Kun Zhang 0001 - researchr alias

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Viewing Publication 1 - 62 from 62

2023

Causal discovery of 1-factor measurement models in linear latent variable models with arbitrary noise distributionsFeng Xie 0002, Yan Zeng 0002, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang 0001. ijon, 526:48-61, March 2023. [doi]

Model-Based Transfer Reinforcement Learning Based on Graphical Model RepresentationsYuewen Sun, Kun Zhang 0001, Changyin Sun. tnn, 34(2):1035-1048, February 2023. [doi]

2022

Identification of Linear Non-Gaussian Latent Hierarchical StructureFeng Xie 0002, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang 0001. icml 2022: 24370-24387 [doi]

Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image TranslationYanwu Xu 0003, Shaoan Xie, Wenhao Wu, Kun Zhang 0001, Mingming Gong, Kayhan Batmanghelich. cvpr 2022: 18290-18299 [doi]

Truncated Matrix Power Iteration for Differentiable DAG LearningZhen Zhang 0008, Ignavier Ng, Dong Gong, Yuhang Liu, Ehsan Abbasnejad, Mingming Gong, Kun Zhang 0001, Javen Qinfeng Shi. nips 2022: [doi]

Residual Similarity Based Conditional Independence Test and Its Application in Causal DiscoveryHao Zhang 0079, Shuigeng Zhou, Kun Zhang 0001, Jihong Guan. AAAI 2022: 5942-5949 [doi]

Causal Discovery in Linear Non-Gaussian Acyclic Model With Multiple Latent ConfoundersWei Chen, Ruichu Cai, Kun Zhang 0001, Zhifeng Hao. tnn, 33(7):2816-2827, 2022. [doi]

Identification of Linear Latent Variable Model with Arbitrary DistributionZhengming Chen, Feng Xie, Jie Qiao, Zhifeng Hao, Kun Zhang 0001, Ruichu Cai. AAAI 2022: 6350-6357 [doi]

Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian ModelsHaoyue Dai, Peter Spirtes, Kun Zhang 0001. nips 2022: [doi]

Factored Adaptation for Non-Stationary Reinforcement LearningFan Feng, Biwei Huang, Kun Zhang 0001, Sara Magliacane. nips 2022: [doi]

Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency ConstraintJiaxian Guo, Jiachen Li, Huan Fu, Mingming Gong, Kun Zhang 0001, Dacheng Tao. cvpr 2022: 18228-18238 [doi]

Action-Sufficient State Representation Learning for Control with Structural ConstraintsBiwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang 0001. icml 2022: 9260-9279 [doi]

AdaRL: What, Where, and How to Adapt in Transfer Reinforcement LearningBiwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang 0001. iclr 2022: [doi]

Latent Hierarchical Causal Structure Discovery with Rank ConstraintsBiwei Huang, Charles Jia Han Low, Feng Xie 0002, Clark Glymour, Kun Zhang 0001. nips 2022: [doi]

Partial disentanglement for domain adaptationLingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang 0001. icml 2022: 11455-11472 [doi]

Towards Federated Bayesian Network Structure Learning with Continuous OptimizationIgnavier Ng, Kun Zhang 0001. aistats 2022: 8095-8111 [doi]

On the Convergence of Continuous Constrained Optimization for Structure LearningIgnavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien, Kun Zhang 0001. aistats 2022: 8176-8198 [doi]

Conditional Contrastive Learning with KernelYao-Hung Hubert Tsai, Tianqin Li, Martin Q. Ma, Han Zhao 0002, Kun Zhang 0001, Louis-Philippe Morency, Ruslan Salakhutdinov. iclr 2022: [doi]

Unsupervised Image-to-Image Translation with Density Changing RegularizationShaoan Xie, Qirong Ho, Kun Zhang 0001. nips 2022: [doi]

Causal Discovery in Linear Latent Variable Models Subject to Measurement ErrorYuqin Yang, AmirEmad Ghassami, Mohamed S. Nafea, Negar Kiyavash, Kun Zhang 0001, Ilya Shpitser. nips 2022: [doi]

Temporally Disentangled Representation LearningWeiran Yao, Guangyi Chen 0002, Kun Zhang 0001. nips 2022: [doi]

Learning Temporally Causal Latent Processes from General Temporal DataWeiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, Kun Zhang 0001. iclr 2022: [doi]

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks (Extended Abstract)Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu 0001, Kun Zhang 0001, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang. bigdataconf 2022: 4995-4996 [doi]

Adversarial Robustness Through the Lens of CausalityYonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu 0001, Xinmei Tian 0001, Bo Han 0003, Bernhard Schölkopf, Kun Zhang 0001. iclr 2022: [doi]

On the Identifiability of Nonlinear ICA: Sparsity and BeyondYujia Zheng, Ignavier Ng, Kun Zhang 0001. nips 2022: [doi]

Counterfactual Fairness with Partially Known Causal GraphAoqi Zuo, Susan Wei, Tongliang Liu, Bo Han 0003, Kun Zhang 0001, Mingming Gong. nips 2022: [doi]

2021

Unpaired data empowers association testsMingming Gong, Peng Liu, Frank C. Sciurba, Petar Stojanov, Dacheng Tao, George C. Tseng, Kun Zhang 0001, Kayhan Batmanghelich. bioinformatics, 37(6):785-792, 2021. [doi]

Adversarial orthogonal regression: Two non-linear regressions for causal inferenceM. Reza Heydari, Saber Salehkaleybar, Kun Zhang 0001. NN, 143:66-73, 2021. [doi]

Reliable Causal Discovery with Improved Exact Search and Weaker AssumptionsIgnavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang 0001. nips 2021: 20308-20320 [doi]

Causal Discovery with Confounding Cascade Nonlinear Additive Noise ModelsJie Qiao, Ruichu Cai, Kun Zhang 0001, Zhenjie Zhang, Zhifeng Hao. tist, 12(6), 2021. [doi]

Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?Petar Stojanov, Zijian Li 0001, Mingming Gong, Ruichu Cai, Jaime G. Carbonell, Kun Zhang 0001. nips 2021: 24791-24803 [doi]

Unaligned Image-to-Image Translation by Learning to ReweightShaoan Xie, Mingming Gong, Yanwu Xu 0001, Kun Zhang 0001. iccv 2021: 14154-14164 [doi]

Instance-dependent Label-noise Learning under a Structural Causal ModelYu Yao, Tongliang Liu, Mingming Gong, Bo Han 0003, Gang Niu 0001, Kun Zhang 0001. nips 2021: 4409-4420 [doi]

2020

Domain Adaptation as a Problem of Inference on Graphical ModelsKun Zhang 0001, Mingming Gong, Petar Stojanov, Biwei Huang, Qingsong Liu, Clark Glymour. nips 2020: [doi]

Generalized Independent Noise Condition for Estimating Latent Variable Causal GraphsFeng Xie 0002, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang 0001. nips 2020: [doi]

{LTFJiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang 0001, Dacheng Tao. icml 2020: 3843-3853 [doi]

Causal Discovery from Heterogeneous/Nonstationary DataBiwei Huang, Kun Zhang 0001, Jiji Zhang, Joseph Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf. jmlr, 21, 2020. [doi]

Preface: The 2020 ACM SIGKDD Workshop on Causal DiscoveryThuc Duy Le, Lin Liu 0003, Kun Zhang 0001, Emre Kiciman, Peng Cui 0001, Aapo Hyvärinen. kdd 2020: 1-3 [doi]

On the Role of Sparsity and DAG Constraints for Learning Linear DAGsIgnavier Ng, AmirEmad Ghassami, Kun Zhang 0001. nips 2020: [doi]

Learning Linear Non-Gaussian Causal Models in the Presence of Latent VariablesSaber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, Kun Zhang 0001. jmlr, 21, 2020. [doi]

Label-Noise Robust Domain AdaptationXiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang 0001, Kayhan Batmanghelich, Dacheng Tao. icml 2020: 10913-10924 [doi]

How do fair decisions fare in long-term qualification?Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellström, Kun Zhang 0001, Cheng Zhang 0005. nips 2020: [doi]

2019

Learning Disentangled Semantic Representation for Domain AdaptationRuichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang 0001, Zhifeng Hao. IJCAI 2019: 2060-2066 [doi]

Triad Constraints for Learning Causal Structure of Latent VariablesRuichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang 0001. nips 2019: 12863-12872 [doi]

Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain MappingHuan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, Kun Zhang 0001, Dacheng Tao. cvpr 2018: 2427-2436 [doi]

Counting and Sampling from Markov Equivalent DAGs Using Clique TreesAmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang 0001. AAAI 2019: 3664-3671 [doi]

Twin Auxilary Classifiers GANMingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang 0001, Kayhan Batmanghelich. nips 2019: 1328-1337 [doi]

Domain Generalization via Multidomain Discriminant AnalysisShoubo Hu, Kun Zhang 0001, Zhitang Chen, Laiwan Chan. uai 2018: 101 [doi]

Causal Discovery and Forecasting in Nonstationary Environments with State-Space ModelsBiwei Huang, Kun Zhang 0001, Mingming Gong, Clark Glymour. icml 2019: 2901-2910 [doi]

Specific and Shared Causal Relation Modeling and Mechanism-Based ClusteringBiwei Huang, Kun Zhang 0001, Pengtao Xie, Mingming Gong, Eric P. Xing, Clark Glymour. nips 2019: 13510-13521 [doi]

Preface: The 2019 ACM SIGKDD Workshop on Causal DiscoveryThuc Duy Le, Jiuyong Li, Kun Zhang 0001, Emre Kiciman, Peng Cui 0001, Aapo Hyvärinen. kdd 2019: 1-3 [doi]

Causal Discovery with General Non-Linear Relationships using Non-Linear ICARicardo Pio Monti, Kun Zhang 0001, Aapo Hyvärinen. uai 2018: 45 [doi]

Low-Dimensional Density Ratio Estimation for Covariate Shift CorrectionPetar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang 0001. aistats 2019: 3449-3458 [doi]

Data-Driven Approach to Multiple-Source Domain AdaptationPetar Stojanov, Mingming Gong, Jaime G. Carbonell, Kun Zhang 0001. aistats 2019: 3487-3496 [doi]

Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm EvaluationRuibo Tu, Kun Zhang 0001, Bo C. Bertilson, Hedvig Kjellström, Cheng Zhang 0005. nips 2019: 12773-12784 [doi]

2018

2017

Causal Discovery from Temporally Aggregated Time SeriesMingming Gong, Kun Zhang 0001, Bernhard Schölkopf, Clark Glymour, Dacheng Tao. uai 2017: [doi]

Behind Distribution Shift: Mining Driving Forces of Changes and Causal ArrowsBiwei Huang, Kun Zhang 0001, Jiji Zhang, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf. icdm 2017: 913-918 [doi]

2013

Semi-supervised Learning in Causal and Anticausal SettingsBernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang 0001, Joris M. Mooij. BIRTHDAY 2013: 129-141 [doi]

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