Zhang, Kun

245 publications

ICLR 2025 A Conditional Independence Test in the Presence of Discretization Boyang Sun, Yu Yao, Guang-Yuan Hao, Yumou Qiu, Kun Zhang

ICML 2025 A General Representation-Based Approach to Multi-Source Domain Adaptation Ignavier Ng, Yan Li, Zijian Li, Yujia Zheng, Guangyi Chen, Kun Zhang

ICLR 2025 A Robust Method to Discover Causal or Anticausal Relation Yu Yao, Yang Zhou, Bo Han, Mingming Gong, Kun Zhang, Tongliang Liu

ICML 2025 A Sample Efficient Conditional Independence Test in the Presence of Discretization Boyang Sun, Yu Yao, Xinshuai Dong, Zongfang Liu, Tongliang Liu, Yumou Qiu, Kun Zhang

ICLR 2025 A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery Yingyu Lin, Yuxing Huang, Wenqin Liu, Haoran Deng, Ignavier Ng, Kun Zhang, Mingming Gong, Yian Ma, Biwei Huang

MLJ 2025 Alignclip: Navigating the Misalignments for Robust Vision-Language Generalization Zhongyi Han, Gongxu Luo, Hao Sun, Yaqian Li, Bo Han, Mingming Gong, Kun Zhang, Tongliang Liu

ICLR 2025 Analytic DAG Constraints for Differentiable DAG Learning Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Mingming Gong, Biwei Huang, Kun Zhang, Anton van den Hengel, Javen Qinfeng Shi

ICLR 2025 Causal Graph Transformer for Treatment Effect Estimation Under Unknown Interference Anpeng Wu, Haiyi Qiu, Zhengming Chen, Zijian Li, Ruoxuan Xiong, Fei Wu, Kun Zhang

AISTATS 2025 Causal Representation Learning from General Environments Under Nonparametric Mixing Ignavier Ng, Shaoan Xie, Xinshuai Dong, Peter Spirtes, Kun Zhang

ICLR 2025 Causal Representation Learning from Multimodal Biomedical Observations Yuewen Sun, Lingjing Kong, Guangyi Chen, Loka Li, Gongxu Luo, Zijian Li, Yixuan Zhang, Yujia Zheng, Mengyue Yang, Petar Stojanov, Eran Segal, Eric P. Xing, Kun Zhang

NeurIPS 2025 CausalVerse: Benchmarking Causal Representation Learning with Configurable High-Fidelity Simulations Guangyi Chen, Yunlong Deng, Peiyuan Zhu, Yan Li, Yifan Shen, Zijian Li, Kun Zhang

AAAI 2025 Continual Unsupervised Generative Modelling via Online Optimal Transport Fei Ye, Adrian G. Bors, Kun Zhang

CVPR 2025 DH-Set: Improving Vision-Language Alignment with Diverse and Hybrid Set-Embeddings Learning Kun Zhang, Jingyu Li, Zhe Li, S.Kevin Zhou

NeurIPS 2025 Detecting Generated Images by Fitting Natural Image Distributions Yonggang Zhang, Jun Nie, Xinmei Tian, Mingming Gong, Kun Zhang, Bo Han

ICLR 2025 Differentiable Causal Discovery for Latent Hierarchical Causal Models Parjanya Prajakta Prashant, Ignavier Ng, Kun Zhang, Biwei Huang

AAAI 2025 Dynamic Expansion Diffusion Learning for Lifelong Generative Modelling Fei Ye, Adrian G. Bors, Kun Zhang

IJCAI 2025 Empowering LLMs with Logical Reasoning: A Comprehensive Survey Fengxiang Cheng, Haoxuan Li, Fenrong Liu, Robert van Rooij, Kun Zhang, Zhouchen Lin

ICML 2025 Extracting Rare Dependence Patterns via Adaptive Sample Reweighting Yiqing Li, Yewei Xia, Xiaofei Wang, Zhengming Chen, Liuhua Peng, Mingming Gong, Kun Zhang

ICML 2025 Fairness on Principal Stratum: A New Perspective on Counterfactual Fairness Haoxuan Li, Zeyu Tang, Zhichao Jiang, Zhuangyan Fang, Yue Liu, Zhi Geng, Kun Zhang

ICLR 2025 Flow: Modularized Agentic Workflow Automation Boye Niu, Yiliao Song, Kai Lian, Yifan Shen, Yu Yao, Kun Zhang, Tongliang Liu

NeurIPS 2025 Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders Gongxu Luo, Haoyue Dai, Loka Li, Chengqian Gao, Boyang Sun, Kun Zhang

ICCV 2025 Hierarchy-Aware Pseudo Word Learning with Text Adaptation for Zero-Shot Composed Image Retrieval Zhe Li, Lei Zhang, Zheren Fu, Kun Zhang, Zhendong Mao

ICLR 2025 Identification of Intermittent Temporal Latent Process Yuke Li, Yujia Zheng, Guangyi Chen, Kun Zhang, Heng Huang

ICML 2025 Identification of Latent Confounders via Investigating the Tensor Ranks of the Nonlinear Observations Zhengming Chen, Yewei Xia, Feng Xie, Jie Qiao, Zhifeng Hao, Ruichu Cai, Kun Zhang

NeurIPS 2025 LLM Interpretability with Identifiable Temporal-Instantaneous Representation Xiangchen Song, Jiaqi Sun, Zijian Li, Yujia Zheng, Kun Zhang

TMLR 2025 Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi

ICML 2025 Latent Variable Causal Discovery Under Selection Bias Haoyue Dai, Yiwen Qiu, Ignavier Ng, Xinshuai Dong, Peter Spirtes, Kun Zhang

NeurIPS 2025 Learning Counterfactual Outcomes Under Rank Preservation Peng Wu, Haoxuan Li, Chunyuan Zheng, Yan Zeng, Jiawei Chen, Yang Liu, Ruocheng Guo, Kun Zhang

ICLR 2025 Learning Graph Invariance by Harnessing Spuriosity Tianjun Yao, Yongqiang Chen, Kai Hu, Tongliang Liu, Kun Zhang, Zhiqiang Shen

ICML 2025 Learning Vision and Language Concepts for Controllable Image Generation Shaoan Xie, Lingjing Kong, Yujia Zheng, Zeyu Tang, Eric Xing, Guangyi Chen, Kun Zhang

TMLR 2025 Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges Usman Gohar, Zeyu Tang, Jialu Wang, Kun Zhang, Peter Spirtes, Yang Liu, Lu Cheng

IJCAI 2025 MVP-CBM: Multi-Layer Visual Preference-Enhanced Concept Bottleneck Model for Explainable Medical Image Classification Chunjiang Wang, Kun Zhang, Yandong Liu, Zhiyang He, Xiaodong Tao, S. Kevin Zhou

ICLR 2025 Noisy Test-Time Adaptation in Vision-Language Models Chentao Cao, Zhun Zhong, Zhanke Zhou, Tongliang Liu, Yang Liu, Kun Zhang, Bo Han

AISTATS 2025 Nonparametric Factor Analysis and Beyond Yujia Zheng, Yang Liu, Jiaxiong Yao, Yingyao Hu, Kun Zhang

ICML 2025 Nonparametric Identification of Latent Concepts Yujia Zheng, Shaoan Xie, Kun Zhang

CVPR 2025 OCRT: Boosting Foundation Models in the Open World with Object-Concept-Relation Triad Luyao Tang, Yuxuan Yuan, Chaoqi Chen, Zeyu Zhang, Yue Huang, Kun Zhang

ICLR 2025 On the Identification of Temporal Causal Representation with Instantaneous Dependence Zijian Li, Yifan Shen, Kaitao Zheng, Ruichu Cai, Xiangchen Song, Mingming Gong, Guangyi Chen, Kun Zhang

ICLRW 2025 On the Language of Thoughts in Large Language Models Chenxi Liu, Yongqiang Chen, Tongliang Liu, James Cheng, Bo Han, Kun Zhang

NeurIPS 2025 Online Time Series Forecasting with Theoretical Guarantees Zijian Li, Changze Zhou, Minghao Fu, Sanjay Manjunath, Fan Feng, Guangyi Chen, Yingyao Hu, Ruichu Cai, Kun Zhang

ICML 2025 Permutation-Based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data Xinshuai Dong, Ignavier Ng, Boyang Sun, Haoyue Dai, Guang-Yuan Hao, Shunxing Fan, Peter Spirtes, Yumou Qiu, Kun Zhang

NeurIPS 2025 PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis Yan Wu, Esther Wershof, Sebastian M Schmon, Marcel Nassar, Błażej Osiński, Ridvan Eksi, Zichao Yan, Rory Stark, Kun Zhang, Thore Graepel

NeurIPS 2025 Practical Kernel Selection for Kernel-Based Conditional Independence Test Wenjie Wang, Mingming Gong, Biwei Huang, James Bailey, Bo Han, Kun Zhang, Feng Liu

ICLR 2025 Prompting Fairness: Integrating Causality to Debias Large Language Models Jingling Li, Zeyu Tang, Xiaoyu Liu, Peter Spirtes, Kun Zhang, Liu Leqi, Yang Liu

ICML 2025 Reflection-Window Decoding: Text Generation with Selective Refinement Zeyu Tang, Zhenhao Chen, Xiangchen Song, Loka Li, Yunlong Deng, Yifan Shen, Guangyi Chen, Peter Spirtes, Kun Zhang

CVPR 2025 SmartCLIP: Modular Vision-Language Alignment with Identification Guarantees Shaoan Xie, Lingjing Lingjing, Yujia Zheng, Yu Yao, Zeyu Tang, Eric P. Xing, Guangyi Chen, Kun Zhang

ICLR 2025 Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning Zijian Li, Shunxing Fan, Yujia Zheng, Ignavier Ng, Shaoan Xie, Guangyi Chen, Xinshuai Dong, Ruichu Cai, Kun Zhang

NeurIPS 2025 The Third Pillar of Causal Analysis? a Measurement Perspective on Causal Representations Dingling Yao, Shimeng Huang, Riccardo Cadei, Kun Zhang, Francesco Locatello

NeurIPS 2025 Thought Communication in Multiagent Collaboration Yujia Zheng, Zhuokai Zhao, Zijian Li, Yaqi Xie, Mingze Gao, Lizhu Zhang, Kun Zhang

NeurIPS 2025 Towards Accurate Time Series Forecasting via Implicit Decoding Xinyu Li, Yuchen Luo, Hao Wang, Haoxuan Li, Liuhua Peng, Feng Liu, Yandong Guo, Kun Zhang, Mingming Gong

NeurIPS 2025 Towards Identifiability of Hierarchical Temporal Causal Representation Learning Zijian Li, Minghao Fu, Junxian Huang, Yifan Shen, Ruichu Cai, Yuewen Sun, Guangyi Chen, Kun Zhang

NeurIPS 2025 Towards Self-Refinement of Vision-Language Models with Triangular Consistency Yunlong Deng, Guangyi Chen, Tianpei Gu, Lingjing Kong, Yan Li, Zeyu Tang, Kun Zhang

AISTATS 2025 Type Information-Assisted Self-Supervised Knowledge Graph Denoising Jiaqi Sun, Yujia Zheng, Xinshuai Dong, Haoyue Dai, Kun Zhang

ICLR 2025 When Selection Meets Intervention: Additional Complexities in Causal Discovery Haoyue Dai, Ignavier Ng, Jianle Sun, Zeyu Tang, Gongxu Luo, Xinshuai Dong, Peter Spirtes, Kun Zhang

NeurIPS 2024 A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs Haoxuan Li, Yue Liu, Zhi Geng, Kun Zhang

ICLR 2024 A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang

AAAI 2024 ACAMDA: Improving Data Efficiency in Reinforcement Learning Through Guided Counterfactual Data Augmentation Yuewen Sun, Erli Wang, Biwei Huang, Chaochao Lu, Lu Feng, Changyin Sun, Kun Zhang

ICML 2024 CaRiNG: Learning Temporal Causal Representation Under Non-Invertible Generation Process Guangyi Chen, Yifan Shen, Zhenhao Chen, Xiangchen Song, Yuewen Sun, Weiran Yao, Xiao Liu, Kun Zhang

NeurIPSW 2024 Causal Discovery in Linear Models with Unobserved Variables and Measurement Error Yuqin Yang, Mohamed S Nafea, Negar Kiyavash, Kun Zhang, AmirEmad Ghassami

ICML 2024 Causal Representation Learning from Multiple Distributions: A General Setting Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng

ICLR 2024 Causal Structure Recovery with Latent Variables Under Milder Distributional and Graphical Assumptions Xiu-Chuan Li, Kun Zhang, Tongliang Liu

NeurIPS 2024 Causal Temporal Representation Learning with Nonstationary Sparse Transition Xiangchen Song, Zijian Li, Guangyi Chen, Yujia Zheng, Yewen Fan, Xinshuai Dong, Kun Zhang

MLOSS 2024 Causal-Learn: Causal Discovery in Python Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang

MLJ 2024 Cost-Sensitive Sparse Group Online Learning for Imbalanced Data Streams Zhong Chen, Victor S. Sheng, Andrea Edwards, Kun Zhang

ICML 2024 Detecting and Identifying Selection Structure in Sequential Data Yujia Zheng, Zeyu Tang, Yiwen Qiu, Bernhard Schölkopf, Kun Zhang

NeurIPSW 2024 Differentiable Causal Discovery for Latent Hierarchical Causal Models Parjanya Prajakta Prashant, Ignavier Ng, Kun Zhang, Biwei Huang

NeurIPS 2024 Discovery of the Hidden World with Large Language Models Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang

ICML 2024 Empowering Graph Invariance Learning with Deep Spurious Infomax Tianjun Yao, Yongqiang Chen, Zhenhao Chen, Kai Hu, Zhiqiang Shen, Kun Zhang

ICLR 2024 Federated Causal Discovery from Heterogeneous Data Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang

ICLR 2024 Gene Regulatory Network Inference in the Presence of Dropouts: A Causal View Haoyue Dai, Ignavier Ng, Gongxu Luo, Peter Spirtes, Petar Stojanov, Kun Zhang

JMLR 2024 Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang

ICLRW 2024 How Well Does GPT-4V(ision) Adapt to Distribution Shifts? a Preliminary Investigation Zhongyi Han, Guanglin Zhou, Rundong He, Jindong Wang, Tailin Wu, Yilong Yin, Salman Khan, Lina Yao, Tongliang Liu, Kun Zhang

JMLR 2024 Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations Yuanyuan Wang, Wei Huang, Mingming Gong, Xi Geng, Tongliang Liu, Kun Zhang, Dacheng Tao

ICLR 2024 Identifiable Latent Polynomial Causal Models Through the Lens of Change Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi

AAAI 2024 Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants Wei Chen, Zhiyi Huang, Ruichu Cai, Zhifeng Hao, Kun Zhang

AAAI 2024 Identification of Necessary Semantic Undertakers in the Causal View for Image-Text Matching Huatian Zhang, Lei Zhang, Kun Zhang, Zhendong Mao

NeurIPS 2024 Identifying Latent State-Transition Processes for Individualized Reinforcement Learning Yuewen Sun, Biwei Huang, Yu Yao, Donghuo Zeng, Xinshuai Dong, Songyao Jin, Boyang Sun, Roberto Legaspi, Kazushi Ikeda, Peter Spirtes, Kun Zhang

NeurIPS 2024 Identifying Selections for Unsupervised Subtask Discovery Yiwen Qiu, Yujia Zheng, Kun Zhang

NeurIPSW 2024 Increasing Fairness via Combination with Learning Guarantees Yijun Bian, Kun Zhang

ICLR 2024 LLCP: Learning Latent Causal Processes for Reasoning-Based Video Question Answer Guangyi Chen, Yuke Li, Xiao Liu, Zijian Li, Eman Al Suradi, Donglai Wei, Kun Zhang

NeurIPS 2024 Learning Discrete Concepts in Latent Hierarchical Models Lingjing Kong, Guangyi Chen, Biwei Huang, Eric P. Xing, Yuejie Chi, Kun Zhang

NeurIPS 2024 Learning Discrete Latent Variable Structures with Tensor Rank Conditions Zhengming Chen, Ruichu Cai, Feng Xie, Jie Qiao, Anpeng Wu, Zijian Li, Zhifeng Hao, Kun Zhang

AISTATS 2024 Local Causal Discovery with Linear Non-Gaussian Cyclic Models Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang

NeurIPS 2024 Natural Counterfactuals with Necessary Backtracking Guang-Yuan Hao, Jiji Zhang, Biwei Huang, Hao Wang, Kun Zhang

NeurIPS 2024 Neural Collapse Inspired Feature Alignment for Out-of-Distribution Generalization Zhikang Chen, Min Zhang, Sen Cui, Haoxuan Li, Gang Niu, Mingming Gong, Changshui Zhang, Kun Zhang

NeurIPS 2024 On Causal Discovery in the Presence of Deterministic Relations Loka Li, Haoyue Dai, Hanin Al Ghothani, Biwei Huang, Jiji Zhang, Shahar Harel, Isaac Bentwich, Guangyi Chen, Kun Zhang

NeurIPS 2024 On the Parameter Identifiability of Partially Observed Linear Causal Models Xinshuai Dong, Ignavier Ng, Biwei Huang, Yuewen Sun, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang

ICML 2024 On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data Shunxing Fan, Mingming Gong, Kun Zhang

ICML 2024 Optimal Kernel Choice for Score Function-Based Causal Discovery Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong

NeurIPSW 2024 PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis Yan Wu, Esther Wershof, Sebastian M Schmon, Marcel Nassar, Błażej Osiński, Ridvan Eksi, Kun Zhang, Thore Graepel

ICLR 2024 Procedural Fairness Through Decoupling Objectionable Data Generating Components Zeyu Tang, Jialu Wang, Yang Liu, Peter Spirtes, Kun Zhang

NeurIPSW 2024 Provably Learning Concepts by Comparison Yujia Zheng, Shaoan Xie, Kun Zhang

AAAI 2024 S3A: Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment Sheng Zhang, Muzammal Naseer, Guangyi Chen, Zhiqiang Shen, Salman H. Khan, Kun Zhang, Fahad Khan

ICML 2024 Score-Based Causal Discovery of Latent Variable Causal Models Ignavier Ng, Xinshuai Dong, Haoyue Dai, Biwei Huang, Peter Spirtes, Kun Zhang

ICLRW 2024 Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing Framework Jingling Li, Zeyu Tang, Xiaoyu Liu, Peter Spirtes, Kun Zhang, Liu Leqi, Yang Liu

ICLR 2024 Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability Songyao Jin, Feng Xie, Guangyi Chen, Biwei Huang, Zhengming Chen, Xinshuai Dong, Kun Zhang

CLeaR 2024 Structure Learning with Continuous Optimization: A Sober Look and Beyond Ignavier Ng, Biwei Huang, Kun Zhang

NeurIPS 2024 Towards Understanding Extrapolation: A Causal Lens Lingjing Kong, Guangyi Chen, Petar Stojanov, Haoxuan Li, Eric P. Xing, Kun Zhang

AAAI 2024 Tree-of-Reasoning Question Decomposition for Complex Question Answering with Large Language Models Kun Zhang, Jiali Zeng, Fandong Meng, Yuanzhuo Wang, Shiqi Sun, Long Bai, Huawei Shen, Jie Zhou

ICLR 2023 Calibration Matters: Tackling Maximization Bias in Large-Scale Advertising Recommendation Systems Yewen Fan, Nian Si, Kun Zhang

ICLR 2023 Causal Balancing for Domain Generalization Xinyi Wang, Michael Saxon, Jiachen Li, Hongyang Zhang, Kun Zhang, William Yang Wang

ICML 2023 Causal Discovery with Latent Confounders Based on Higher-Order Cumulants Ruichu Cai, Zhiyi Huang, Wei Chen, Zhifeng Hao, Kun Zhang

CLeaR 2023 Causal Discovery with Score Matching on Additive Models with Arbitrary Noise Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello

NeurIPS 2023 Counterfactual Generation with Identifiability Guarantees Hanqi Yan, Lingjing Kong, Lin Gui, Yuejie Chi, Eric P. Xing, Yulan He, Kun Zhang

NeurIPS 2023 Disentangling Cognitive Diagnosis with Limited Exercise Labels Xiangzhi Chen, Le Wu, Fei Liu, Lei Chen, Kun Zhang, Richang Hong, Meng Wang

ICML 2023 Evolving Semantic Prototype Improves Generative Zero-Shot Learning Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang

AAAI 2023 Fair Representation Learning for Recommendation: A Mutual Information Perspective Chen Zhao, Le Wu, Pengyang Shao, Kun Zhang, Richang Hong, Meng Wang

ICML 2023 Feature Expansion for Graph Neural Networks Jiaqi Sun, Lin Zhang, Guangyi Chen, Peng Xu, Kun Zhang, Yujiu Yang

ICLR 2023 GAIN: On the Generalization of Instructional Action Understanding Junlong Li, Guangyi Chen, Yansong Tang, Jinan Bao, Kun Zhang, Jie Zhou, Jiwen Lu

ICLR 2023 Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks Yujia Zheng, Ignavier Ng, Yewen Fan, Kun Zhang

NeurIPS 2023 Generalizing Nonlinear ICA Beyond Structural Sparsity Yujia Zheng, Kun Zhang

ICML 2023 Identifiability of Label Noise Transition Matrix Yang Liu, Hao Cheng, Kun Zhang

NeurIPS 2023 Identification of Nonlinear Latent Hierarchical Models Lingjing Kong, Biwei Huang, Feng Xie, Eric P. Xing, Yuejie Chi, Kun Zhang

NeurIPS 2023 Learning World Models with Identifiable Factorization Yuren Liu, Biwei Huang, Zhengmao Zhu, Honglong Tian, Mingming Gong, Yang Yu, Kun Zhang

AAAI 2023 Measuring the Privacy Leakage via Graph Reconstruction Attacks on Simplicial Neural Networks (Student Abstract) Huixin Zhan, Kun Zhang, Keyi Lu, Victor S. Sheng

ICML 2023 Model Transferability with Responsive Decision Subjects Yatong Chen, Zeyu Tang, Kun Zhang, Yang Liu

ICLR 2023 Multi-Domain Image Generation and Translation with Identifiability Guarantees Shaoan Xie, Lingjing Kong, Mingming Gong, Kun Zhang

NeurIPS 2023 On the Identifiability of Sparse ICA Without Assuming Non-Gaussianity Ignavier Ng, Yujia Zheng, Xinshuai Dong, Kun Zhang

ICLR 2023 PLOT: Prompt Learning with Optimal Transport for Vision-Language Models Guangyi Chen, Weiran Yao, Xiangchen Song, Xinyue Li, Yongming Rao, Kun Zhang

CLeaR 2023 Scalable Causal Discovery with Score Matching Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello

CVPR 2023 SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model Shaoan Xie, Zhifei Zhang, Zhe Lin, Tobias Hinz, Kun Zhang

NeurIPS 2023 Subspace Identification for Multi-Source Domain Adaptation Zijian Li, Ruichu Cai, Guangyi Chen, Boyang Sun, Zhifeng Hao, Kun Zhang

ICCV 2023 Tem-Adapter: Adapting Image-Text Pretraining for Video Question Answer Guangyi Chen, Xiao Liu, Guangrun Wang, Kun Zhang, Philip H.S. Torr, Xiao-Ping Zhang, Yansong Tang

NeurIPS 2023 Temporally Disentangled Representation Learning Under Unknown Nonstationarity Xiangchen Song, Weiran Yao, Yewen Fan, Xinshuai Dong, Guangyi Chen, Juan Carlos Niebles, Eric P. Xing, Kun Zhang

ICLR 2023 Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors Zeyu Tang, Yatong Chen, Yang Liu, Kun Zhang

CVPR 2023 Understanding Masked Autoencoders via Hierarchical Latent Variable Models Lingjing Kong, Martin Q. Ma, Guangyi Chen, Eric P. Xing, Yuejie Chi, Louis-Philippe Morency, Kun Zhang

CVPR 2023 Unpaired Image-to-Image Translation with Shortest Path Regularization Shaoan Xie, Yanwu Xu, Mingming Gong, Kun Zhang

CVPR 2023 Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction Guangyi Chen, Zhenhao Chen, Shunxing Fan, Kun Zhang

ICML 2023 Which Is Better for Learning with Noisy Labels: The Semi-Supervised Method or Modeling Label Noise? Yu Yao, Mingming Gong, Yuxuan Du, Jun Yu, Bo Han, Kun Zhang, Tongliang Liu

AISTATS 2022 On the Convergence of Continuous Constrained Optimization for Structure Learning Ignavier Ng, Sebastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien, Kun Zhang

AISTATS 2022 Towards Federated Bayesian Network Structure Learning with Continuous Optimization Ignavier Ng, Kun Zhang

ICML 2022 Action-Sufficient State Representation Learning for Control with Structural Constraints Biwei Huang, Chaochao Lu, Liu Leqi, Jose Miguel Hernandez-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang

ICLRW 2022 Action-Sufficient State Representation Learning for Control with Structural Constraints Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang

ICLR 2022 AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang

ICLR 2022 Adversarial Robustness Through the Lens of Causality Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang

CVPR 2022 Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint Jiaxian Guo, Jiachen Li, Huan Fu, Mingming Gong, Kun Zhang, Dacheng Tao

CLeaR 2022 Attainability and Optimality: The Equalized Odds Fairness Revisited Zeyu Tang, Kun Zhang

ICMLW 2022 Causal Balancing for Domain Generalization Xinyi Wang, Michael Saxon, Jiachen Li, Hongyang Zhang, Kun Zhang, William Yang Wang

NeurIPS 2022 Causal Discovery in Linear Latent Variable Models Subject to Measurement Error Yuqin Yang, AmirEmad Ghassami, Mohamed Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser

ICLR 2022 Conditional Contrastive Learning with Kernel Yao-Hung Hubert Tsai, Tianqin Li, Martin Q. Ma, Han Zhao, Kun Zhang, Louis-Philippe Morency, Ruslan Salakhutdinov

NeurIPS 2022 Counterfactual Fairness with Partially Known Causal Graph Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong

NeurIPS 2022 Factored Adaptation for Non-Stationary Reinforcement Learning Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane

ICML 2022 GLaM: Efficient Scaling of Language Models with Mixture-of-Experts Nan Du, Yanping Huang, Andrew M Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten P Bosma, Zongwei Zhou, Tao Wang, Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc Le, Yonghui Wu, Zhifeng Chen, Claire Cui

AAAI 2022 Identification of Linear Latent Variable Model with Arbitrary Distribution Zhengming Chen, Feng Xie, Jie Qiao, Zhifeng Hao, Kun Zhang, Ruichu Cai

ICML 2022 Identification of Linear Non-Gaussian Latent Hierarchical Structure Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang

NeurIPS 2022 Independence Testing-Based Approach to Causal Discovery Under Measurement Error and Linear Non-Gaussian Models Haoyue Dai, Peter Spirtes, Kun Zhang

AAAI 2022 Invariant Action Effect Model for Reinforcement Learning Zheng-Mao Zhu, Shengyi Jiang, Yu-Ren Liu, Yang Yu, Kun Zhang

NeurIPS 2022 Latent Hierarchical Causal Structure Discovery with Rank Constraints Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang

ICLR 2022 Learning Temporally Causal Latent Processes from General Temporal Data Weiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, Kun Zhang

CVPR 2022 Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong, Kayhan Batmanghelich

NeurIPS 2022 MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard Bondell

CVPR 2022 Negative-Aware Attention Framework for Image-Text Matching Kun Zhang, Zhendong Mao, Quan Wang, Yongdong Zhang

ICLRW 2022 On the Identifiability of Nonlinear ICA with Unconditional Priors Yujia Zheng, Ignavier Ng, Kun Zhang

NeurIPS 2022 On the Identifiability of Nonlinear ICA: Sparsity and Beyond Yujia Zheng, Ignavier Ng, Kun Zhang

ICLR 2022 Optimal Transport for Causal Discovery Ruibo Tu, Kun Zhang, Hedvig Kjellstrom, Cheng Zhang

ICML 2022 Partial Disentanglement for Domain Adaptation Lingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang

AAAI 2022 Residual Similarity Based Conditional Independence Test and Its Application in Causal Discovery Hao Zhang, Shuigeng Zhou, Kun Zhang, Jihong Guan

NeurIPSW 2022 Scalable Causal Discovery with Score Matching Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello

AAAI 2022 Show Your Faith: Cross-Modal Confidence-Aware Network for Image-Text Matching Huatian Zhang, Zhendong Mao, Kun Zhang, Yongdong Zhang

NeurIPS 2022 Temporally Disentangled Representation Learning Weiran Yao, Guangyi Chen, Kun Zhang

NeurIPS 2022 Truncated Matrix Power Iteration for Differentiable DAG Learning Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Ehsan Abbasnejad, Mingming Gong, Kun Zhang, Javen Qinfeng Shi

NeurIPS 2022 Unsupervised Image-to-Image Translation with Density Changing Regularization Shaoan Xie, Qirong Ho, Kun Zhang

ACML 2021 $K^2$-GNN: Multiple Users’ Comments Integration with Probabilistic K-Hop Knowledge Graph Neural Networks Huixin Zhan, Kun Zhang, Chenyi Hu, Victor Sheng

ICCV 2021 DAE-GAN: Dynamic Aspect-Aware GAN for Text-to-Image Synthesis Shulan Ruan, Yong Zhang, Kun Zhang, Yanbo Fan, Fan Tang, Qi Liu, Enhong Chen

AAAI 2021 DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding Zhicheng Wang, Biwei Huang, Shikui Tu, Kun Zhang, Lei Xu

NeurIPS 2021 Domain Adaptation with Invariant Representation Learning: What Transformations to Learn? Petar Stojanov, Zijian Li, Mingming Gong, Ruichu Cai, Jaime G. Carbonell, Kun Zhang

NeurIPS 2021 Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases Jeffrey Adams, Niels Hansen, Kun Zhang

AAAI 2021 Ideography Leads Us to the Field of Cognition: A Radical-Guided Associative Model for Chinese Text Classification Hanqing Tao, Shiwei Tong, Kun Zhang, Tong Xu, Qi Liu, Enhong Chen, Min Hou

AAAI 2021 Improving Causal Discovery by Optimal Bayesian Network Learning Ni Y. Lu, Kun Zhang, Changhe Yuan

NeurIPS 2021 Instance-Dependent Label-Noise Learning Under a Structural Causal Model Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang

AAAI 2021 Making the Relation Matters: Relation of Relation Learning Network for Sentence Semantic Matching Kun Zhang, Le Wu, Guangyi Lv, Meng Wang, Enhong Chen, Shulan Ruan

IJCAI 2021 Progressive Open-Domain Response Generation with Multiple Controllable Attributes Haiqin Yang, Xiaoyuan Yao, Yiqun Duan, Jianping Shen, Jie Zhong, Kun Zhang

NeurIPS 2021 Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang

AAAI 2021 Testing Independence Between Linear Combinations for Causal Discovery Hao Zhang, Kun Zhang, Shuigeng Zhou, Jihong Guan, Ji Zhang

ICCV 2021 Unaligned Image-to-Image Translation by Learning to Reweight Shaoan Xie, Mingming Gong, Yanwu Xu, Kun Zhang

NeurIPS 2020 A Causal View on Robustness of Neural Networks Cheng Zhang, Kun Zhang, Yingzhen Li

JMLR 2020 Causal Discovery from Heterogeneous/Nonstationary Data Biwei Huang, Kun Zhang, Jiji Zhang, Joseph Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf

AAAI 2020 Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour

ICMLW 2020 Causal Discovery in the Presence of Missing Values for Neuropathic Pain Diagnosis Ruibo Tu, Kun Zhang, Bo Christer Bertilson, Clark Glymour, Hedvig Kjellström, Cheng Zhang

ICML 2020 Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs Amiremad Ghassami, Alan Yang, Negar Kiyavash, Kun Zhang

AAAI 2020 Compressed Self-Attention for Deep Metric Learning Ziye Chen, Mingming Gong, Yanwu Xu, Chaohui Wang, Kun Zhang, Bo Du

NeurIPS 2020 Domain Adaptation as a Problem of Inference on Graphical Models Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Qingsong Liu, Clark Glymour

AAAI 2020 Gated Convolutional Networks with Hybrid Connectivity for Image Classification Chuanguang Yang, Zhulin An, Hui Zhu, Xiaolong Hu, Kun Zhang, Kaiqiang Xu, Chao Li, Yongjun Xu

NeurIPS 2020 Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang

AAAI 2020 Generative-Discriminative Complementary Learning Yanwu Xu, Mingming Gong, Junxiang Chen, Tongliang Liu, Kun Zhang, Kayhan Batmanghelich

NeurIPS 2020 How Do Fair Decisions Fare in Long-Term Qualification? Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellstrom, Kun Zhang, Cheng Zhang

ICML 2020 LTF: A Label Transformation Framework for Correcting Label Shift Jiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang, Dacheng Tao

ICML 2020 Label-Noise Robust Domain Adaptation Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, Dacheng Tao

JMLR 2020 Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, Kun Zhang

NeurIPS 2020 On the Role of Sparsity and DAG Constraints for Learning Linear DAGs Ignavier Ng, AmirEmad Ghassami, Kun Zhang

AAAI 2020 PIDS: An Intelligent Electric Power Management Platform Yongqing Zheng, Han Yu, Yuliang Shi, Kun Zhang, Shuai Zhen, Lizhen Cui, Cyril Leung, Chunyan Miao

AAAI 2020 Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach Lei Chen, Le Wu, Richang Hong, Kun Zhang, Meng Wang

ICML 2019 Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour

AISTATS 2019 Causal Discovery in the Presence of Missing Data Ruibo Tu, Cheng Zhang, Paul Ackermann, Karthika Mohan, Hedvig Kjellström, Kun Zhang

IJCAI 2019 Causal Discovery with Cascade Nonlinear Additive Noise Model Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao

UAI 2019 Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA Ricardo Pio Monti, Kun Zhang, Aapo Hyvärinen

AAAI 2019 Counting and Sampling from Markov Equivalent DAGs Using Clique Trees AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang

AAAI 2019 DRr-Net: Dynamic Re-Read Network for Sentence Semantic Matching Kun Zhang, Guangyi Lv, Linyuan Wang, Le Wu, Enhong Chen, Fangzhao Wu, Xing Xie

AISTATS 2019 Data-Driven Approach to Multiple-Source Domain Adaptation Petar Stojanov, Mingming Gong, Jaime Carbonell, Kun Zhang

UAI 2019 Domain Generalization via Multidomain Discriminant Analysis Shoubo Hu, Kun Zhang, Zhitang Chen, Laiwan Chan

ICCVW 2019 Factorizing and Reconstituting Large-Kernel MBConv for Lightweight Face Recognition Yaqi Lyu, Jing Jiang, Kun Zhang, Yilun Hua, Miao Cheng

IJCAI 2019 Intelligent Decision Support for Improving Power Management Yongqing Zheng, Han Yu, Kun Zhang, Yuliang Shi, Cyril Leung, Chunyan Miao

IJCAI 2019 Learning Disentangled Semantic Representation for Domain Adaptation Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao

NeurIPS 2019 Likelihood-Free Overcomplete ICA and Applications in Causal Discovery Chenwei Ding, Mingming Gong, Kun Zhang, Dacheng Tao

AISTATS 2019 Low-Dimensional Density Ratio Estimation for Covariate Shift Correction Petar Stojanov, Mingming Gong, Jaime Carbonell, Kun Zhang

NeurIPS 2019 Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation Ruibo Tu, Kun Zhang, Bo Bertilson, Hedvig Kjellstrom, Cheng Zhang

ICML 2019 On Learning Invariant Representations for Domain Adaptation Han Zhao, Remi Tachet Des Combes, Kun Zhang, Geoffrey Gordon

NeurIPS 2019 Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric P Xing, Clark Glymour

NeurIPS 2019 Triad Constraints for Learning Causal Structure of Latent Variables Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang

NeurIPS 2019 Twin Auxilary Classifiers GAN Mingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang, Kayhan Batmanghelich

NeurIPS 2018 Causal Discovery from Discrete Data Using Hidden Compact Representation Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao

UAI 2018 Causal Discovery with Linear Non-Gaussian Models Under Measurement Error: Structural Identifiability Results Kun Zhang, Mingming Gong, Joseph D. Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour

AAAI 2018 Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation Menghan Wang, Xiaolin Zheng, Yang Yang, Kun Zhang

ECCV 2018 Deep Domain Generalization via Conditional Invariant Adversarial Networks Ya Li, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, Dacheng Tao

AAAI 2018 Learning Vector Autoregressive Models with Latent Processes Saber Salehkaleybar, Jalal Etesami, Negar Kiyavash, Kun Zhang

NeurIPS 2018 Modeling Dynamic Missingness of Implicit Feedback for Recommendation Menghan Wang, Mingming Gong, Xiaolin Zheng, Kun Zhang

NeurIPS 2018 Multi-Domain Causal Structure Learning in Linear Systems AmirEmad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang

AAAI 2017 A Context-Enriched Neural Network Method for Recognizing Lexical Entailment Kun Zhang, Enhong Chen, Qi Liu, Chuanren Liu, Guangyi Lv

AAAI 2017 Causal Discovery Using Regression-Based Conditional Independence Tests Hao Zhang, Shuigeng Zhou, Kun Zhang, Jihong Guan

IJCAI 2017 Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination Kun Zhang, Biwei Huang, Jiji Zhang, Clark Glymour, Bernhard Schölkopf

UAI 2017 Causal Discovery from Temporally Aggregated Time Series Mingming Gong, Kun Zhang, Bernhard Schölkopf, Clark Glymour, Dacheng Tao

NeurIPS 2017 Learning Causal Structures Using Regression Invariance AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang

ICML 2016 Domain Adaptation with Conditional Transferable Components Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, Bernhard Schölkopf

UAI 2016 Learning Network of Multivariate Hawkes Processes: A Time Series Approach Jalal Etesami, Negar Kiyavash, Kun Zhang, Kushagra Singhal

UAI 2016 On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection Kun Zhang, Jiji Zhang, Biwei Huang, Bernhard Schölkopf, Clark Glymour

ICML 2015 Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components Philipp Geiger, Kun Zhang, Bernhard Schoelkopf, Mingming Gong, Dominik Janzing

ICML 2015 Discovering Temporal Causal Relations from Subsampled Data Mingming Gong, Kun Zhang, Bernhard Schoelkopf, Dacheng Tao, Philipp Geiger

IJCAI 2015 Identification of Time-Dependent Causal Model: A Gaussian Process Treatment Biwei Huang, Kun Zhang, Bernhard Schölkopf

AAAI 2015 Multi-Source Domain Adaptation: A Causal View Kun Zhang, Mingming Gong, Bernhard Schölkopf

UAI 2014 A Permutation-Based Kernel Conditional Independence Test Gary Doran, Krikamol Muandet, Kun Zhang, Bernhard Schölkopf

AAAI 2014 Cross-Lingual Knowledge Validation Based Taxonomy Derivation from Heterogeneous Online Wikis Zhigang Wang, Juanzi Li, Shuangjie Li, Mingyang Li, Jie Tang, Kuo Zhang, Kun Zhang

ICML 2013 Domain Adaptation Under Target and Conditional Shift Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, Zhikun Wang

NeurIPS 2012 Causal Discovery with Scale-Mixture Model for Spatiotemporal Variance Dependencies Zhitang Chen, Kun Zhang, Laiwan Chan

ICML 2012 On Causal and Anticausal Learning Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris M. Mooij

ACML 2011 A General Linear Non-Gaussian State-Space Model: Identifiability, Identification, and Applications Kun Zhang, Aapo Hyvärinen

UAI 2011 Kernel-Based Conditional Independence Test and Application in Causal Discovery Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schölkopf

UAI 2011 Testing Whether Linear Equations Are Causal: A Free Probability Theory Approach Jakob Zscheischler, Dominik Janzing, Kun Zhang

JMLR 2010 Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer

UAI 2010 Inferring Deterministic Causal Relations Povilas Daniusis, Dominik Janzing, Joris M. Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schölkopf

UAI 2010 Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery Kun Zhang, Bernhard Schölkopf, Dominik Janzing

NeurIPS 2010 Probabilistic Latent Variable Models for Distinguishing Between Cause and Effect Oliver Stegle, Dominik Janzing, Kun Zhang, Joris M. Mooij, Bernhard Schölkopf

UAI 2010 Source Separation and Higher-Order Causal Analysis of MEG and EEG Kun Zhang, Aapo Hyvärinen

ECML-PKDD 2009 Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective Kun Zhang, Aapo Hyvärinen

UAI 2009 On the Identifiability of the Post-Nonlinear Causal Model Kun Zhang, Aapo Hyvärinen

JMLR 2008 Minimal Nonlinear Distortion Principle for Nonlinear Independent Component Analysis Kun Zhang, Laiwan Chan

ICML 2007 Nonlinear Independent Component Analysis with Minimal Nonlinear Distortion Kun Zhang, Laiwan Chan