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Kun Zhang 0001
Person information

- affiliation: Carnegie Mellon University, Department of Philosophy, Pittsburgh, PA, USA
- affiliation: Max Planck Institute for Intelligent Systems, Tübingen, Germany
- affiliation (PhD 2005): Chinese University of Hong Kong, Hong Kong
Other persons with the same name
- Kun Zhang — disambiguation page
- Kun Zhang 0002
— Taiyuan University of Technology, College of Mechanical Engineering, China
- Kun Zhang 0003
— East China Normal University, Key Laboratory of Geographic Information Science, Shanghai, China
- Kun Zhang 0004
— State University of New York at Stony Brook, Department of Physics and Astronomy, NY, USA
- Kun Zhang 0005
— Northeastern University, College of Information Science and Engineering, Shenyang, China
- Kun Zhang 0006 — Georgia Institute of Technology, Atlanta, GA, USA
- Kun Zhang 0007
— Beijing University of Technology, Key Laboratory of Advanced Manufacturing Technology, China
- Kun Zhang 0008
— Air Force Engineering University, Information and Navigation College, Xian, China
- Kun Zhang 0009
— Xidian University, National Laboratory of Radar Signal Processing, China
- Kun Zhang 0010
— Nantong University, School of Electrical Engineering, China
- Kun Zhang 0011
— Hainan University, State Key Laboratory of Marine Resource Utilization in South China Sea, Haikou, China
- Kun Zhang 0012
— Xavier University of Louisiana, New Orleans, LA, USA
- Kun Zhang 0013 — Shandong University, Jinan, China (and 1 more)
- Kun Zhang 0014
— University of Colorado at Boulder, Boulder, CO, USA
- Kun Zhang 0015
— Hefei University of Technology, School of Computer Science and Information Engineering, Key Laboratory of Knowledge Engineering with Big Data, Hefei, China (and 1 more)
- Kun Zhang 0016
— University of Chinese Academy of Sciences, School of Cyber Security, Beijing, China
- Kun Zhang 0017
— Hong Kong University of Science and Technology, Hong Kong (and 1 more)
- Kun Zhang 0018
— Northwestern Polytechnical University, School of Astronautics, Shaanxi Aerospace Flight Vehicle Design Key Laboratory, Xi'an, China
- Kun Zhang 0019
— Southeast University, School of Economics and Management, Nanjing, China
- Kun Zhang 0020 — University of California San Diego, Department of Bioengineering, La Jolla, CA, USA
- Kun Zhang 0021
— Nanjing University of Science and Technology, School of Computer Science and Engineering, China
- Kun Zhang 0022
— Shenyang University of Technology, School of Materials Science and Engineering, China
- Kun Zhang 0023
— Shaanxi Provincial Tumor Hospital, Xi'an, China
- Kun Zhang 0024
— Chinese Academy of Sciences, Institute of Tibetan Plateau Research, Beijing, China (and 1 more)
- Kun Zhang 0025
— University of Science and Technology of China, CAS Key Laboratory of Wireless-Optical Communications, Hefei, China
- Kun Zhang 0026
— Shandong University of Science and Technology, College of Mechanical and Electronic Engineering, Qingdao, China (and 2 more)
- Kun Zhang 0027
— Huaqiao University, College of Tourism, Quanzhou, China
- Kun Zhang 0028 — University of Science and Technology of China, Department of Automation, Hefei, China
- Kun Zhang 0029 — Northwestern Polytechnical University, School of Electronics and Information, Xi'an, China
- Kun Zhang 0030
— Beihang University, Fert Beijing Research Institute, Beijing, China (and 2 more)
- Kun Zhang 0031
— Central China Normal University, National Engineering Laboratory for Educational Big Data and the National Engineering Research Center for E-Learning, Wuhan, China
- Kun Zhang 0032
— Luoyang Polytechnic, School of Automotive and Rail Transportation, Luoyang, China
- Kun Zhang 0033
— South China University of Technology, School of Electronics and Information Engineering, Guangzhou, China
- Kun Zhang 0034
— Xi'an Peihua University, School of Communication, Xi'an, China
- Kun Zhang 0035
— Nanjing University of Science and Technology, School of Computer Science and Engineering, Nanjing, China
- Kun Zhang 0036
— Renmin University of China, Institute of Statistics and Big Data, Beijing, China (and 1 more)
- Kun Zhang 0037
— Zhejiang Lab, Hangzhou, China
- Kun Zhang 0038
— Shandong University of Science and Technology, College of Intelligent Equipment, Tai'an, China
- Kun Zhang 0039
— Liaoning University, School of Mathematics, Shenyang, China
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2020 – today
- 2023
- [j25]Feng Xie, Yan Zeng, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang:
Causal discovery of 1-factor measurement models in linear latent variable models with arbitrary noise distributions. Neurocomputing 526: 48-61 (2023) - [j24]Yuewen Sun
, Kun Zhang, Changyin Sun
:
Model-Based Transfer Reinforcement Learning Based on Graphical Model Representations. IEEE Trans. Neural Networks Learn. Syst. 34(2): 1035-1048 (2023) - [i87]Zeyu Tang, Yatong Chen, Yang Liu, Kun Zhang:
Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors. CoRR abs/2301.08987 (2023) - [i86]Ignavier Ng, Biwei Huang, Kun Zhang:
Structure Learning with Continuous Optimization: A Sober Look and Beyond. CoRR abs/2304.02146 (2023) - [i85]Hanqi Yan, Lin Gui, Menghan Wang, Kun Zhang, Yulan He:
Explainable Recommender with Geometric Information Bottleneck. CoRR abs/2305.05331 (2023) - 2022
- [j23]Feng Xie
, Yangbo He, Zhi Geng, Zhengming Chen
, Ru Hou, Kun Zhang:
Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models. Entropy 24(4): 512 (2022) - [j22]Sizhe Chen, Fan He, Xiaolin Huang, Kun Zhang:
Relevance attack on detectors. Pattern Recognit. 124: 108491 (2022) - [j21]Wei Chen
, Ruichu Cai
, Kun Zhang, Zhifeng Hao:
Causal Discovery in Linear Non-Gaussian Acyclic Model With Multiple Latent Confounders. IEEE Trans. Neural Networks Learn. Syst. 33(7): 2816-2827 (2022) - [c115]Hao Zhang, Shuigeng Zhou, Kun Zhang, Jihong Guan:
Residual Similarity Based Conditional Independence Test and Its Application in Causal Discovery. AAAI 2022: 5942-5949 - [c114]Zhengming Chen, Feng Xie, Jie Qiao, Zhifeng Hao, Kun Zhang, Ruichu Cai:
Identification of Linear Latent Variable Model with Arbitrary Distribution. AAAI 2022: 6350-6357 - [c113]Ignavier Ng, Kun Zhang:
Towards Federated Bayesian Network Structure Learning with Continuous Optimization. AISTATS 2022: 8095-8111 - [c112]Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien, Kun Zhang:
On the Convergence of Continuous Constrained Optimization for Structure Learning. AISTATS 2022: 8176-8198 - [c111]Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang:
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks (Extended Abstract). Big Data 2022: 4995-4996 - [c110]Zeyu Tang, Kun Zhang:
Attainability and Optimality: The Equalized Odds Fairness Revisited. CLeaR 2022: 754-786 - [c109]Jiaxian Guo, Jiachen Li, Huan Fu, Mingming Gong, Kun Zhang, Dacheng Tao:
Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint. CVPR 2022: 18228-18238 - [c108]Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong, Kayhan Batmanghelich:
Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation. CVPR 2022: 18290-18299 - [c107]Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang:
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning. ICLR 2022 - [c106]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 - [c105]Weiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, Kun Zhang:
Learning Temporally Causal Latent Processes from General Temporal Data. ICLR 2022 - [c104]Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang:
Adversarial Robustness Through the Lens of Causality. ICLR 2022 - [c103]Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang:
Action-Sufficient State Representation Learning for Control with Structural Constraints. ICML 2022: 9260-9279 - [c102]Lingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang:
Partial disentanglement for domain adaptation. ICML 2022: 11455-11472 - [c101]Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang:
Identification of Linear Non-Gaussian Latent Hierarchical Structure. ICML 2022: 24370-24387 - [c100]Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Ehsan Abbasnejad, Mingming Gong, Kun Zhang, Javen Qinfeng Shi:
Truncated Matrix Power Iteration for Differentiable DAG Learning. NeurIPS 2022 - [c99]Haoyue Dai, Peter Spirtes, Kun Zhang:
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models. NeurIPS 2022 - [c98]Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane:
Factored Adaptation for Non-Stationary Reinforcement Learning. NeurIPS 2022 - [c97]Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang:
Latent Hierarchical Causal Structure Discovery with Rank Constraints. NeurIPS 2022 - [c96]Shaoan Xie, Qirong Ho, Kun Zhang:
Unsupervised Image-to-Image Translation with Density Changing Regularization. NeurIPS 2022 - [c95]Yuqin Yang, AmirEmad Ghassami, Mohamed S. Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser:
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error. NeurIPS 2022 - [c94]Weiran Yao, Guangyi Chen, Kun Zhang:
Temporally Disentangled Representation Learning. NeurIPS 2022 - [c93]Yujia Zheng, Ignavier Ng, Kun Zhang:
On the Identifiability of Nonlinear ICA: Sparsity and Beyond. NeurIPS 2022 - [c92]Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong:
Counterfactual Fairness with Partially Known Causal Graph. NeurIPS 2022 - [e5]Bernhard Schölkopf, Caroline Uhler, Kun Zhang:
1st Conference on Causal Learning and Reasoning, CLeaR 2022, Sequoia Conference Center, Eureka, CA, USA, 11-13 April, 2022. Proceedings of Machine Learning Research 177, PMLR 2022 [contents] - [e4]James Cussens, Kun Zhang:
Uncertainty in Artificial Intelligence, Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, UAI 2022, 1-5 August 2022, Eindhoven, The Netherlands. Proceedings of Machine Learning Research 180, PMLR 2022 [contents] - [i84]Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang:
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions. CoRR abs/2201.05666 (2022) - [i83]Ruibo Tu, Kun Zhang, Hedvig Kjellström, Cheng Zhang:
Optimal transport for causal discovery. CoRR abs/2201.09366 (2022) - [i82]Weiran Yao, Guangyi Chen, Kun Zhang:
Learning Latent Causal Dynamics. CoRR abs/2202.04828 (2022) - [i81]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) - [i80]Zeyu Tang, Kun Zhang:
Attainability and Optimality: The Equalized Odds Fairness Revisited. CoRR abs/2202.11853 (2022) - [i79]Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong, Kayhan Batmanghelich:
Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation. CoRR abs/2203.12707 (2022) - [i78]Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane:
Factored Adaptation for Non-Stationary Reinforcement Learning. CoRR abs/2203.16582 (2022) - [i77]Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard D. Bondell
:
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. CoRR abs/2205.13869 (2022) - [i76]Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong:
Counterfactual Fairness with Partially Known Causal Graph. CoRR abs/2205.13972 (2022) - [i75]Zeyu Tang
, Jiji Zhang, Kun Zhang:
What-Is and How-To for Fairness in Machine Learning: A Survey, Reflection, and Perspective. CoRR abs/2206.04101 (2022) - [i74]Yujia Zheng, Ignavier Ng, Kun Zhang:
On the Identifiability of Nonlinear ICA: Sparsity and Beyond. CoRR abs/2206.07751 (2022) - [i73]Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi
:
Weight-variant Latent Causal Models. CoRR abs/2208.14153 (2022) - [i72]Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Kun Zhang, Javen Qinfeng Shi
:
Identifying Latent Causal Content for Multi-Source Domain Adaptation. CoRR abs/2208.14161 (2022) - [i71]Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, M. Ehsan Abbasnejad, Mingming Gong, Kun Zhang, Javen Qinfeng Shi
:
Truncated Matrix Power Iteration for Differentiable DAG Learning. CoRR abs/2208.14571 (2022) - [i70]Guangyi Chen, Weiran Yao, Xiangchen Song, Xinyue Li, Yongming Rao, Kun Zhang:
Prompt Learning with Optimal Transport for Vision-Language Models. CoRR abs/2210.01253 (2022) - [i69]Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang:
Latent Hierarchical Causal Structure Discovery with Rank Constraints. CoRR abs/2210.01798 (2022) - [i68]Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu, Kun Zhang, Dacheng Tao:
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations. CoRR abs/2210.05955 (2022) - [i67]Haoyue Dai, Peter Spirtes, Kun Zhang:
Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models. CoRR abs/2210.11021 (2022) - [i66]Weiran Yao, Guangyi Chen, Kun Zhang:
Temporally Disentangled Representation Learning. CoRR abs/2210.13647 (2022) - [i65]Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang:
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks. CoRR abs/2211.00261 (2022) - [i64]Yuqin Yang, AmirEmad Ghassami, Mohamed S. Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser:
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error. CoRR abs/2211.03984 (2022) - [i63]Shaoan Xie, Zhifei Zhang, Zhe Lin, Tobias Hinz, Kun Zhang:
SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model. CoRR abs/2212.05034 (2022) - 2021
- [j20]Mingming Gong, Peng Liu, Frank C. Sciurba
, Petar Stojanov, Dacheng Tao, George C. Tseng
, Kun Zhang, Kayhan Batmanghelich:
Unpaired data empowers association tests. Bioinform. 37(6): 785-792 (2021) - [j19]M. Reza Heydari
, Saber Salehkaleybar
, Kun Zhang:
Adversarial orthogonal regression: Two non-linear regressions for causal inference. Neural Networks 143: 66-73 (2021) - [j18]Jie Qiao, Ruichu Cai, Kun Zhang, Zhenjie Zhang, Zhifeng Hao:
Causal Discovery with Confounding Cascade Nonlinear Additive Noise Models. ACM Trans. Intell. Syst. Technol. 12(6): 80:1-80:28 (2021) - [j17]Yige Zhang
, Aaron Yi Ding
, Jörg Ott, Mingxuan Yuan, Jia Zeng, Kun Zhang, Weixiong Rao
:
Transfer Learning-Based Outdoor Position Recovery With Cellular Data. IEEE Trans. Mob. Comput. 20(5): 2094-2110 (2021) - [c91]Zhicheng Wang, Biwei Huang, Shikui Tu, Kun Zhang, Lei Xu:
DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding. AAAI 2021: 643-650 - [c90]Hao Zhang, Kun Zhang, Shuigeng Zhou, Jihong Guan, Ji Zhang:
Testing Independence Between Linear Combinations for Causal Discovery. AAAI 2021: 6538-6546 - [c89]Ni Y. Lu, Kun Zhang, Changhe Yuan:
Improving Causal Discovery By Optimal Bayesian Network Learning. AAAI 2021: 8741-8748 - [c88]Shaoan Xie, Mingming Gong, Yanwu Xu, Kun Zhang:
Unaligned Image-to-Image Translation by Learning to Reweight. ICCV 2021: 14154-14164 - [c87]Haiqin Yang, Xiaoyuan Yao, Yiqun Duan, Jianping Shen, Jie Zhong, Kun Zhang:
Progressive Open-Domain Response Generation with Multiple Controllable Attributes. IJCAI 2021: 3279-3285 - [c86]Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang:
Instance-dependent Label-noise Learning under a Structural Causal Model. NeurIPS 2021: 4409-4420 - [c85]Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang:
Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions. NeurIPS 2021: 20308-20320 - [c84]Jeffrey Adams, Niels Hansen, Kun Zhang:
Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases. NeurIPS 2021: 22822-22833 - [c83]Petar Stojanov, Zijian Li, Mingming Gong, Ruichu Cai, Jaime G. Carbonell, Kun Zhang:
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn? NeurIPS 2021: 24791-24803 - [i62]Wei Chen, Kun Zhang, Ruichu Cai, Biwei Huang, Joseph D. Ramsey, Zhifeng Hao, Clark Glymour:
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders. CoRR abs/2103.14238 (2021) - [i61]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) - [i60]Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang:
Adversarial Robustness through the Lens of Causality. CoRR abs/2106.06196 (2021) - [i59]Ruichu Cai, Fengzhu Wu, Zijian Li, Pengfei Wei, Lingling Yi, Kun Zhang:
Graph Domain Adaptation: A Generative View. CoRR abs/2106.07482 (2021) - [i58]Haiqin Yang, Xiaoyuan Yao, Yiqun Duan, Jianping Shen, Jie Zhong, Kun Zhang:
Progressive Open-Domain Response Generation with Multiple Controllable Attributes. CoRR abs/2106.14614 (2021) - [i57]Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang:
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning. CoRR abs/2107.02729 (2021) - [i56]Yige Zhang, Weixiong Rao, Kun Zhang, Lei Chen:
Outdoor Position Recovery from HeterogeneousTelco Cellular Data. CoRR abs/2108.10613 (2021) - [i55]Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang:
Instance-dependent Label-noise Learning under a Structural Causal Model. CoRR abs/2109.02986 (2021) - [i54]Shaoan Xie, Mingming Gong, Yanwu Xu, Kun Zhang:
Unaligned Image-to-Image Translation by Learning to Reweight. CoRR abs/2109.11736 (2021) - [i53]Weiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, Kun Zhang:
Learning Temporally Causal Latent Processes from General Temporal Data. CoRR abs/2110.05428 (2021) - [i52]Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang:
Action-Sufficient State Representation Learning for Control with Structural Constraints. CoRR abs/2110.05721 (2021) - [i51]Ignavier Ng, Kun Zhang:
Towards Federated Bayesian Network Structure Learning with Continuous Optimization. CoRR abs/2110.09356 (2021) - [i50]Zijian Li, Ruichu Cai, Tom Z. J. Fu, Kun Zhang:
Transferable Time-Series Forecasting under Causal Conditional Shift. CoRR abs/2111.03422 (2021) - 2020
- [j16]Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, Kun Zhang:
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables. J. Mach. Learn. Res. 21: 39:1-39:24 (2020) - [j15]Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf:
Causal Discovery from Heterogeneous/Nonstationary Data. J. Mach. Learn. Res. 21: 89:1-89:53 (2020) - [c82]Ziye Chen, Mingming Gong, Yanwu Xu, Chaohui Wang, Kun Zhang, Bo Du:
Compressed Self-Attention for Deep Metric Learning. AAAI 2020: 3561-3568 - [c81]Yanwu Xu, Mingming Gong, Junxiang Chen, Tongliang Liu, Kun Zhang, Kayhan Batmanghelich:
Generative-Discriminative Complementary Learning. AAAI 2020: 6526-6533 - [c80]Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour:
Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets. AAAI 2020: 10153-10161 - [c79]Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven C. H. Hoi:
Adaptive Task Sampling for Meta-learning. ECCV (18) 2020: 752-769 - [c78]AmirEmad Ghassami, Alan Yang, Negar Kiyavash, Kun Zhang:
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs. ICML 2020: 3494-3504 - [c77]Jiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang, Dacheng Tao:
LTF: A Label Transformation Framework for Correcting Label Shift. ICML 2020: 3843-3853 - [c76]Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, Dacheng Tao:
Label-Noise Robust Domain Adaptation. ICML 2020: 10913-10924 - [c75]Thuc Duy Le, Lin Liu, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen:
Preface: The 2020 ACM SIGKDD Workshop on Causal Discovery. CD@KDD 2020: 1-3 - [c74]Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Qingsong Liu, Clark Glymour:
Domain Adaptation as a Problem of Inference on Graphical Models. NeurIPS 2020 - [c73]Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang:
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. NeurIPS 2020 - [c72]Cheng Zhang, Kun Zhang, Yingzhen Li:
A Causal View on Robustness of Neural Networks. NeurIPS 2020 - [c71]Ignavier Ng, AmirEmad Ghassami, Kun Zhang:
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs. NeurIPS 2020 - [c70]Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellström, Kun Zhang, Cheng Zhang:
How do fair decisions fare in long-term qualification? NeurIPS 2020 - [e3]Thuc Duy Le, Lin Liu, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen:
Proceedings of the 2020 KDD Workshop on Causal Discovery (CD@KDD 2020), San Diego, CA, USA, 24 August 2020. Proceedings of Machine Learning Research 127, PMLR 2020 [contents] - [i49]Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Clark Glymour:
Domain Adaptation As a Problem of Inference on Graphical Models. CoRR abs/2002.03278 (2020) - [i48]Naji Shajarisales, Peter Spirtes, Kun Zhang:
Learning from Positive and Unlabeled Data by Identifying the Annotation Process. CoRR abs/2003.01067 (2020) - [i47]Cheng Zhang, Kun Zhang, Yingzhen Li:
A Causal View on Robustness of Neural Networks. CoRR abs/2005.01095 (2020) - [i46]Ignavier Ng, AmirEmad Ghassami, Kun Zhang:
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs. CoRR abs/2006.10201 (2020) - [i45]Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven C. H. Hoi:
Adaptive Task Sampling for Meta-Learning. CoRR abs/2007.08735 (2020) - [i44]Sizhe Chen, Fan He, Xiaolin Huang, Kun Zhang:
Attack on Multi-Node Attention for Object Detection. CoRR abs/2008.06822 (2020) - [i43]Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang:
Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs. CoRR abs/2010.04917 (2020) - [i42]Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellström, Kun Zhang, Cheng Zhang:
How Do Fair Decisions Fare in Long-term Qualification? CoRR abs/2010.11300 (2020) - [i41]Chaochao Lu, Biwei Huang, Ke Wang, José Miguel Hernández-Lobato, Kun Zhang, Bernhard Schölkopf:
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation. CoRR abs/2012.09092 (2020) - [i40]Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao:
Learning Disentangled Semantic Representation for Domain Adaptation. CoRR abs/2012.11807 (2020)
2010 – 2019
- 2019
- [j14]Jiuyong Li
, Kun Zhang, Emre Kiciman, Peng Cui:
Introduction to the Special Section on Advances in Causal Discovery and Inference. ACM Trans. Intell. Syst. Technol. 10(5): 45:1-45:3 (2019) - [c69]AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang:
Counting and Sampling from Markov Equivalent DAGs Using Clique Trees. AAAI 2019: 3664-3671 - [c68]Ruibo Tu, Cheng Zhang, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang:
Causal Discovery in the Presence of Missing Data. AISTATS 2019: 1762-1770 - [c67]Petar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang:
Low-Dimensional Density Ratio Estimation for Covariate Shift Correction. AISTATS 2019: 3449-3458 - [c66]Petar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang:
Data-Driven Approach to Multiple-Source Domain Adaptation. AISTATS 2019: 3487-3496 - [c65]Yige Zhang, Weixiong Rao, Kun Zhang, Mingxuan Yuan, Jia Zeng:
PRNet: Outdoor Position Recovery for Heterogenous Telco Data by Deep Neural Network. CIKM 2019: 1933-1942 - [c64]Huan Fu
, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, Kun Zhang, Dacheng Tao:
Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping. CVPR 2019: 2427-2436 - [c63]