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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)
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2020 – today
- 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) - [c75]Han Zhao:
Fair and Optimal Prediction via Post-Processing. AAAI 2024: 22686 - [c74]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 - [c73]Guillaume Houry, Han Bao, Han Zhao, Makoto Yamada:
Fast 1-Wasserstein distance approximations using greedy strategies. AISTATS 2024: 325-333 - [c72]Ziyu Gong, Ben Usman, Han Zhao, David I. Inouye:
Towards Practical Non-Adversarial Distribution Matching. AISTATS 2024: 4276-4284 - [c71]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 - [c70]Yifei He, Haoxiang Wang, Ziyan Jiang, Alexandros Papangelis, Han Zhao:
Semi-Supervised Reward Modeling via Iterative Self-Training. EMNLP (Findings) 2024: 7365-7377 - [c69]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 - [c68]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 - [c67]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 - [c66]Shikun Liu, Deyu Zou, Han Zhao, Pan Li:
Pairwise Alignment Improves Graph Domain Adaptation. ICML 2024 - [c65]Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao:
Differentially Private Post-Processing for Fair Regression. ICML 2024 - [i77]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) - [i76]Haoxiang Wang, Haozhe Si, Huajie Shao, Han Zhao:
Enhancing Compositional Generalization via Compositional Feature Alignment. CoRR abs/2402.02851 (2024) - [i75]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) - [i74]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) - [i73]Shikun Liu, Deyu Zou, Han Zhao, Pan Li:
Pairwise Alignment Improves Graph Domain Adaptation. CoRR abs/2403.01092 (2024) - [i72]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) - [i71]Ruicheng Xian, Han Zhao:
Optimal Group Fair Classifiers from Linear Post-Processing. CoRR abs/2405.04025 (2024) - [i70]Ruicheng Xian, Qiaobo Li, Gautam Kamath, Han Zhao:
Differentially Private Post-Processing for Fair Regression. CoRR abs/2405.04034 (2024) - [i69]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) - [i68]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) - [i67]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) - [i66]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) - [i65]Yifei He, Haoxiang Wang, Ziyan Jiang, Alexandros Papangelis, Han Zhao:
Semi-Supervised Reward Modeling via Iterative Self-Training. CoRR abs/2409.06903 (2024) - [i64]Yuzheng Hu, Pingbang Hu, Han Zhao, Jiaqi W. Ma:
Most Influential Subset Selection: Challenges, Promises, and Beyond. CoRR abs/2409.18153 (2024) - [i63]Siqi Zeng, Sixian Du, Makoto Yamada, Han Zhao:
Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal Transport. CoRR abs/2410.03052 (2024) - [i62]Lang Yin, Han Zhao:
On the Expressive Power of Tree-Structured Probabilistic Circuits. CoRR abs/2410.05465 (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]