
Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation. CoRR abs/2210.07394 (2022) - [i137]Chenxi Gu, Chengsong Huang, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh:
Watermarking Pre-trained Language Models with Backdooring. CoRR abs/2210.07543 (2022) - [i136]Nilesh Gupta, Patrick H. Chen, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
End-to-End Learning to Index and Search in Large Output Spaces. CoRR abs/2210.08410 (2022) - [i135]Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu:
Uncertainty in Extreme Multi-label Classification. CoRR abs/2210.10160 (2022) - [i134]Andrew Bai, Cho-Jui Hsieh, Wendy Chi-wen Kan, Hsuan-Tien Lin:
Reducing Training Sample Memorization in GANs by Training with Memorization Rejection. CoRR abs/2210.12231 (2022) - [i133]Fan Yin, Yao Li, Cho-Jui Hsieh, Kai-Wei Chang:
ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation. CoRR abs/2210.12396 (2022) - [i132]Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix X. Yu, Cho-Jui Hsieh, Inderjit S. Dhillon, Sanjiv Kumar:
Preserving In-Context Learning ability in Large Language Model Fine-tuning. CoRR abs/2211.00635 (2022) - [i131]Anaelia Ovalle, Evan Czyzycki, Cho-Jui Hsieh:
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning. CoRR abs/2211.02468 (2022) - [i130]Li-Cheng Lan, Huan Zhang, Ti-Rong Wu, Meng-Yu Tsai, I-Chen Wu, Cho-Jui Hsieh:
Are AlphaZero-like Agents Robust to Adversarial Perturbations? CoRR abs/2211.03769 (2022) - [i129]Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh:
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory. CoRR abs/2211.10586 (2022) - 2021
- [j22]Yang You, Jingyue Huang, Cho-Jui Hsieh, Richard W. Vuduc, James Demmel:
Communication-avoiding kernel ridge regression on parallel and distributed systems. CCF Trans. High Perform. Comput. 3(3): 252-270 (2021) - [c139]Li-Cheng Lan, Ti-Rong Wu, I-Chen Wu, Cho-Jui Hsieh:
Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search. AAAI 2021: 259-267 - [c138]Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das:
Self-Progressing Robust Training. AAAI 2021: 7107-7115 - [c137]Benlin Liu, Yongming Rao, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh:
Multi-Proxy Wasserstein Classifier for Image Classification. AAAI 2021: 8618-8626 - [c136]Yi Zhou, Xiaoqing Zheng, Cho-Jui Hsieh, Kai-Wei Chang, Xuanjing Huang:
Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble. ACL/IJCNLP (1) 2021: 5482-5492 - [c135]Qin Ding, Cho-Jui Hsieh, James Sharpnack:
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling. AISTATS 2021: 1585-1593 - [c134]Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong:
Robust and Accurate Object Detection via Adversarial Learning. CVPR 2021: 16622-16631 - [c133]Liping Yuan, Xiaoqing Zheng, Yi Zhou, Cho-Jui Hsieh, Kai-Wei Chang:
On the Transferability of Adversarial Attacks against Neural Text Classifier. EMNLP (1) 2021: 1612-1625 - [c132]Zongyi Li, Jianhan Xu, Jiehang Zeng, Linyang Li, Xiaoqing Zheng, Qi Zhang, Kai-Wei Chang, Cho-Jui Hsieh:
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution. EMNLP (1) 2021: 3137-3147 - [c131]Yongming Rao, Benlin Liu, Yi Wei, Jiwen Lu, Cho-Jui Hsieh, Jie Zhou:
RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection. ICCV 2021: 3263-3272 - [c130]Yao Li, Martin Renqiang Min, Thomas C. M. Lee, Wenchao Yu, Erik Kruus, Wei Wang, Cho-Jui Hsieh:
Towards Robustness of Deep Neural Networks via Regularization. ICCV 2021: 7476-7485 - [c129]Ruochen Wang, Xiangning Chen, Minhao Cheng, Xiaocheng Tang, Cho-Jui Hsieh:
RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving. ICCV 2021: 10357-10366 - [c128]Xiangning Chen, Ruochen Wang, Minhao Cheng, Xiaocheng Tang, Cho-Jui Hsieh:
DrNAS: Dirichlet Neural Architecture Search. ICLR 2021 - [c127]Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh:
Evaluations and Methods for Explanation through Robustness Analysis. ICLR 2021 - [c126]Ruochen Wang, Minhao Cheng, Xiangning Chen, Xiaocheng Tang, Cho-Jui Hsieh:
Rethinking Architecture Selection in Differentiable NAS. ICLR 2021 - [c125]Kaidi Xu, Huan Zhang, Shiqi Wang, Yihan Wang, Suman Jana, Xue Lin, Cho-Jui Hsieh:
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers. ICLR 2021 - [c124]Huan Zhang, Hongge Chen, Duane S. Boning, Cho-Jui Hsieh:
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary. ICLR 2021 - [c123]Pei-Hung Chen, Wei Wei, Cho-Jui Hsieh, Bo Dai:
Overcoming Catastrophic Forgetting by Bayesian Generative Regularization. ICML 2021: 1760-1770 - [c122]Pin-Yu Chen, Cho-Jui Hsieh, Bo Li, Sijia Liu:
Third Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2021). KDD 2021: 4112-4113 - [c121]Sunipa Dev, Mehrnoosh Sameki, Jwala Dhamala, Cho-Jui Hsieh:
Measures and Best Practices for Responsible AI. KDD 2021: 4118 - [c120]Chong Zhang, Jieyu Zhao, Huan Zhang, Kai-Wei Chang, Cho-Jui Hsieh:
Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation. NAACL-HLT 2021: 3899-3916 - [c119]Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, Cho-Jui Hsieh:
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification. NeurIPS 2021: 13937-13949 - [c118]Xuanqing Liu, Wei-Cheng Chang, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S. Dhillon:
Label Disentanglement in Partition-based Extreme Multilabel Classification. NeurIPS 2021: 15359-15369 - [c117]Yang Li, Si Si, Gang Li, Cho-Jui Hsieh, Samy Bengio:
Learnable Fourier Features for Multi-dimensional Spatial Positional Encoding. NeurIPS 2021: 15816-15829 - [c116]Zhouxing Shi, Yihan Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh:
Fast Certified Robust Training with Short Warmup. NeurIPS 2021: 18335-18349 - [c115]Patrick H. Chen, Hsiang-Fu Yu, Inderjit S. Dhillon, Cho-Jui Hsieh:
DRONE: Data-aware Low-rank Compression for Large NLP Models. NeurIPS 2021: 29321-29334 - [c114]Shiqi Wang, Huan Zhang, Kaidi Xu, Xue Lin, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter:
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification. NeurIPS 2021: 29909-29921 - [i128]Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh:
Robust Text CAPTCHAs Using Adversarial Examples. CoRR abs/2101.02483 (2021) - [i127]Seong-Eun Moon, Chun-Jui Chen, Cho-Jui Hsieh, Jane-Ling Wang, Jong-Seok Lee:
Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks. CoRR abs/2101.07069 (2021) - [i126]Huan Zhang, Hongge Chen, Duane S. Boning, Cho-Jui Hsieh:
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary. CoRR abs/2101.08452 (2021) - [i125]Hojung Lee, Cho-Jui Hsieh, Jong-Seok Lee:
Local Critic Training for Model-Parallel Learning of Deep Neural Networks. CoRR abs/2102.01963 (2021) - [i124]Shiqi Wang, Huan Zhang, Kaidi Xu, Xue Lin, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter:
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Verification. CoRR abs/2103.06624 (2021) - [i123]Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong:
Robust and Accurate Object Detection via Adversarial Learning. CoRR abs/2103.13886 (2021) - [i122]Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh:
On the Adversarial Robustness of Visual Transformers. CoRR abs/2103.15670 (2021) - [i121]Zhouxing Shi, Yihan Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh:
Fast Certified Robust Training via Better Initialization and Shorter Warmup. CoRR abs/2103.17268 (2021) - [i120]Chong Zhang, Jieyu Zhao, Huan Zhang, Kai-Wei Chang, Cho-Jui Hsieh:
Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation. CoRR abs/2104.05232 (2021) - [i119]Fan Yin, Zhouxing Shi, Cho-Jui Hsieh, Kai-Wei Chang:
On the Faithfulness Measurements for Model Interpretations. CoRR abs/2104.08782 (2021) - [i118]Yu-Chuan Su, Soravit Changpinyo, Xiangning Chen, Sathish Thoppay, Cho-Jui Hsieh, Lior Shapira, Radu Soricut, Hartwig Adam, Matthew Brown, Ming-Hsuan Yang, Boqing Gong:
2.5D Visual Relationship Detection. CoRR abs/2104.12727 (2021) - [i117]Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, Jong-Seok Lee:
Deep Image Destruction: A Comprehensive Study on Vulnerability of Deep Image-to-Image Models against Adversarial Attacks. CoRR abs/2104.15022 (2021) - [i116]Yao Li, Tongyi Tang, Cho-Jui Hsieh, Thomas C. M. Lee:
Detecting Adversarial Examples with Bayesian Neural Network. CoRR abs/2105.08620 (2021) - [i115]Seungyeon Kim, Daniel Glasner, Srikumar Ramalingam, Cho-Jui Hsieh, Kishore Papineni, Sanjiv Kumar:
Balancing Robustness and Sensitivity using Feature Contrastive Learning. CoRR abs/2105.09394 (2021) - [i114]Yong Liu, Xiangning Chen, Minhao Cheng, Cho-Jui Hsieh, Yang You:
Concurrent Adversarial Learning for Large-Batch Training. CoRR abs/2106.00221 (2021) - [i113]