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ORCIDHan Zhao 0002
赵晗
Person information
- unicode name: 赵晗
- affiliation: University of Illinois at Urbana-Champaign, Department of Computer Science, IL, USA
- affiliation (PhD 2020): Carnegie Mellon University, School of Computer Science, Machine Learning Department, Pittsburgh, PA, USA
- affiliation (former): University of Waterloo, Department of Computer Science, ON, Canada
- affiliation (former): University of Notre Dame, Department of Computer Science and Engineering, IN, USA
Other persons with the same name
- Han Zhao — disambiguation page
- Han Zhao 0001 — University of Florida, Department of Computer and Information Science and Engineering, Gainesville, FL, USA (and 1 more)
- Han Zhao 0003 — Northwestern Polytechnical University, Xi'an, China
- Han Zhao 0004 — Bournemouth University, Poole, UK
- Han Zhao 0005
![[0000-0002-1561-5329]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-1561-5329")
— Shanghai Jiao Tong University, Shanghai, China - Han Zhao 0006 — University of Florida, Gainesville, FL, USA
- Han Zhao 0007 — Hefei University of Technology, School of Mechanical Engineering, China (and 1 more)
- Han Zhao 0008 — Westlake University, Hangzhou, China (and 1 more)
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2020 – today
- 2025
[c83]Siqi Zeng, Sixian Du, Makoto Yamada, Han Zhao:
Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal Transport. ICLR 2025- [c82]Hongjue Zhao, Yuchen Wang, Hairong Qi, Zijie Huang, Han Zhao, Lui Sha, Huajie Shao:
Accelerating Neural ODEs: A Variational Formulation-based Approach. ICLR 2025 - [c81]Samuele Poppi, Zheng-Xin Yong, Yifei He, Bobbie Chern, Han Zhao, Aobo Yang, Jianfeng Chi:
Towards Understanding the Fragility of Multilingual LLMs against Fine-Tuning Attacks. NAACL (Findings) 2025: 2358-2372 - [i86]Weixin Chen, Simon Yu, Huajie Shao, Lui Sha, Han Zhao:
Neural Probabilistic Circuits: Enabling Compositional and Interpretable Predictions through Logical Reasoning. CoRR abs/2501.07021 (2025) - [i85]Weiyu Chen, Xiaoyuan Zhang, Baijiong Lin, Xi Lin, Han Zhao, Qingfu Zhang, James T. Kwok:
Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond. CoRR abs/2501.10945 (2025) - [i84]Siqi Zeng, Yifei He, Weiqiu You, Yifan Hao, Yao-Hung Hubert Tsai, Makoto Yamada, Han Zhao:
Efficient Model Editing with Task Vector Bases: A Theoretical Framework and Scalable Approach. CoRR abs/2502.01015 (2025) - [i83]Haozhe Si, Yuxuan Wan, Minh Do, Deepak Vasisht, Han Zhao, Hendrik F. Hamann:
Towards Scalable Foundation Model for Multi-modal and Hyperspectral Geospatial Data. CoRR abs/2503.12843 (2025) - 2024
- [j14]Han Zhao:
Fair and optimal prediction via post-processing. AI Mag. 45(3): 411-418 (2024) - [j13]Yifan Yang, Mingquan Lin, Han Zhao, Yifan Peng, Furong Huang, Zhiyong Lu:
A survey of recent methods for addressing AI fairness and bias in biomedicine. J. Biomed. Informatics 154: 104646 (2024) - [j12]Yifei He, Runxiang Cheng, Gargi Balasubramaniam, Yao-Hung Hubert Tsai, Han Zhao:
Efficient Modality Selection in Multimodal Learning. J. Mach. Learn. Res. 25: 47:1-47:39 (2024) - [j11]Jayanth Shenoy, Xingjian Davis Zhang, Bill Tao, Shlok Mehrotra, Rem Yang, Han Zhao, Deepak Vasisht:
Self-Supervised Learning across the Spectrum. Remote. Sens. 16(18): 3470 (2024) - [j10]Haoxiang Wang, Haozhe Si, Huajie Shao, Han Zhao:
Enhancing Compositional Generalization via Compositional Feature Alignment. Trans. Mach. Learn. Res. 2024 (2024) - [j9]Viet Duong, Qiong Wu, Zhengyi Zhou, Eric Zavesky, Wen-Ling Hsu, Han Zhao, Huajie Shao:
A General-Purpose Multi-Modal OOD Detection Framework. Trans. Mach. Learn. Res. 2024 (2024) - [j8]Xiaoyang Wang, Han Zhao, Klara Nahrstedt, Sanmi Koyejo:
Personalized Federated Learning with Spurious Features: An Adversarial Approach. Trans. Mach. Learn. Res. 2024 (2024) - [c80]Han Zhao:
Fair and Optimal Prediction via Post-Processing. AAAI 2024: 22686 - [c79]Haoxiang Wang, Yong Lin, Wei Xiong, Rui Yang, Shizhe Diao, Shuang Qiu, Han Zhao, Tong Zhang:
Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards. ACL (1) 2024: 8642-8655 - [c78]Guillaume Houry, Han Bao, Han Zhao, Makoto Yamada:
Fast 1-Wasserstein distance approximations using greedy strategies. AISTATS 2024: 325-333 - [c77]Ziyu Gong, Ben Usman, Han Zhao, David I. Inouye:
Towards Practical Non-Adversarial Distribution Matching. AISTATS 2024: 4276-4284 - [c76]Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, Tong Zhang:
Mitigating the Alignment Tax of RLHF. EMNLP 2024: 580-606 - [c75]Yifei He, Haoxiang Wang, Ziyan Jiang, Alexandros Papangelis, Han Zhao:
Semi-Supervised Reward Modeling via Iterative Self-Training. EMNLP (Findings) 2024: 7365-7377 - [c74]Haoxiang Wang, Wei Xiong, Tengyang Xie, Han Zhao, Tong Zhang:
Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts. EMNLP (Findings) 2024: 10582-10592 - [c73]Xiaotian Han, Jianfeng Chi, Yu Chen, Qifan Wang, Han Zhao, Na Zou, Xia Hu:
FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods. ICLR 2024 - [c72]Yifei He, Shiji Zhou, Guojun Zhang, Hyokun Yun, Yi Xu, Belinda Zeng, Trishul Chilimbi, Han Zhao:
Robust Multi-Task Learning with Excess Risks. ICML 2024 - [c71]Shikun Liu, Deyu Zou, Han Zhao, Pan Li:
Pairwise Alignment Improves Graph Domain Adaptation. ICML 2024 - [c70]Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao:
Differentially Private Post-Processing for Fair Regression. ICML 2024 - [c69]Yuzheng Hu, Pingbang Hu, Han Zhao, Jiaqi W. Ma:
Most Influential Subset Selection: Challenges, Promises, and Beyond. NeurIPS 2024 - [c68]Evelyn Ma, Chao Pan, S. Rasoul Etesami, Han Zhao, Olgica Milenkovic:
FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning. NeurIPS 2024 - [c67]Aditya Sinha, Siqi Zeng, Makoto Yamada, Han Zhao:
Learning Structured Representations with Hyperbolic Embeddings. NeurIPS 2024 - [c66]Lang Yin, Han Zhao:
On the Expressive Power of Tree-Structured Probabilistic Circuits. NeurIPS 2024 - [c65]Xiaoyuan Zhang, Liang Zhao, Yingying Yu, Xi Lin, Yifan Chen, Han Zhao, Qingfu Zhang:
LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch. NeurIPS 2024 - [i82]Yifei He, Shiji Zhou, Guojun Zhang, Hyokun Yun, Yi Xu, Belinda Zeng, Trishul Chilimbi, Han Zhao:
Robust Multi-Task Learning with Excess Risks. CoRR abs/2402.02009 (2024) - [i81]Haoxiang Wang, Haozhe Si, Huajie Shao, Han Zhao:
Enhancing Compositional Generalization via Compositional Feature Alignment. CoRR abs/2402.02851 (2024) - [i80]Yifan Yang, Mingquan Lin, Han Zhao, Yifan Peng, Furong Huang, Zhiyong Lu:
A survey of recent methods for addressing AI fairness and bias in biomedicine. CoRR abs/2402.08250 (2024) - [i79]Haoxiang Wang, Yong Lin, Wei Xiong, Rui Yang, Shizhe Diao, Shuang Qiu, Han Zhao, Tong Zhang:
Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards. CoRR abs/2402.18571 (2024) - [i78]Shikun Liu, Deyu Zou, Han Zhao, Pan Li:
Pairwise Alignment Improves Graph Domain Adaptation. CoRR abs/2403.01092 (2024) - [i77]Jayanth Shenoy, Xingjian Davis Zhang, Shlok Mehrotra, Bill Tao, Rem Yang, Han Zhao, Deepak Vasisht:
S4: Self-Supervised Sensing Across the Spectrum. CoRR abs/2405.01656 (2024) - [i76]Ruicheng Xian, Han Zhao:
Optimal Group Fair Classifiers from Linear Post-Processing. CoRR abs/2405.04025 (2024) - [i75]Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao:
Differentially Private Post-Processing for Fair Regression. CoRR abs/2405.04034 (2024) - [i74]Hanze Dong, Wei Xiong, Bo Pang, Haoxiang Wang, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang:
RLHF Workflow: From Reward Modeling to Online RLHF. CoRR abs/2405.07863 (2024) - [i73]Haoxiang Wang, Wei Xiong, Tengyang Xie, Han Zhao, Tong Zhang:
Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts. CoRR abs/2406.12845 (2024) - [i72]Yifei He, Yuzheng Hu, Yong Lin, Tong Zhang, Han Zhao:
Localize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic. CoRR abs/2408.13656 (2024) - [i71]Xiaoyuan Zhang, Liang Zhao, Yingying Yu, Xi Lin, Zhenkun Wang, Han Zhao, Qingfu Zhang:
LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch. CoRR abs/2409.02969 (2024) - [i70]Yifei He, Haoxiang Wang, Ziyan Jiang, Alexandros Papangelis, Han Zhao:
Semi-Supervised Reward Modeling via Iterative Self-Training. CoRR abs/2409.06903 (2024) - [i69]Yuzheng Hu, Pingbang Hu, Han Zhao, Jiaqi W. Ma:
Most Influential Subset Selection: Challenges, Promises, and Beyond. CoRR abs/2409.18153 (2024) - [i68]Siqi Zeng, Sixian Du, Makoto Yamada, Han Zhao:
Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal Transport. CoRR abs/2410.03052 (2024) - [i67]Lang Yin, Han Zhao:
On the Expressive Power of Tree-Structured Probabilistic Circuits. CoRR abs/2410.05465 (2024) - [i66]Yifei He, Alon Benhaim, Barun Patra, Praneetha Vaddamanu, Sanchit Ahuja, Parul Chopra, Vishrav Chaudhary, Han Zhao, Xia Song:
Scaling Laws for Multilingual Language Models. CoRR abs/2410.12883 (2024) - [i65]Evelyn Ma, Chao Pan, S. Rasoul Etesami, Han Zhao, Olgica Milenkovic:
FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning. CoRR abs/2410.13045 (2024) - [i64]Samuele Poppi, Zheng-Xin Yong, Yifei He, Bobbie Chern, Han Zhao, Aobo Yang, Jianfeng Chi:
Towards Understanding the Fragility of Multilingual LLMs against Fine-Tuning Attacks. CoRR abs/2410.18210 (2024) - [i63]Deming Chen, Alaa Youssef, Ruchi Pendse, André Schleife, Bryan K. Clark, Hendrik F. Hamann, Jingrui He, Teodoro Laino, Lav R. Varshney, Yuxiong Wang, Avirup Sil, Reyhaneh Jabbarvand, Tianyin Xu, Volodymyr V. Kindratenko, Carlos H. A. Costa, Sarita V. Adve, Charith Mendis, Minjia Zhang, Santiago Núñez-Corrales, Raghu K. Ganti, Mudhakar Srivatsa, Nam Sung Kim, Josep Torrellas, Jian Huang, Seetharami R. Seelam, Klara Nahrstedt, Tarek F. Abdelzaher, Tamar Eilam, Huimin Zhao, Matteo Manica, Ravishankar K. Iyer, Martin Hirzel, Vikram S. Adve, Darko Marinov, Hubertus Franke, Hanghang Tong, Elizabeth A. Ainsworth, Han Zhao, Deepak Vasisht, Minh Do, Fábio Oliveira, Giovanni Pacifici, Ruchir Puri, Priya Nagpurkar:
Transforming the Hybrid Cloud for Emerging AI Workloads. CoRR abs/2411.13239 (2024) - [i62]Aditya Sinha, Siqi Zeng, Makoto Yamada, Han Zhao:
Learning Structured Representations with Hyperbolic Embeddings. CoRR abs/2412.01023 (2024) - 2023
- [j7]Jian Shen, Hang Lai, Minghuan Liu, Han Zhao, Yong Yu, Weinan Zhang:
Adaptation Augmented Model-based Policy Optimization. J. Mach. Learn. Res. 24: 218:1-218:35 (2023) - [j6]Han Zhao:
Costs and Benefits of Fair Regression. Trans. Mach. Learn. Res. 2023 (2023) - [c64]Seiyun Shin, Han Zhao, Ilan Shomorony:
Adaptive Power Method: Eigenvector Estimation from Sampled Data. ALT 2023: 1387-1410 - [c63]Yan Shen, Jian Du, Han Zhao, Zhanghexuan Ji, Chunwei Ma, Mingchen Gao:
FedMM: A Communication Efficient Solver for Federated Adversarial Domain Adaptation. AAMAS 2023: 1808-1816 - [c62]Qian Jiang, Changyou Chen, Han Zhao, Liqun Chen, Qing Ping, Son Dinh Tran, Yi Xu, Belinda Zeng, Trishul Chilimbi:
Understanding and Constructing Latent Modality Structures in Multi-Modal Representation Learning. CVPR 2023: 7661-7671 - [c61]Zikun Chen, Han Zhao, Parham Aarabi, Ruowei Jiang:
SC2GAN: Rethinking Entanglement by Self-correcting Correlated GAN Space. ICCV (Workshops) 2023: 4459-4468 - [c60]Siqi Zeng, Remi Tachet des Combes, Han Zhao:
Learning Structured Representations by Embedding Class Hierarchy. ICLR 2023 - [c59]Yuzheng Hu, Fan Wu, Hongyang Zhang, Han Zhao:
Understanding the Impact of Adversarial Robustness on Accuracy Disparity. ICML 2023: 13679-13709 - [c58]Shikun Liu, Tianchun Li, Yongbin Feng, Nhan Tran, Han Zhao, Qiang Qiu, Pan Li:
Structural Re-weighting Improves Graph Domain Adaptation. ICML 2023: 21778-21793 - [c57]Ruicheng Xian, Lang Yin, Han Zhao:
Fair and Optimal Classification via Post-Processing. ICML 2023: 37977-38012 - [c56]Yuzheng Hu, Ruicheng Xian, Qilong Wu, Qiuling Fan, Lang Yin, Han Zhao:
Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective. NeurIPS 2023 - [c55]Seiyun Shin, Ilan Shomorony, Han Zhao:
Efficient Learning of Linear Graph Neural Networks via Node Subsampling. NeurIPS 2023 - [c54]Ruicheng Xian, Honglei Zhuang, Zhen Qin, Hamed Zamani, Jing Lu, Ji Ma, Kai Hui, Han Zhao, Xuanhui Wang, Michael Bendersky:
Learning List-Level Domain-Invariant Representations for Ranking. NeurIPS 2023 - [c53]Costas Mavromatis, Vassilis N. Ioannidis, Shen Wang, Da Zheng, Soji Adeshina, Jun Ma, Han Zhao, Christos Faloutsos, George Karypis:
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs. ECML/PKDD (3) 2023: 157-173 - [i61]Qian Jiang, Changyou Chen, Han Zhao, Liqun Chen, Qing Ping, Son Dinh Tran, Yi Xu, Belinda Zeng, Trishul Chilimbi:
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning. CoRR abs/2303.05952 (2023) - [i60]Costas Mavromatis, Vassilis N. Ioannidis, Shen Wang, Da Zheng, Soji Adeshina, Jun Ma, Han Zhao, Christos Faloutsos, George Karypis:
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs. CoRR abs/2304.10668 (2023) - [i59]Chi Han, Ziqi Wang, Han Zhao, Heng Ji:
In-Context Learning of Large Language Models Explained as Kernel Regression. CoRR abs/2305.12766 (2023) - [i58]Shikun Liu, Tianchun Li, Yongbin Feng, Nhan Tran, Han Zhao, Qiu Qiang, Pan Li:
Structural Re-weighting Improves Graph Domain Adaptation. CoRR abs/2306.03221 (2023) - [i57]Xiaotian Han, Jianfeng Chi, Yu Chen, Qifan Wang, Han Zhao, Na Zou, Xia Hu:
FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods. CoRR abs/2306.09468 (2023) - [i56]Yuzheng Hu, Ruicheng Xian, Qilong Wu, Qiuling Fan, Lang Yin, Han Zhao:
Revisiting Scalarization in Multi-Task Learning: A Theoretical Perspective. CoRR abs/2308.13985 (2023) - [i55]Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Yuan Yao, Tong Zhang:
Mitigating the Alignment Tax of RLHF. CoRR abs/2309.06256 (2023) - [i54]Zikun Chen, Han Zhao, Parham Aarabi, Ruowei Jiang:
SC2GAN: Rethinking Entanglement by Self-correcting Correlated GAN Space. CoRR abs/2310.06667 (2023) - [i53]Bowen Jin, Wentao Zhang, Yu Zhang, Yu Meng, Han Zhao, Jiawei Han:
Learning Multiplex Embeddings on Text-rich Networks with One Text Encoder. CoRR abs/2310.06684 (2023) - [i52]Makoto Yamada, Yuki Takezawa, Guillaume Houry, Kira Michaela Dusterwald, Déborah Sulem, Han Zhao, Yao-Hung Hubert Tsai:
An Empirical Study of Simplicial Representation Learning with Wasserstein Distance. CoRR abs/2310.10143 (2023) - [i51]Yifei He, Haoxiang Wang, Bo Li, Han Zhao:
Gradual Domain Adaptation: Theory and Algorithms. CoRR abs/2310.13852 (2023) - [i50]Ziyu Gong, Ben Usman, Han Zhao, David I. Inouye:
Towards Practical Non-Adversarial Distribution Alignment via Variational Bounds. CoRR abs/2310.19690 (2023) - [i49]Haoxiang Wang, Gargi Balasubramaniam, Haozhe Si, Bo Li, Han Zhao:
Invariant-Feature Subspace Recovery: A New Class of Provable Domain Generalization Algorithms. CoRR abs/2311.00966 (2023) - 2022
- [j5]Han Zhao, Geoffrey J. Gordon:
Inherent Tradeoffs in Learning Fair Representations. J. Mach. Learn. Res. 23: 57:1-57:26 (2022) - [j4]Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar:
Fundamental Limits and Tradeoffs in Invariant Representation Learning. J. Mach. Learn. Res. 23: 340:1-340:49 (2022) - [j3]Jing Dong, Shiji Zhou, Baoxiang Wang, Han Zhao:
Algorithms and Theory for Supervised Gradual Domain Adaptation. Trans. Mach. Learn. Res. 2022 (2022) - [j2]Sicheng Zhao, Xiangyu Yue, Shanghang Zhang, Bo Li, Han Zhao, Bichen Wu, Ravi Krishna, Joseph E. Gonzalez, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia, Kurt Keutzer:
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation. IEEE Trans. Neural Networks Learn. Syst. 33(2): 473-493 (2022) - [c52]Bo Li, Yifei Shen, Yezhen Wang, Wenzhen Zhu, Colorado Reed, Dongsheng Li, Kurt Keutzer, Han Zhao:
Invariant Information Bottleneck for Domain Generalization. AAAI 2022: 7399-7407 - [c51]Jianfeng Chi, Jian Shen, Xinyi Dai, Weinan Zhang, Yuan Tian, Han Zhao:
Towards Return Parity in Markov Decision Processes. AISTATS 2022: 1161-1178 - [c50]Shiji Zhou, Han Zhao, Shanghang Zhang, Lianzhe Wang, Heng Chang, Zhi Wang, Wenwu Zhu:
Online Continual Adaptation with Active Self-Training. AISTATS 2022: 8852-8883 - [c49]Huajie Shao, Yifei Yang, Haohong Lin, Longzhong Lin, Yizhuo Chen, Qinmin Yang, Han Zhao:
Rethinking Controllable Variational Autoencoders. CVPR 2022: 19228-19237 - [c48]Zikun Chen, Ruowei Jiang, Brendan Duke, Han Zhao, Parham Aarabi:
Exploring Gradient-Based Multi-directional Controls in GANs. ECCV (23) 2022: 104-119 - [c47]Jianfeng Chi, William Shand, Yaodong Yu, Kai-Wei Chang, Han Zhao, Yuan Tian:
Conditional Supervised Contrastive Learning for Fair Text Classification. EMNLP (Findings) 2022: 2736-2756 - [c46]Yao-Hung Hubert Tsai, Tianqin Li, Martin Q. Ma, Han Zhao, Kun Zhang, Louis-Philippe Morency, Ruslan Salakhutdinov:
Conditional Contrastive Learning with Kernel. ICLR 2022 - [c45]Ruicheng Xian, Heng Ji, Han Zhao:
Cross-Lingual Transfer with Class-Weighted Language-Invariant Representations. ICLR 2022 - [c44]Haoxiang Wang, Bo Li, Han Zhao:
Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond. ICML 2022: 22784-22801 - [c43]Haoxiang Wang, Haozhe Si, Bo Li, Han Zhao:
Provable Domain Generalization via Invariant-Feature Subspace Recovery. ICML 2022: 23018-23033 - [c42]Runxiang Cheng, Gargi Balasubramaniam, Yifei He, Yao-Hung Hubert Tsai, Han Zhao:
Greedy modality selection via approximate submodular maximization. UAI 2022: 389-399 - [i48]Haoxiang Wang, Haozhe Si, Bo Li, Han Zhao:
Provable Domain Generalization via Invariant-Feature Subspace Recovery. CoRR abs/2201.12919 (2022) - [i47]Yao-Hung Hubert Tsai, Tianqin Li, Martin Q. Ma, Han Zhao, Kun Zhang, Louis-Philippe Morency, Ruslan Salakhutdinov:
Conditional Contrastive Learning with Kernel. CoRR abs/2202.05458 (2022) - [i46]Zhenhailong Wang, Hang Yu, Manling Li, Han Zhao, Heng Ji:
Model-Agnostic Multitask Fine-tuning for Few-shot Vision-Language Transfer Learning. CoRR abs/2203.04904 (2022) - [i45]Haoxiang Wang, Bo Li, Han Zhao:
Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond. CoRR abs/2204.08200 (2022) - [i44]Jing Dong, Shiji Zhou, Baoxiang Wang, Han Zhao:
Algorithms and Theory for Supervised Gradual Domain Adaptation. CoRR abs/2204.11644 (2022) - [i43]Jianfeng Chi, William Shand, Yaodong Yu, Kai-Wei Chang, Han Zhao, Yuan Tian:
Conditional Supervised Contrastive Learning for Fair Text Classification. CoRR abs/2205.11485 (2022) - [i42]Wenda Chu, Chulin Xie, Boxin Wang, Linyi Li, Lang Yin, Han Zhao, Bo Li:
FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data. CoRR abs/2207.10265 (2022) - [i41]Zikun Chen, Ruowei Jiang, Brendan Duke, Han Zhao, Parham Aarabi:
Exploring Gradient-based Multi-directional Controls in GANs. CoRR abs/2209.00698 (2022) - [i40]Runxiang Cheng, Gargi Balasubramaniam, Yifei He, Yao-Hung Hubert Tsai, Han Zhao:
Greedy Modality Selection via Approximate Submodular Maximization. CoRR abs/2210.12562 (2022) - [i39]Ruicheng Xian, Lang Yin, Han Zhao:
Fair and Optimal Classification via Transports to Wasserstein-Barycenter. CoRR abs/2211.01528 (2022) - [i38]Yuzheng Hu, Fan Wu, Hongyang Zhang, Han Zhao:
Understanding the Impact of Adversarial Robustness on Accuracy Disparity. CoRR abs/2211.15762 (2022) - [i37]Ruicheng Xian, Honglei Zhuang, Zhen Qin, Hamed Zamani, Jing Lu, Ji Ma, Kai Hui, Han Zhao, Xuanhui Wang, Michael Bendersky:
Learning List-Level Domain-Invariant Representations for Ranking. CoRR abs/2212.10764 (2022) - 2021
- [b1]Han Zhao:
Towards a Unified Framework for Learning and Reasoning. Carnegie Mellon University, USA, 2021 - [c41]Bo Li, Yezhen Wang, Shanghang Zhang, Dongsheng Li, Kurt Keutzer, Trevor Darrell, Han Zhao:
Learning Invariant Representations and Risks for Semi-Supervised Domain Adaptation. CVPR 2021: 1104-1113 - [c40]Zixuan Zhang, Hongwei Wang, Han Zhao, Hanghang Tong, Heng Ji:
EventKE: Event-Enhanced Knowledge Graph Embedding. EMNLP (Findings) 2021: 1389-1400 - [c39]Peizhao Li, Yifei Wang, Han Zhao, Pengyu Hong, Hongfu Liu:
On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections. ICLR 2021 - [c38]Yao-Hung Hubert Tsai, Martin Q. Ma, Muqiao Yang, Han Zhao, Louis-Philippe Morency, Ruslan Salakhutdinov:
Self-supervised Representation Learning with Relative Predictive Coding. ICLR 2021 - [c37]Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon, Han Zhao:
Understanding and Mitigating Accuracy Disparity in Regression. ICML 2021: 1866-1876 - [c36]Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi S. Jaakkola, Geoffrey J. Gordon, Stefanie Jegelka, Ruslan Salakhutdinov:
Information Obfuscation of Graph Neural Networks. ICML 2021: 6600-6610 - [c35]Haoxiang Wang, Han Zhao, Bo Li:
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation. ICML 2021: 10991-11002 - [c34]Wen Wang, Han Zhao, Dokyun Lee, George H. Chen:
Machine Learning for Consumers and Markets. KDD 2021: 4165-4166 - [c33]Guojun Zhang, Han Zhao, Yaoliang Yu, Pascal Poupart:
Quantifying and Improving Transferability in Domain Generalization. NeurIPS 2021: 10957-10970 - [i36]Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon, Han Zhao:
Understanding and Mitigating Accuracy Disparity in Regression. CoRR abs/2102.12013 (2021) - [i35]Yao-Hung Hubert Tsai, Martin Q. Ma, Muqiao Yang, Han Zhao, Louis-Philippe Morency, Ruslan Salakhutdinov:
Self-supervised Representation Learning with Relative Predictive Coding. CoRR abs/2103.11275 (2021) - [i34]Yao-Hung Hubert Tsai, Martin Q. Ma, Han Zhao, Kun Zhang, Louis-Philippe Morency, Ruslan Salakhutdinov:
Conditional Contrastive Learning: Removing Undesirable Information in Self-Supervised Representations. CoRR abs/2106.02866 (2021) - [i33]Guojun Zhang, Han Zhao, Yaoliang Yu, Pascal Poupart:
Quantifying and Improving Transferability in Domain Generalization. CoRR abs/2106.03632 (2021) - [i32]Bo Li, Yifei Shen, Yezhen Wang, Wenzhen Zhu, Colorado J. Reed, Jun Zhang, Dongsheng Li, Kurt Keutzer, Han Zhao:
Invariant Information Bottleneck for Domain Generalization. CoRR abs/2106.06333 (2021) - [i31]Shiji Zhou, Han Zhao, Shanghang Zhang, Lianzhe Wang, Heng Chang, Zhi Wang, Wenwu Zhu:
Online Continual Adaptation with Active Self-Training. CoRR abs/2106.06526 (2021) - [i30]Han Zhao:
Costs and Benefits of Wasserstein Fair Regression. CoRR abs/2106.08812 (2021) - [i29]Haoxiang Wang, Han Zhao, Bo Li:
Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation. CoRR abs/2106.09017 (2021) - [i28]Jianfeng Chi, Jian Shen, Xinyi Dai, Weinan Zhang, Yuan Tian, Han Zhao:
Towards Return Parity in Markov Decision Processes. CoRR abs/2111.10476 (2021) - 2020
- [c32]Peizhao Li, Han Zhao, Hongfu Liu:
Deep Fair Clustering for Visual Learning. CVPR 2020: 9067-9076 - [c31]Han Zhao, Amanda Coston, Tameem Adel, Geoffrey J. Gordon:
Conditional Learning of Fair Representations. ICLR 2020 - [c30]Tameem Adel, Han Zhao, Richard E. Turner:
Continual Learning with Adaptive Weights (CLAW). ICLR 2020 - [c29]Han Zhao, Junjie Hu, Andrej Risteski:
On Learning Language-Invariant Representations for Universal Machine Translation. ICML 2020: 11352-11364 - [c28]Han Zhao, Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon:
Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation. NeurIPS 2020 - [c27]Jian Shen, Han Zhao, Weinan Zhang, Yong Yu:
Model-based Policy Optimization with Unsupervised Model Adaptation. NeurIPS 2020 - [c26]Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, Geoffrey J. Gordon:
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift. NeurIPS 2020 - [c25]Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada, Louis-Philippe Morency, Ruslan Salakhutdinov:
Neural Methods for Point-wise Dependency Estimation. NeurIPS 2020 - [c24]Wen Wang, Han Zhao, Honglei Zhuang, Nirav Shah, Rema Padman:
DyCRS: Dynamic Interpretable Postoperative Complication Risk Scoring. WWW 2020: 1839-1850 - [i27]Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, Geoffrey J. Gordon:
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift. CoRR abs/2003.04475 (2020) - [i26]Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada, Louis-Philippe Morency, Ruslan Salakhutdinov:
Neural Methods for Point-wise Dependency Estimation. CoRR abs/2006.05553 (2020) - [i25]Han Zhao, Junjie Hu, Andrej Risteski:
On Learning Language-Invariant Representations for Universal Machine Translation. CoRR abs/2008.04510 (2020) - [i24]Sicheng Zhao, Xiangyu Yue, Shanghang Zhang, Bo Li, Han Zhao, Bichen Wu, Ravi Krishna, Joseph E. Gonzalez, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia, Kurt Keutzer:
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation. CoRR abs/2009.00155 (2020) - [i23]Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi S. Jaakkola, Geoffrey J. Gordon, Stefanie Jegelka, Ruslan Salakhutdinov:
Graph Adversarial Networks: Protecting Information against Adversarial Attacks. CoRR abs/2009.13504 (2020) - [i22]Bo Li, Yezhen Wang, Shanghang Zhang, Dongsheng Li, Trevor Darrell, Kurt Keutzer, Han Zhao:
Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation. CoRR abs/2010.04647 (2020) - [i21]Jian Shen, Han Zhao, Weinan Zhang, Yong Yu:
Model-based Policy Optimization with Unsupervised Model Adaptation. CoRR abs/2010.09546 (2020) - [i20]Han Zhao, Chen Dan, Bryon Aragam, Tommi S. Jaakkola, Geoffrey J. Gordon, Pradeep Ravikumar:
Fundamental Limits and Tradeoffs in Invariant Representation Learning. CoRR abs/2012.10713 (2020)
2010 – 2019
- 2019
- [c23]Han Zhao, Junjie Hu, Zhenyao Zhu, Adam Coates, Geoffrey J. Gordon:
Deep Generative and Discriminative Domain Adaptation. AAMAS 2019: 2315-2317 - [c22]Chen Liang, Jianbo Ye, Han Zhao, Bart Pursel, C. Lee Giles:
Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations. EDM 2019 - [c21]Han Zhao, Remi Tachet des Combes, Kun Zhang, Geoffrey J. Gordon:
On Learning Invariant Representations for Domain Adaptation. ICML 2019: 7523-7532 - [c20]Yichong Xu, Han Zhao, Xiaofei Shi, Nihar B. Shah:
On Strategyproof Conference Peer Review. IJCAI 2019: 616-622 - [c19]Han Zhao, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov, Geoffrey J. Gordon:
Learning Neural Networks with Adaptive Regularization. NeurIPS 2019: 11389-11400 - [c18]Han Zhao, Geoffrey J. Gordon:
Inherent Tradeoffs in Learning Fair Representations. NeurIPS 2019: 15649-15659 - [c17]Han Zhao, Otilia Stretcu, Alexander J. Smola, Geoffrey J. Gordon:
Efficient Multitask Feature and Relationship Learning. UAI 2019: 777-787 - [i19]Han Zhao, Remi Tachet des Combes, Kun Zhang, Geoffrey J. Gordon:
On Learning Invariant Representation for Domain Adaptation. CoRR abs/1901.09453 (2019) - [i18]Han Zhao, Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon:
Adversarial Task-Specific Privacy Preservation under Attribute Attack. CoRR abs/1906.07902 (2019) - [i17]Han Zhao, Geoffrey J. Gordon:
Inherent Tradeoffs in Learning Fair Representation. CoRR abs/1906.08386 (2019) - [i16]Han Zhao, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov, Geoffrey J. Gordon:
Learning Neural Networks with Adaptive Regularization. CoRR abs/1907.06288 (2019) - [i15]Han Zhao, Amanda Coston, Tameem Adel, Geoffrey J. Gordon:
Conditional Learning of Fair Representations. CoRR abs/1910.07162 (2019) - [i14]Tameem Adel, Han Zhao, Richard E. Turner:
Continual Learning with Adaptive Weights (CLAW). CoRR abs/1911.09514 (2019) - 2018
- [c16]Han Zhao, Shuayb Zarar, Ivan Tashev, Chin-Hui Lee:
Convolutional-Recurrent Neural Networks for Speech Enhancement. ICASSP 2018: 2401-2405 - [c15]Han Zhao, Shanghang Zhang, Guanhang Wu, João Paulo Costeira, José M. F. Moura, Geoffrey J. Gordon:
Multiple Source Domain Adaptation with Adversarial Learning. ICLR (Workshop) 2018 - [c14]Han Zhao, Shanghang Zhang, Guanhang Wu, José M. F. Moura, João Paulo Costeira, Geoffrey J. Gordon:
Adversarial Multiple Source Domain Adaptation. NeurIPS 2018: 8568-8579 - [c13]Han Zhao, Geoffrey J. Gordon:
Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint. UAI 2018: 124-134 - [i13]Chen Liang, Jianbo Ye, Han Zhao, Bart Pursel, C. Lee Giles:
Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations. CoRR abs/1801.06481 (2018) - [i12]Han Zhao, Shuayb Zarar, Ivan Tashev, Chin-Hui Lee:
Convolutional-Recurrent Neural Networks for Speech Enhancement. CoRR abs/1805.00579 (2018) - [i11]Yichong Xu, Han Zhao, Xiaofei Shi, Nihar B. Shah:
On Strategyproof Conference Peer Review. CoRR abs/1806.06266 (2018) - 2017
- [c12]Tameem Adel, Han Zhao, Alexander Wong:
Unsupervised Domain Adaptation with a Relaxed Covariate Shift Assumption. AAAI 2017: 1691-1697 - [c11]Han Zhao, Geoffrey J. Gordon:
Linear Time Computation of Moments in Sum-Product Networks. NIPS 2017: 6894-6903 - [i10]Han Zhao, Otilia Stretcu, Renato Negrinho, Alexander J. Smola, Geoffrey J. Gordon:
Efficient Multi-task Feature and Relationship Learning. CoRR abs/1702.04423 (2017) - [i9]Han Zhao, Geoffrey J. Gordon:
Efficient Computation of Moments in Sum-Product Networks. CoRR abs/1702.04767 (2017) - [i8]Han Zhao, Zhenyao Zhu, Junjie Hu, Adam Coates, Geoffrey J. Gordon:
Principled Hybrids of Generative and Discriminative Domain Adaptation. CoRR abs/1705.09011 (2017) - [i7]Han Zhao, Shanghang Zhang, Guanhang Wu, João Paulo Costeira, José M. F. Moura, Geoffrey J. Gordon:
Multiple Source Domain Adaptation with Adversarial Training of Neural Networks. CoRR abs/1705.09684 (2017) - [i6]Han Zhao, Geoffrey J. Gordon:
Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint. CoRR abs/1706.06348 (2017) - [i5]Yao-Hung Hubert Tsai, Han Zhao, Ruslan Salakhutdinov, Nebojsa Jojic:
Discovering Order in Unordered Datasets: Generative Markov Networks. CoRR abs/1711.03167 (2017) - 2016
- [c10]Abdullah Rashwan, Han Zhao, Pascal Poupart:
Online and Distributed Bayesian Moment Matching for Parameter Learning in Sum-Product Networks. AISTATS 2016: 1469-1477 - [c9]Han Zhao, Tameem Adel, Geoffrey J. Gordon, Brandon Amos:
Collapsed Variational Inference for Sum-Product Networks. ICML 2016: 1310-1318 - [c8]Han Zhao, Pascal Poupart, Geoffrey J. Gordon:
A Unified Approach for Learning the Parameters of Sum-Product Networks. NIPS 2016: 433-441 - [c7]Priyank Jaini, Abdullah Rashwan, Han Zhao, Yue Liu, Ershad Banijamali, Zhitang Chen, Pascal Poupart:
Online Algorithms for Sum-Product Networks with Continuous Variables. Probabilistic Graphical Models 2016: 228-239 - [i4]Han Zhao, Pascal Poupart, Geoffrey J. Gordon:
A Unified Approach for Learning the Parameters of Sum-Product Networks. CoRR abs/1601.00318 (2016) - 2015
- [j1]Fazle Elahi Faisal, Han Zhao, Tijana Milenkovic:
Global Network Alignment in the Context of Aging. IEEE ACM Trans. Comput. Biol. Bioinform. 12(1): 40-52 (2015) - [c6]Han Zhao, Pascal Poupart, Yongfeng Zhang, Martin Lysy:
SoF: Soft-Cluster Matrix Factorization for Probabilistic Clustering. AAAI 2015: 3188-3195 - [c5]Han Zhao, Mazen Melibari, Pascal Poupart:
On the Relationship between Sum-Product Networks and Bayesian Networks. ICML 2015: 116-124 - [c4]Han Zhao, Zhengdong Lu, Pascal Poupart:
Self-Adaptive Hierarchical Sentence Model. IJCAI 2015: 4069-4076 - [i3]Han Zhao, Mazen Melibari, Pascal Poupart:
On the Relationship between Sum-Product Networks and Bayesian Networks. CoRR abs/1501.01239 (2015) - [i2]Han Zhao, Zhengdong Lu, Pascal Poupart:
Self-Adaptive Hierarchical Sentence Model. CoRR abs/1504.05070 (2015) - 2014
- [c3]Fazle Elahi Faisal, Han Zhao, Tijana Milenkovic:
Global network alignment in the context of aging. BCB 2014: 580 - [i1]Han Zhao, Pascal Poupart:
A Sober Look at Spectral Learning. CoRR abs/1406.4631 (2014) - 2013
- [c2]Tijana Milenkovic, Han Zhao, Fazle Elahi Faisal:
Global Network Alignment In The Context Of Aging. BCB 2013: 23 - [c1]Tijana Milenkovic, Han Zhao, Fazle Elahi Faisal:
Using Global Network Alignment In The Context Of Aging. BCB 2013: 661
Coauthor Index
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