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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 — Shanghai Jiao Tong University, Shanghai, China
- Han Zhao 0006 — University of Florida, Gainesville, FL, USA
- Han Zhao 0007
![[0000-0003-1796-8395]](https://dblp.org/img/orcid-mark.12x12.png "0000-0003-1796-8395")
— Hefei University of Technology, School of Mechanical Engineering, China (and 1 more)
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2020 – today
- 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) - [c60]Seiyun Shin, Han Zhao, Ilan Shomorony:
Adaptive Power Method: Eigenvector Estimation from Sampled Data. ALT 2023: 1387-1410 - [c59]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 - [c58]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 - [c57]Siqi Zeng, Remi Tachet des Combes, Han Zhao:
Learning Structured Representations by Embedding Class Hierarchy. ICLR 2023 - [c56]Yuzheng Hu, Fan Wu, Hongyang Zhang, Han Zhao:
Understanding the Impact of Adversarial Robustness on Accuracy Disparity. ICML 2023: 13679-13709 - [c55]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 - [c54]Ruicheng Xian, Lang Yin, Han Zhao:
Fair and Optimal Classification via Post-Processing. ICML 2023: 37977-38012 - [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 - [i60]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) - [i59]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) - [i58]Chi Han, Ziqi Wang, Han Zhao, Heng Ji:
In-Context Learning of Large Language Models Explained as Kernel Regression. CoRR abs/2305.12766 (2023) - [i57]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) - [i56]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) - [i55]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) - [i54]Yong Lin, Lu Tan, Hangyu Lin, Zeming Zheng, Renjie Pi, Jipeng Zhang, Shizhe Diao, Haoxiang Wang, Han Zhao, Yuan Yao, Tong Zhang:
Speciality vs Generality: An Empirical Study on Catastrophic Forgetting in Fine-tuning Foundation Models. CoRR abs/2309.06256 (2023) - [i53]Zikun Chen, Han Zhao, Parham Aarabi, Ruowei Jiang:
SC2GAN: Rethinking Entanglement by Self-correcting Correlated GAN Space. CoRR abs/2310.06667 (2023) - [i52]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) - [i51]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) - [i50]Yifei He, Haoxiang Wang, Bo Li, Han Zhao:
Gradual Domain Adaptation: Theory and Algorithms. CoRR abs/2310.13852 (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,