Dimension Reduction Based on Orthogonality - A Decorrelation Method in ICA. - Kun Zhang 001; Lai-Wan Chan (ICANN, 2003)

Practical method for blind inversion of Wiener systems. - Kun Zhang 001; Lai-Wan Chan (IJCNN, 2004)

To apply score function difference based ICA algorithms to high-dimensional data. - Kun Zhang 001; Lai-Wan Chan (ESANN, 2005)

Extended Gaussianization Method for Blind Separation of Post-Nonlinear Mixtures. - Kun Zhang 001; Lai-Wan Chan (Neural Comput., 2005)

Extensions of independent component analysis: towards applications. - Kun Zhang 001 (2005)

ICA by PCA Approach: Relating Higher-Order Statistics to Second-Order Moments. - Kun Zhang 001; Lai-Wan Chan (ICA, 2006)

Enhancement of Source Independence for Blind Source Separation. - Kun Zhang 001; Lai-Wan Chan (ICA, 2006)

Extensions of ICA for Causality Discovery in the Hong Kong Stock Market. - Kun Zhang 001; Lai-Wan Chan (ICONIP (3), 2006)

ICA with Sparse Connections. - Kun Zhang 001; Lai-Wan Chan (IDEAL, 2006)

An Adaptive Method for Subband Decomposition ICA. - Kun Zhang 001; Lai-Wan Chan (Neural Comput., 2006)

Symbol Recognition with Kernel Density Matching. - Wan Zhang; Liu Wenyin; Kun Zhang 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2006)

Dimension reduction as a deflation method in ICA. - Kun Zhang 001; Lai-Wan Chan (IEEE Signal Process. Lett., 2006)

Kernel-Based Nonlinear Independent Component Analysis. - Kun Zhang 001; Laiwan Chan (ICA, 2007)

Nonlinear independent component analysis with minimal nonlinear distortion. - Kun Zhang 001; Laiwan Chan (ICML, 2007)

Independent Factor Reinforcement Learning for Portfolio Management. - Jian Li; Kun Zhang 001; Laiwan Chan (IDEAL, 2007)

Separating Convolutive Mixtures By Pairwise Mutual Information Minimization. - Kun Zhang 001; Laiwan Chan (IEEE Signal Process. Lett., 2007)

ICA with Sparse Connections: Revisited. - Kun Zhang 001; Heng Peng; Laiwan Chan; Aapo Hyvärinen (ICA, 2009)

Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective. - Kun Zhang 001; Aapo Hyvärinen (ECML/PKDD (2), 2009)

On the Identifiability of the Post-Nonlinear Causal Model. - Kun Zhang 001; Aapo Hyvärinen (UAI, 2009)

Multi-label learning by exploiting label dependency. - Min-Ling Zhang; Kun Zhang 001 (KDD, 2010)

Probabilistic latent variable models for distinguishing between cause and effect. - Joris M. Mooij; Oliver Stegle; Dominik Janzing; Kun Zhang 001; Bernhard Schölkopf (NIPS, 2010)

Inferring deterministic causal relations. - Povilas Daniusis; Dominik Janzing; Joris M. Mooij; Jakob Zscheischler; Bastian Steudel; Kun Zhang 001; Bernhard Schölkopf (UAI, 2010)

Source Separation and Higher-Order Causal Analysis of MEG and EEG. - Kun Zhang 001; Aapo Hyvärinen (UAI, 2010)

Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery. - Kun Zhang 001; Bernhard Schölkopf; Dominik Janzing (UAI, 2010)

Convolutive blind source separation by efficient blind deconvolution and minimal filter distortion. - Kun Zhang 001; Lai-Wan Chan (Neurocomputing, 2010)

Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. - Aapo Hyvärinen; Kun Zhang 001; Shohei Shimizu; Patrik O. Hoyer (J. Mach. Learn. Res., 2010)

Nonlinear acyclic causal models. - Kun Zhang 001; Aapo Hyvärinen (NIPS Causality: Objectives and Assessment, 2010)

Kernel-based Conditional Independence Test and Application in Causal Discovery. - Kun Zhang 001; 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 001 (UAI, 2011)

Robust Learning via Cause-Effect Models - Bernhard Schölkopf; Dominik Janzing; Jonas Peters; Kun Zhang 001 (CoRR, 2011)

A General Linear Non-Gaussian State-Space Model. - Kun Zhang 001; Aapo Hyvärinen (ACML, 2011)

On causal and anticausal learning. - Bernhard Schölkopf; Dominik Janzing; Jonas Peters; Eleni Sgouritsa; Kun Zhang 001; Joris M. Mooij (ICML, 2012)

Causal discovery with scale-mixture model for spatiotemporal variance dependencies. - Zhitang Chen; Kun Zhang 001; Laiwan Chan (NIPS, 2012)

Information-geometric approach to inferring causal directions. - Dominik Janzing; Joris M. Mooij; Kun Zhang 001; Jan Lemeire; Jakob Zscheischler; Povilas Daniusis; Bastian Steudel; Bernhard Schölkopf (Artif. Intell., 2012)

Semi-supervised Learning in Causal and Anticausal Settings. - Bernhard Schölkopf; Dominik Janzing; Jonas Peters; Eleni Sgouritsa; Kun Zhang 001; Joris M. Mooij (Empirical Inference, 2013)

Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method. - Zhitang Chen; Kun Zhang 001; Laiwan Chan (ICDM, 2013)

On Estimation of Functional Causal Models: Post-Nonlinear Causal Model as an Example. - Kun Zhang 001; Zhikun Wang; Bernhard Schölkopf (ICDM Workshops, 2013)

Domain Adaptation under Target and Conditional Shift. - Kun Zhang 001; Bernhard Schölkopf; Krikamol Muandet; Zhikun Wang (ICML (3), 2013)

Bridging Information Criteria and Parameter Shrinkage for Model Selection. - Kun Zhang 001; Heng Peng; Laiwan Chan; Aapo Hyvärinen (CoRR, 2013)

A Permutation-Based Kernel Conditional Independence Test. - Gary Doran; Krikamol Muandet; Kun Zhang 001; Bernhard Schölkopf (UAI, 2014)

Causal Discovery via Reproducing Kernel Hilbert Space Embeddings. - Zhitang Chen; Kun Zhang 001; Laiwan Chan; Bernhard Schölkopf (Neural Comput., 2014)

Multi-Source Domain Adaptation: A Causal View. - Kun Zhang 001; Mingming Gong; Bernhard Schölkopf (AAAI, 2015)

Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components. - Philipp Geiger; Kun Zhang 001; Bernhard Schölkopf; Mingming Gong; Dominik Janzing (ICML, 2015)

Discovering Temporal Causal Relations from Subsampled Data. - Mingming Gong; Kun Zhang 001; Bernhard Schölkopf; Dacheng Tao; Philipp Geiger (ICML, 2015)

Identification of Time-Dependent Causal Model: A Gaussian Process Treatment. - Biwei Huang; Kun Zhang 001; Bernhard Schölkopf (IJCAI, 2015)

Towards Robust and Specific Causal Discovery from fMRI. - Kun Zhang 001; Biwei Huang; Bernhard Schölkopf; Michel Besserve; Masataka Watanabe; Dajiang Zhu (CoRR, 2015)

Distinguishing Cause from Effect Based on Exogeneity. - Kun Zhang 001; Jiji Zhang; Bernhard Schölkopf (CoRR, 2015)

Domain Adaptation with Conditional Transferable Components. - Mingming Gong; Kun Zhang 001; Tongliang Liu; Dacheng Tao; Clark Glymour; Bernhard Schölkopf (ICML, 2016)

Learning Network of Multivariate Hawkes Processes: A Time Series Approach. - Jalal Etesami; Negar Kiyavash; Kun Zhang 001; Kushagra Singhal (UAI, 2016)

On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection. - Kun Zhang 001; Jiji Zhang; Biwei Huang; Bernhard Schölkopf; Clark Glymour (UAI, 2016)

Preface to the ACM TIST Special Issue on Causal Discovery and Inference. - Kun Zhang 001; Jiuyong Li; Elias Bareinboim; Bernhard Schölkopf; Judea Pearl (ACM Trans. Intell. Syst. Technol., 2016)

On Estimation of Functional Causal Models: General Results and Application to the Post-Nonlinear Causal Model. - Kun Zhang 001; Zhikun Wang; Jiji Zhang; Bernhard Schölkopf (ACM Trans. Intell. Syst. Technol., 2016)

Causal Discovery Using Regression-Based Conditional Independence Tests. - Hao Zhang 079; Shuigeng Zhou; Kun Zhang 001; Jihong Guan (AAAI, 2017)

Causal Learning and Machine Learning. - Kun Zhang 001 (AMBN, 2017)

Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows. - Biwei Huang; Kun Zhang 001; Jiji Zhang; Ruben Sanchez-Romero; Clark Glymour; Bernhard Schölkopf (ICDM, 2017)

Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination. - Kun Zhang 001; Biwei Huang; Jiji Zhang; Clark Glymour; Bernhard Schölkopf (IJCAI, 2017)

Learning Causal Structures Using Regression Invariance. - AmirEmad Ghassami; Saber Salehkaleybar; Negar Kiyavash; Kun Zhang 001 (NIPS, 2017)

Causal Discovery from Temporally Aggregated Time Series. - Mingming Gong; Kun Zhang 001; Bernhard Schölkopf; Clark Glymour; Dacheng Tao (UAI, 2017)

A New Measure of Conditional Dependence for Causal Structural Learning. - Jalal Etesami; Kun Zhang 001; Negar Kiyavash (CoRR, 2017)

Causal Discovery in the Presence of Measurement Error: Identifiability Conditions. - Kun Zhang 001; Mingming Gong; Joseph D. Ramsey; Kayhan Batmanghelich; Peter Spirtes; Clark Glymour (CoRR, 2017)

Causality Refined Diagnostic Prediction. - Marcus Klasson; Kun Zhang 001; Bo C. Bertilson; Cheng Zhang 005; Hedvig Kjellström (CoRR, 2017)

Guest editorial: special issue on causal discovery. - Jiuyong Li; Kun Zhang 001; Elias Bareinboim; Lin Liu 003 (Int. J. Data Sci. Anal., 2017)

Learning Vector Autoregressive Models With Latent Processes. - Saber Salehkaleybar; Jalal Etesami; Negar Kiyavash; Kun Zhang 001 (AAAI, 2018)

Collaborative Filtering With Social Exposure: A Modular Approach to Social Recommendation. - Menghan Wang; Xiaolin Zheng; Yang Yang; Kun Zhang 001 (AAAI, 2018)

Deep Domain Generalization via Conditional Invariant Adversarial Networks. - Ya Li; Xinmei Tian 001; Mingming Gong; Yajing Liu; Tongliang Liu; Kun Zhang 001; Dacheng Tao (ECCV (15), 2018)

Generalized Score Functions for Causal Discovery. - Biwei Huang; Kun Zhang 001; Yizhu Lin; Bernhard Schölkopf; Clark Glymour (KDD, 2018)

Preface: The 2018 ACM SIGKDD Workshop on Causal Discovery. - Thuc Duy Le; Kun Zhang 001; Emre Kiciman; Aapo Hyvärinen; Lin Liu 003 (CD@KDD, 2018)

Multi-scale Masked 3-D U-Net for Brain Tumor Segmentation. - Yanwu Xu 001; Mingming Gong; Huan Fu; Dacheng Tao; Kun Zhang 001; Kayhan Batmanghelich (BrainLes@MICCAI (2), 2018)

Causal Discovery from Discrete Data using Hidden Compact Representation. - Ruichu Cai; Jie Qiao; Kun Zhang 001; Zhenjie Zhang; Zhifeng Hao (NeurIPS, 2018)

Multi-domain Causal Structure Learning in Linear Systems. - AmirEmad Ghassami; Negar Kiyavash; Biwei Huang; Kun Zhang 001 (NeurIPS, 2018)

Modeling Dynamic Missingness of Implicit Feedback for Recommendation. - Menghan Wang; Mingming Gong; Xiaolin Zheng; Kun Zhang 001 (NeurIPS, 2018)

Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. - Kun Zhang 001; Mingming Gong; Joseph D. Ramsey; Kayhan Batmanghelich; Peter Spirtes; Clark Glymour (UAI, 2018)

Causal Generative Domain Adaptation Networks. - Mingming Gong; Kun Zhang 001; Biwei Huang; Clark Glymour; Dacheng Tao; Kayhan Batmanghelich (CoRR, 2018)

User-Sensitive Recommendation Ensemble with Clustered Multi-Task Learning. - Menghan Wang; Xiaolin Zheng; Kun Zhang 001 (CoRR, 2018)

Causal discovery in the presence of missing data. - Ruibo Tu; Cheng Zhang 005; Paul Ackermann; Hedvig Kjellström; Kun Zhang 001 (CoRR, 2018)

Geometry-Consistent Adversarial Networks for One-Sided Unsupervised Domain Mapping. - Huan Fu; Mingming Gong; Chaohui Wang; Kayhan Batmanghelich; Kun Zhang 001; Dacheng Tao (CoRR, 2018)

Guest Editorial: Special Issue on Causal Discovery 2017. - Lin Liu 003; Jiuyong Li; Kun Zhang 001; Emre Kiciman; Negar Kiyavash (Int. J. Data Sci. Anal., 2018)

Counting and Sampling from Markov Equivalent DAGs Using Clique Trees. - AmirEmad Ghassami; Saber Salehkaleybar; Negar Kiyavash; Kun Zhang 001 (AAAI, 2019)

Low-Dimensional Density Ratio Estimation for Covariate Shift Correction. - Petar Stojanov; Mingming Gong; Jaime G. Carbonell; Kun Zhang 001 (AISTATS, 2019)

Data-Driven Approach to Multiple-Source Domain Adaptation. - Petar Stojanov; Mingming Gong; Jaime G. Carbonell; Kun Zhang 001 (AISTATS, 2019)

Causal Discovery in the Presence of Missing Data. - Ruibo Tu; Cheng Zhang 005; Paul Ackermann; Karthika Mohan; Hedvig Kjellström; Kun Zhang 001 (AISTATS, 2019)

PRNet: Outdoor Position Recovery for Heterogenous Telco Data by Deep Neural Network. - Yige Zhang; Weixiong Rao; Kun Zhang 001; Mingxuan Yuan; Jia Zeng (CIKM, 2019)

Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping. - Huan Fu; Mingming Gong; Chaohui Wang; Kayhan Batmanghelich; Kun Zhang 001; Dacheng Tao (CVPR, 2019)

On Learning Invariant Representations for Domain Adaptation. - Han Zhao 002; Remi Tachet des Combes; Kun Zhang 001; Geoffrey J. Gordon (ICML, 2019)

Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. - Biwei Huang; Kun Zhang 001; Mingming Gong; Clark Glymour (ICML, 2019)

Learning Disentangled Semantic Representation for Domain Adaptation. - Ruichu Cai; Zijian Li 001; Pengfei Wei 001; Jie Qiao; Kun Zhang 001; Zhifeng Hao (IJCAI, 2019)

Causal Discovery with Cascade Nonlinear Additive Noise Model. - Ruichu Cai; Jie Qiao; Kun Zhang 001; Zhenjie Zhang; Zhifeng Hao (IJCAI, 2019)

Preface: The 2019 ACM SIGKDD Workshop on Causal Discovery. - Thuc Duy Le; Jiuyong Li; Kun Zhang 001; Emre Kiciman; Peng Cui 001; Aapo Hyvärinen (CD@KDD, 2019)

Triad Constraints for Learning Causal Structure of Latent Variables. - Ruichu Cai; Feng Xie 002; Clark Glymour; Zhifeng Hao; Kun Zhang 001 (NeurIPS, 2019)

Likelihood-Free Overcomplete ICA and Applications In Causal Discovery. - Chenwei Ding; Mingming Gong; Kun Zhang 001; Dacheng Tao (NeurIPS, 2019)

Twin Auxilary Classifiers GAN. - Mingming Gong; Yanwu Xu 003; Chunyuan Li; Kun Zhang 001; Kayhan Batmanghelich (NeurIPS, 2019)

Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering. - Biwei Huang; Kun Zhang 001; Pengtao Xie; Mingming Gong; Eric P. Xing; Clark Glymour (NeurIPS, 2019)

Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation. - Ruibo Tu; Kun Zhang 001; Bo C. Bertilson; Hedvig Kjellström; Cheng Zhang 005 (NeurIPS, 2019)

Domain Generalization via Multidomain Discriminant Analysis. - Shoubo Hu; Kun Zhang 001; Zhitang Chen; Laiwan Chan (UAI, 2019)

Causal Discovery with General Non-Linear Relationships using Non-Linear ICA. - Ricardo Pio Monti; Kun Zhang 001; Aapo Hyvärinen (UAI, 2019)

On Learning Invariant Representation for Domain Adaptation. - Han Zhao 002; Remi Tachet des Combes; Kun Zhang 001; Geoffrey J. Gordon (CoRR, 2019)

Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data. - Biwei Huang; Kun Zhang 001; Ruben Sanchez-Romero; Joseph D. Ramsey; Madelyn Glymour; Clark Glymour (CoRR, 2019)

Causal Discovery with Cascade Nonlinear Additive Noise Models. - Ruichu Cai; Jie Qiao; Kun Zhang 001; Zhenjie Zhang; Zhifeng Hao (CoRR, 2019)

Twin Auxiliary Classifiers GAN. - Mingming Gong; Yanwu Xu 003; Chunyuan Li; Kun Zhang 001; Kayhan Batmanghelich (CoRR, 2019)

Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach. - Yipeng Mou; Mingming Gong; Huan Fu; Kayhan Batmanghelich; Kun Zhang 001; Dacheng Tao (CoRR, 2019)

Identification of Effective Connectivity Subregions. - Ruben Sanchez-Romero; Joseph D. Ramsey; Kun Zhang 001; Clark Glymour (CoRR, 2019)

Characterizing Distribution Equivalence for Cyclic and Acyclic Directed Graphs. - AmirEmad Ghassami; Kun Zhang 001; Negar Kiyavash (CoRR, 2019)

Disentanglement Challenge: From Regularization to Reconstruction. - Jie Qiao; Zijian Li 001; Boyan Xu; Ruichu Cai; Kun Zhang 001 (CoRR, 2019)

Transfer Learning-Based Outdoor Position Recovery with Telco Data. - Yige Zhang; Aaron Yi Ding; Jörg Ott; Mingxuan Yuan; Jia Zeng; Kun Zhang 001; Weixiong Rao (CoRR, 2019)

Tracing the Propagation Path: A Flow Perspective of Representation Learning on Graphs. - Menghan Wang; Kun Zhang 001; Gulin Li; Keping Yang; Luo Si (CoRR, 2019)

Introduction to the Special Section on Advances in Causal Discovery and Inference. - Jiuyong Li; Kun Zhang 001; Emre Kiciman; Peng Cui 001 (ACM Trans. Intell. Syst. Technol., 2019)

Compressed Self-Attention for Deep Metric Learning. - Ziye Chen; Mingming Gong; Yanwu Xu 003; Chaohui Wang; Kun Zhang 001; Bo Du 001 (AAAI, 2020)

Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets. - Biwei Huang; Kun Zhang 001; Mingming Gong; Clark Glymour (AAAI, 2020)

Generative-Discriminative Complementary Learning. - Yanwu Xu 003; Mingming Gong; Junxiang Chen; Tongliang Liu; Kun Zhang 001; Kayhan Batmanghelich (AAAI, 2020)

Adaptive Task Sampling for Meta-learning. - Chenghao Liu; Zhihao Wang 004; Doyen Sahoo; Yuan Fang 001; Kun Zhang 001; Steven C. H. Hoi (ECCV (18), 2020)

Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs. - AmirEmad Ghassami; Alan Yang; Negar Kiyavash; Kun Zhang 001 (ICML, 2020)

LTF: A Label Transformation Framework for Correcting Label Shift. - Jiaxian Guo; Mingming Gong; Tongliang Liu; Kun Zhang 001; Dacheng Tao (ICML, 2020)

Label-Noise Robust Domain Adaptation. - Xiyu Yu; Tongliang Liu; Mingming Gong; Kun Zhang 001; Kayhan Batmanghelich; Dacheng Tao (ICML, 2020)

Preface: The 2020 ACM SIGKDD Workshop on Causal Discovery. - Thuc Duy Le; Lin Liu 003; Kun Zhang 001; Emre Kiciman; Peng Cui 001; Aapo Hyvärinen (CD@KDD, 2020)

Domain Adaptation as a Problem of Inference on Graphical Models. - Kun Zhang 001; Mingming Gong; Petar Stojanov; Biwei Huang; Qingsong Liu; Clark Glymour (NeurIPS, 2020)

Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. - Feng Xie 002; Ruichu Cai; Biwei Huang; Clark Glymour; Zhifeng Hao; Kun Zhang 001 (NeurIPS, 2020)

A Causal View on Robustness of Neural Networks. - Cheng Zhang 005; Kun Zhang 001; Yingzhen Li (NeurIPS, 2020)

On the Role of Sparsity and DAG Constraints for Learning Linear DAGs. - Ignavier Ng; AmirEmad Ghassami; Kun Zhang 001 (NeurIPS, 2020)

How do fair decisions fare in long-term qualification? - Xueru Zhang; Ruibo Tu; Yang Liu 018; Mingyan Liu; Hedvig Kjellström; Kun Zhang 001; Cheng Zhang 005 (NeurIPS, 2020)

Domain Adaptation As a Problem of Inference on Graphical Models. - Kun Zhang 001; Mingming Gong; Petar Stojanov; Biwei Huang; Clark Glymour (CoRR, 2020)

Learning from Positive and Unlabeled Data by Identifying the Annotation Process. - Naji Shajarisales; Peter Spirtes; Kun Zhang 001 (CoRR, 2020)

Attack on Multi-Node Attention for Object Detection. - Sizhe Chen; Fan He; Xiaolin Huang; Kun Zhang 001 (CoRR, 2020)

Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs. - Feng Xie 002; Ruichu Cai; Biwei Huang; Clark Glymour; Zhifeng Hao; Kun Zhang 001 (CoRR, 2020)

Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation. - Chaochao Lu; Biwei Huang; Ke Wang; José Miguel Hernández-Lobato; Kun Zhang 001; Bernhard Schölkopf (CoRR, 2020)

Causal Discovery from Heterogeneous/Nonstationary Data. - Biwei Huang; Kun Zhang 001; Jiji Zhang; Joseph D. Ramsey; Ruben Sanchez-Romero; Clark Glymour; Bernhard Schölkopf (J. Mach. Learn. Res., 2020)

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables. - Saber Salehkaleybar; AmirEmad Ghassami; Negar Kiyavash; Kun Zhang 001 (J. Mach. Learn. Res., 2020)

Multiple player tracking in basketball court videos. - Xubo Fu; Kun Zhang 001; Changgang Wang; Chao Fan (J. Real Time Image Process., 2020)

Improving Causal Discovery By Optimal Bayesian Network Learning. - Ni Y. Lu; Kun Zhang 001; Changhe Yuan (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 001; Lei Xu 001 (AAAI, 2021)

Testing Independence Between Linear Combinations for Causal Discovery. - Hao Zhang 079; Kun Zhang 001; Shuigeng Zhou; Jihong Guan; Ji Zhang 001 (AAAI, 2021)

Unaligned Image-to-Image Translation by Learning to Reweight. - Shaoan Xie; Mingming Gong; Yanwu Xu 003; Kun Zhang 001 (ICCV, 2021)

Progressive Open-Domain Response Generation with Multiple Controllable Attributes. - Haiqin Yang; Xiaoyuan Yao; Yiqun Duan; Jianping Shen; Jie Zhong; Kun Zhang 001 (IJCAI, 2021)

Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases. - Jeffrey Adams; Niels Hansen; Kun Zhang 001 (NeurIPS, 2021)

Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions. - Ignavier Ng; Yujia Zheng 001; Jiji Zhang; Kun Zhang 001 (NeurIPS, 2021)

Domain Adaptation with Invariant Representation Learning: What Transformations to Learn? - Petar Stojanov; Zijian Li 001; Mingming Gong; Ruichu Cai; Jaime G. Carbonell; Kun Zhang 001 (NeurIPS, 2021)

Instance-dependent Label-noise Learning under a Structural Causal Model. - Yu Yao 005; Tongliang Liu; Mingming Gong; Bo Han 003; Gang Niu 001; Kun Zhang 001 (NeurIPS, 2021)

Unpaired data empowers association tests. - Mingming Gong; Peng Liu; Frank C. Sciurba; Petar Stojanov; Dacheng Tao; George C. Tseng; Kun Zhang 001; Kayhan Batmanghelich (Bioinform., 2021)

FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders. - Wei Chen 103; Kun Zhang 001; Ruichu Cai; Biwei Huang; Joseph D. Ramsey; Zhifeng Hao; Clark Glymour (CoRR, 2021)

Conditional Contrastive Learning: Removing Undesirable Information in Self-Supervised Representations. - Yao-Hung Hubert Tsai; Martin Q. Ma; Han Zhao 002; Kun Zhang 001; Louis-Philippe Morency; Ruslan Salakhutdinov (CoRR, 2021)

Outdoor Position Recovery from HeterogeneousTelco Cellular Data. - Yige Zhang; Weixiong Rao; Kun Zhang 001; Lei Chen 002 (CoRR, 2021)

A Fast PC Algorithm with Reversed-order Pruning and A Parallelization Strategy. - Kai Zhang 041; Chao Tian; Kun Zhang 001; Todd Johnson; Xiaoqian Jiang (CoRR, 2021)

Transferable Time-Series Forecasting under Causal Conditional Shift. - Zijian Li 001; Ruichu Cai; Tom Z. J. Fu; Kun Zhang 001 (CoRR, 2021)

Adversarial orthogonal regression: Two non-linear regressions for causal inference. - M. Reza Heydari; Saber Salehkaleybar; Kun Zhang 001 (Neural Networks, 2021)

Causal Discovery with Confounding Cascade Nonlinear Additive Noise Models. - Jie Qiao; Ruichu Cai; Kun Zhang 001; Zhenjie Zhang; Zhifeng Hao (ACM Trans. Intell. Syst. Technol., 2021)

Transfer Learning-Based Outdoor Position Recovery With Cellular Data. - Yige Zhang; Aaron Yi Ding; Jörg Ott; Mingxuan Yuan; Jia Zeng; Kun Zhang 001; Weixiong Rao (IEEE Trans. Mob. Comput., 2021)

Residual Similarity Based Conditional Independence Test and Its Application in Causal Discovery. - Hao Zhang 079; Shuigeng Zhou; Kun Zhang 001; Jihong Guan (AAAI, 2022)

Identification of Linear Latent Variable Model with Arbitrary Distribution. - Zhengming Chen; Feng Xie 002; Jie Qiao; Zhifeng Hao; Kun Zhang 001; Ruichu Cai (AAAI, 2022)

Invariant Action Effect Model for Reinforcement Learning. - Zheng-Mao Zhu; Shengyi Jiang; Yu-Ren Liu; Yang Yu 001; Kun Zhang 001 (AAAI, 2022)

Towards Federated Bayesian Network Structure Learning with Continuous Optimization. - Ignavier Ng; Kun Zhang 001 (AISTATS, 2022)

On the Convergence of Continuous Constrained Optimization for Structure Learning. - Ignavier Ng; Sébastien Lachapelle; Nan Rosemary Ke; Simon Lacoste-Julien; Kun Zhang 001 (AISTATS, 2022)

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks (Extended Abstract). - Yue Yu 001; Xuan Kan; Hejie Cui; Ran Xu 002; Yujia Zheng 001; Xiangchen Song; Yanqiao Zhu 001; Kun Zhang 001; Razieh Nabi; Ying Guo 003; Chao Zhang 014; Carl Yang 001 (IEEE Big Data, 2022)

Attainability and Optimality: The Equalized Odds Fairness Revisited. - Zeyu Tang 002; Kun Zhang 001 (CLeaR, 2022)

Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation. - Yanwu Xu 003; Shaoan Xie; Wenhao Wu; Kun Zhang 001; Mingming Gong; Kayhan Batmanghelich (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 001; Dacheng Tao (CVPR, 2022)

AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning. - Biwei Huang; Fan Feng; Chaochao Lu; Sara Magliacane; Kun Zhang 001 (ICLR, 2022)

Conditional Contrastive Learning with Kernel. - Yao-Hung Hubert Tsai; Tianqin Li; Martin Q. Ma; Han Zhao 002; Kun Zhang 001; Louis-Philippe Morency; Ruslan Salakhutdinov (ICLR, 2022)

Optimal Transport for Causal Discovery. - Ruibo Tu; Kun Zhang 001; Hedvig Kjellström; Cheng Zhang 005 (ICLR, 2022)

Learning Temporally Causal Latent Processes from General Temporal Data. - Weiran Yao; Yuewen Sun; Alex Ho; Changyin Sun 001; Kun Zhang 001 (ICLR, 2022)

Adversarial Robustness Through the Lens of Causality. - Yonggang Zhang 003; Mingming Gong; Tongliang Liu; Gang Niu 001; Xinmei Tian 001; Bo Han 003; Bernhard Schölkopf; Kun Zhang 001 (ICLR, 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 001 (ICML, 2022)

Partial disentanglement for domain adaptation. - Lingjing Kong; Shaoan Xie; Weiran Yao; Yujia Zheng 001; Guangyi Chen 002; Petar Stojanov; Victor Akinwande; Kun Zhang 001 (ICML, 2022)

Identification of Linear Non-Gaussian Latent Hierarchical Structure. - Feng Xie 002; Biwei Huang; Zhengming Chen; Yangbo He; Zhi Geng; Kun Zhang 001 (ICML, 2022)

Truncated Matrix Power Iteration for Differentiable DAG Learning. - Zhen Zhang 008; Ignavier Ng; Dong Gong; Yuhang Liu 002; Ehsan Abbasnejad; Mingming Gong; Kun Zhang 001; Javen Qinfeng Shi (NeurIPS, 2022)

Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models. - Haoyue Dai; Peter Spirtes; Kun Zhang 001 (NeurIPS, 2022)

Factored Adaptation for Non-Stationary Reinforcement Learning. - Fan Feng; Biwei Huang; Kun Zhang 001; Sara Magliacane (NeurIPS, 2022)

MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models. - Erdun Gao; Ignavier Ng; Mingming Gong; Li Shen 008; Wei Huang; Tongliang Liu; Kun Zhang 001; Howard D. Bondell (NeurIPS, 2022)

Latent Hierarchical Causal Structure Discovery with Rank Constraints. - Biwei Huang; Charles Jia Han Low; Feng Xie 002; Clark Glymour; Kun Zhang 001 (NeurIPS, 2022)

Unsupervised Image-to-Image Translation with Density Changing Regularization. - Shaoan Xie; Qirong Ho; Kun Zhang 001 (NeurIPS, 2022)

Causal Discovery in Linear Latent Variable Models Subject to Measurement Error. - Yuqin Yang; AmirEmad Ghassami; Mohamed S. Nafea; Negar Kiyavash; Kun Zhang 001; Ilya Shpitser (NeurIPS, 2022)

Temporally Disentangled Representation Learning. - Weiran Yao; Guangyi Chen 002; Kun Zhang 001 (NeurIPS, 2022)

On the Identifiability of Nonlinear ICA: Sparsity and Beyond. - Yujia Zheng 001; Ignavier Ng; Kun Zhang 001 (NeurIPS, 2022)

Counterfactual Fairness with Partially Known Causal Graph. - Aoqi Zuo; Susan Wei; Tongliang Liu; Bo Han 003; Kun Zhang 001; Mingming Gong (NeurIPS, 2022)

Learning Latent Causal Dynamics. - Weiran Yao; Guangyi Chen 002; Kun Zhang 001 (CoRR, 2022)

Offline Reinforcement Learning with Causal Structured World Models. - Zheng-Mao Zhu; Xiong-Hui Chen; Hong-Long Tian; Kun Zhang 001; Yang Yu 001 (CoRR, 2022)

Causality-Based Multivariate Time Series Anomaly Detection. - Wenzhuo Yang; Kun Zhang 001; Steven C. H. Hoi (CoRR, 2022)

Weight-variant Latent Causal Models. - Yuhang Liu 002; Zhen Zhang 008; Dong Gong; Mingming Gong; Biwei Huang; Anton van den Hengel; Kun Zhang 001; Javen Qinfeng Shi (CoRR, 2022)

Identifying Latent Causal Content for Multi-Source Domain Adaptation. - Yuhang Liu 002; Zhen Zhang 008; Dong Gong; Mingming Gong; Biwei Huang; Kun Zhang 001; Javen Qinfeng Shi (CoRR, 2022)

Prompt Learning with Optimal Transport for Vision-Language Models. - Guangyi Chen 002; Weiran Yao; Xiangchen Song; Xinyue Li; Yongming Rao; Kun Zhang 001 (CoRR, 2022)

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks. - Yue Yu 001; Xuan Kan; Hejie Cui; Ran Xu 002; Yujia Zheng 001; Xiangchen Song; Yanqiao Zhu 001; Kun Zhang 001; Razieh Nabi; Ying Guo 003; Chao Zhang 014; Carl Yang 001 (CoRR, 2022)

Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models. - Feng Xie 002; Yangbo He; Zhi Geng; Zhengming Chen; Ru Hou; Kun Zhang 001 (Entropy, 2022)

Relevance attack on detectors. - Sizhe Chen; Fan He; Xiaolin Huang; Kun Zhang 001 (Pattern Recognit., 2022)

Causal Discovery in Linear Non-Gaussian Acyclic Model With Multiple Latent Confounders. - Wei Chen 103; Ruichu Cai; Kun Zhang 001; Zhifeng Hao (IEEE Trans. Neural Networks Learn. Syst., 2022)

Causal Discovery with Score Matching on Additive Models with Arbitrary Noise. - Francesco Montagna; Nicoletta Noceti; Lorenzo Rosasco; Kun Zhang 001; Francesco Locatello (CLeaR, 2023)

Scalable Causal Discovery with Score Matching. - Francesco Montagna; Nicoletta Noceti; Lorenzo Rosasco; Kun Zhang 001; Francesco Locatello (CLeaR, 2023)

Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction. - Guangyi Chen 002; Zhenhao Chen; Shunxing Fan; Kun Zhang 001 (CVPR, 2023)

Understanding Masked Autoencoders via Hierarchical Latent Variable Models. - Lingjing Kong; Martin Q. Ma; Guangyi Chen 002; Eric P. Xing; Yuejie Chi; Louis-Philippe Morency; Kun Zhang 001 (CVPR, 2023)

Unpaired Image-to-Image Translation with Shortest Path Regularization. - Shaoan Xie; Yanwu Xu 003; Mingming Gong; Kun Zhang 001 (CVPR, 2023)

SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model. - Shaoan Xie; Zhifei Zhang; Zhe Lin 001; Tobias Hinz; Kun Zhang 001 (CVPR, 2023)

Tem-adapter: Adapting Image-Text Pretraining for Video Question Answer. - Guangyi Chen 002; Xiao Liu; Guangrun Wang; Kun Zhang 001; Philip H. S. Torr; Xiao-Ping Zhang 002; Yansong Tang (ICCV, 2023)

PLOT: Prompt Learning with Optimal Transport for Vision-Language Models. - Guangyi Chen 002; Weiran Yao; Xiangchen Song; Xinyue Li; Yongming Rao; Kun Zhang 001 (ICLR, 2023)

Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems. - Yewen Fan; Nian Si; Kun Zhang 001 (ICLR, 2023)

GAIN: On the Generalization of Instructional Action Understanding. - Junlong Li; Guangyi Chen 002; Yansong Tang; Jinan Bao; Kun Zhang 001; Jie Zhou 001; Jiwen Lu (ICLR, 2023)

Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors. - Zeyu Tang 002; Yatong Chen; Yang Liu 018; Kun Zhang 001 (ICLR, 2023)

Causal Balancing for Domain Generalization. - Xinyi Wang 003; Michael Saxon; Jiachen Li; Hongyang Zhang 001; Kun Zhang 001; William Yang Wang (ICLR, 2023)

Multi-domain image generation and translation with identifiability guarantees. - Shaoan Xie; Lingjing Kong; Mingming Gong; Kun Zhang 001 (ICLR, 2023)

Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks. - Yujia Zheng 001; Ignavier Ng; Yewen Fan; Kun Zhang 001 (ICLR, 2023)

Evolving Semantic Prototype Improves Generative Zero-Shot Learning. - Shiming Chen 002; Wenjin Hou; Ziming Hong; Xiaohan Ding; Yibing Song; Xinge You; Tongliang Liu; Kun Zhang 001 (ICML, 2023)

Causal Discovery with Latent Confounders Based on Higher-Order Cumulants. - Ruichu Cai; Zhiyi Huang 008; Wei Chen 103; Zhifeng Hao; Kun Zhang 001 (ICML, 2023)

Model Transferability with Responsive Decision Subjects. - Yatong Chen; Zeyu Tang 002; Kun Zhang 001; Yang Liu 018 (ICML, 2023)

Identifiability of Label Noise Transition Matrix. - Yang Liu 018; Hao Cheng; Kun Zhang 001 (ICML, 2023)

Feature Expansion for Graph Neural Networks. - Jiaqi Sun; Lin Zhang; Guangyi Chen 002; Peng Xu; Kun Zhang 001; Yujiu Yang (ICML, 2023)

Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise? - Yu Yao 005; Mingming Gong; Yuxuan Du; Jun Yu 001; Bo Han 003; Kun Zhang 001; Tongliang Liu (ICML, 2023)

Deep Dag Learning of Effective Brain Connectivity for FMRI Analysis. - Yue Yu 001; Xuan Kan; Hejie Cui; Ran Xu 002; Yujia Zheng 001; Xiangchen Song; Yanqiao Zhu 001; Kun Zhang 001; Razieh Nabi; Ying Guo 003; Chao Zhang 014; Carl Yang 001 (ISBI, 2023)

Improving the Expressiveness of K-hop Message-Passing GNNs by Injecting Contextualized Substructure Information. - Tianjun Yao; Yingxu Wang; Kun Zhang 001; Shangsong Liang (KDD, 2023)

Subspace Identification for Multi-Source Domain Adaptation. - Zijian Li 001; Ruichu Cai; Guangyi Chen 002; Boyang Sun; Zhifeng Hao; Kun Zhang 001 (NeurIPS, 2023)

Identification of Nonlinear Latent Hierarchical Models. - Lingjing Kong; Biwei Huang; Feng Xie 002; Eric P. Xing; Yuejie Chi; Kun Zhang 001 (NeurIPS, 2023)

Learning World Models with Identifiable Factorization. - Yuren Liu; Biwei Huang; Zhengmao Zhu; Hong-Long Tian; Mingming Gong; Yang Yu 001; Kun Zhang 001 (NeurIPS, 2023)

On the Identifiability of Sparse ICA without Assuming Non-Gaussianity. - Ignavier Ng; Yujia Zheng 001; Xinshuai Dong; Kun Zhang 001 (NeurIPS, 2023)

Temporally Disentangled Representation Learning under Unknown Nonstationarity. - Xiangchen Song; Weiran Yao; Yewen Fan; Xinshuai Dong; Guangyi Chen 002; Juan Carlos Niebles; Eric P. Xing; Kun Zhang 001 (NeurIPS, 2023)

Counterfactual Generation with Identifiability Guarantees. - Hanqi Yan; Lingjing Kong; Lin Gui 003; Yuejie Chi; Eric P. Xing; Yulan He 001; Kun Zhang 001 (NeurIPS, 2023)

Generalizing Nonlinear ICA Beyond Structural Sparsity. - Yujia Zheng 001; Kun Zhang 001 (NeurIPS, 2023)

Increasing Fairness in Compromise on Accuracy via Weighted Vote with Learning Guarantees. - Yijun Bian; Kun Zhang 001 (CoRR, 2023)

Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data. - Devansh Arpit; Matthew Fernandez; Chenghao Liu; Weiran Yao; Wenzhuo Yang; Paul Josel; Shelby Heinecke; Eric Hu; Huan Wang 014; Stephen C. H. Hoi; Caiming Xiong; Kun Zhang 001; et al. (CoRR, 2023)

On the Opportunity of Causal Deep Generative Models: A Survey and Future Directions. - Guanglin Zhou; Lina Yao 001; Xiwei Xu 001; Chen Wang 008; Liming Zhu 001; Kun Zhang 001 (CoRR, 2023)

Beware of Instantaneous Dependence in Reinforcement Learning. - Zheng-Mao Zhu; Yu-Ren Liu; Hong-Long Tian; Yang Yu 001; Kun Zhang 001 (CoRR, 2023)

Voices of Her: Analyzing Gender Differences in the AI Publication World. - Yiwen Ding; Jiarui Liu 004; Zhiheng Lyu; Kun Zhang 001; Bernhard Schölkopf; Zhijing Jin 001; Rada Mihalcea (CoRR, 2023)

Understanding Breast Cancer Survival: Using Causality and Language Models on Multi-omics Data. - Mugariya Farooq; Shahad Hardan; Aigerim Zhumbhayeva; Yujia Zheng 001; Preslav Nakov; Kun Zhang 001 (CoRR, 2023)

Advancing Counterfactual Inference through Quantile Regression. - Shaoan Xie; Biwei Huang; Bin Gu 001; Tongliang Liu; Kun Zhang 001 (CoRR, 2023)

Partial Identifiability for Domain Adaptation. - Lingjing Kong; Shaoan Xie; Weiran Yao; Yujia Zheng 001; Guangyi Chen 002; Petar Stojanov; Victor Akinwande; Kun Zhang 001 (CoRR, 2023)

Learning World Models with Identifiable Factorization. - Yu-Ren Liu; Biwei Huang; Zheng-Mao Zhu; Hong-Long Tian; Mingming Gong; Yang Yu 001; Kun Zhang 001 (CoRR, 2023)

Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment. - Sheng Zhang; Muzammal Naseer; Guangyi Chen 002; Zhiqiang Shen; Salman H. Khan 001; Kun Zhang 001; Fahad Shahbaz Khan (CoRR, 2023)

Identifying Semantic Component for Robust Molecular Property Prediction. - Zijian Li 001; Zunhong Xu; Ruichu Cai; Zhenhui Yang; Yuguang Yan; Zhifeng Hao; Guangyi Chen 002; Kun Zhang 001 (CoRR, 2023)

DreamInpainter: Text-Guided Subject-Driven Image Inpainting with Diffusion Models. - Shaoan Xie; Yang Zhao; Zhisheng Xiao; Kelvin C. K. Chan; Yandong Li; Yanwu Xu 003; Kun Zhang 001; Tingbo Hou (CoRR, 2023)

How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation. - Zhongyi Han; Guanglin Zhou; Rundong He; Jindong Wang 001; Tailin Wu; Yilong Yin; Salman H. Khan 001; Lina Yao 001; Tongliang Liu; Kun Zhang 001 (CoRR, 2023)

What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective. - Zeyu Tang 002; Jiji Zhang; Kun Zhang 001 (ACM Comput. Surv., 2023)

Editorial Special Issue on Causality: Fundamental Limits and Applications. - Negar Kiyavash; Elias Bareinboim; Todd P. Coleman; Alex Dimakis; Bernhard Schlkopf; Peter Spirtes; Kun Zhang 001; Robert Nowak 001 (IEEE J. Sel. Areas Inf. Theory, 2023)

Conditional Independence Test Based on Residual Similarity. - Hao Zhang 079; Yewei Xia; Kun Zhang 001; Shuigeng Zhou; Jihong Guan (ACM Trans. Knowl. Discov. Data, 2023)

Model-Based Transfer Reinforcement Learning Based on Graphical Model Representations. - Yuewen Sun; Kun Zhang 001; Changyin Sun 001 (IEEE Trans. Neural Networks Learn. Syst., 2023)

Causal discovery of 1-factor measurement models in linear latent variable models with arbitrary noise distributions. - Feng Xie 002; Yan Zeng 002; Zhengming Chen; Yangbo He; Zhi Geng; Kun Zhang 001 (Neurocomputing, 2023)

Contextual-Assisted Scratched Photo Restoration. - Weiwei Cai; Huaidong Zhang; Xuemiao Xu; Shengfeng He; Kun Zhang 001; Jing Qin 001 (IEEE Trans. Circuits Syst. Video Technol., 2023)

Outdoor Position Recovery From Heterogeneous Telco Cellular Data. - Yige Zhang; Weixiong Rao; Kun Zhang 001; Lei Chen 002 (IEEE Trans. Knowl. Data Eng., 2023)

Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants. - Wei Chen 103; Zhiyi Huang 008; Ruichu Cai; Zhifeng Hao; Kun Zhang 001 (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 001; Kun Zhang 001 (AAAI, 2024)

S3A: Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment. - Sheng Zhang; Muzammal Naseer; Guangyi Chen 002; Zhiqiang Shen; Salman H. Khan 001; Kun Zhang 001; Fahad Khan 001 (AAAI, 2024)

Local Causal Discovery with Linear non-Gaussian Cyclic Models. - Haoyue Dai; Ignavier Ng; Yujia Zheng 001; Zhengqing Gao; Kun Zhang 001 (AISTATS, 2024)

Structure Learning with Continuous Optimization: A Sober Look and Beyond. - Ignavier Ng; Biwei Huang; Kun Zhang 001 (CLeaR, 2024)

MRIShift: Disentangled Representation Learning for 3D MRI Lesion Segmentation Under Distributional Shifts. - Umaima Rahman; Guangyi Chen 002; Kun Zhang 001 (EUVIP, 2024)

Learning Socio-Temporal Graphs for Multi-Agent Trajectory Prediction. - Yuke Li; Lixiong Chen; Guangyi Chen 002; Ching-Yao Chan; Kun Zhang 001; Stefano Anzellotti; Donglai Wei 001 (HCMA@MM, 2024)

LLCP: Learning Latent Causal Processes for Reasoning-based Video Question Answer. - Guangyi Chen 002; Yuke Li; Xiao Liu; Zijian Li 001; Eman Al Suradi; Donglai Wei 001; Kun Zhang 001 (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 001 (ICLR, 2024)

A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables. - Xinshuai Dong; Biwei Huang; Ignavier Ng; Xiangchen Song; Yujia Zheng 001; Songyao Jin; Roberto Legaspi; Peter Spirtes; Kun Zhang 001 (ICLR, 2024)

Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability. - Songyao Jin; Feng Xie 002; Guangyi Chen 002; Biwei Huang; Zhengming Chen; Xinshuai Dong; Kun Zhang 001 (ICLR, 2024)

Federated Causal Discovery from Heterogeneous Data. - Loka Li; Ignavier Ng; Gongxu Luo; Biwei Huang; Guangyi Chen 002; Tongliang Liu; Bin Gu 001; Kun Zhang 001 (ICLR, 2024)

Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions. - Xiu-Chuan Li; Kun Zhang 001; Tongliang Liu (ICLR, 2024)

Identifiable Latent Polynomial Causal Models through the Lens of Change. - Yuhang Liu 002; Zhen Zhang 008; Dong Gong; Mingming Gong; Biwei Huang; Anton van den Hengel; Kun Zhang 001; Javen Qinfeng Shi (ICLR, 2024)

Procedural Fairness Through Decoupling Objectionable Data Generating Components. - Zeyu Tang 002; Jialu Wang; Yang Liu 018; Peter Spirtes; Kun Zhang 001 (ICLR, 2024)

Causal Representation Learning from Multiple Distributions: A General Setting. - Kun Zhang 001; Shaoan Xie; Ignavier Ng; Yujia Zheng 001 (ICML, 2024)

CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process. - Guangyi Chen 002; Yifan Shen 004; Zhenhao Chen; Xiangchen Song; Yuewen Sun; Weiran Yao; Xiao Liu; Kun Zhang 001 (ICML, 2024)

On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data. - Shunxing Fan; Mingming Gong; Kun Zhang 001 (ICML, 2024)

Score-Based Causal Discovery of Latent Variable Causal Models. - Ignavier Ng; Xinshuai Dong; Haoyue Dai; Biwei Huang; Peter Spirtes; Kun Zhang 001 (ICML, 2024)

Optimal Kernel Choice for Score Function-based Causal Discovery. - Wenjie Wang; Biwei Huang; Feng Liu 003; Xinge You; Tongliang Liu; Kun Zhang 001; Mingming Gong (ICML, 2024)

Empowering Graph Invariance Learning with Deep Spurious Infomax. - Tianjun Yao; Yongqiang Chen 002; Zhenhao Chen; Kai Hu 010; Zhiqiang Shen; Kun Zhang 001 (ICML, 2024)

Detecting and Identifying Selection Structure in Sequential Data. - Yujia Zheng 001; Zeyu Tang 002; Yiwen Qiu; Bernhard Schölkopf; Kun Zhang 001 (ICML, 2024)

Learning Discrete Latent Variable Structures with Tensor Rank Conditions. - Zhengming Chen; Ruichu Cai; Feng Xie 002; Jie Qiao; Anpeng Wu; Zijian Li 001; Zhifeng Hao; Kun Zhang 001 (NeurIPS, 2024)

Neural Collapse Inspired Feature Alignment for Out-of-Distribution Generalization. - Zhikang Chen; Min Zhang 005; Sen Cui; Haoxuan Li 001; Gang Niu 001; Mingming Gong; Changshui Zhang; Kun Zhang 001 (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 001 (NeurIPS, 2024)

Natural Counterfactuals With Necessary Backtracking. - Guang-Yuan Hao; Jiji Zhang; Biwei Huang; Hao Wang 014; Kun Zhang 001 (NeurIPS, 2024)

Learning Discrete Concepts in Latent Hierarchical Models. - Lingjing Kong; Guangyi Chen 002; Biwei Huang; Eric P. Xing; Yuejie Chi; Kun Zhang 001 (NeurIPS, 2024)

Towards Understanding Extrapolation: a Causal Lens. - Lingjing Kong; Guangyi Chen 002; Petar Stojanov; Haoxuan Li 001; Eric P. Xing; Kun Zhang 001 (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 002; Kun Zhang 001 (NeurIPS, 2024)

Discovery of the Hidden World with Large Language Models. - Chenxi Liu; Yongqiang Chen 002; Tongliang Liu; Mingming Gong; James Cheng; Bo Han 003; Kun Zhang 001 (NeurIPS, 2024)

Identifying Selections for Unsupervised Subtask Discovery. - Yiwen Qiu; Yujia Zheng 001; Kun Zhang 001 (NeurIPS, 2024)

Causal Temporal Representation Learning with Nonstationary Sparse Transition. - Xiangchen Song; Zijian Li 001; Guangyi Chen 002; Yujia Zheng 001; Yewen Fan; Xinshuai Dong; Kun Zhang 001 (NeurIPS, 2024)

Identifying Latent State-Transition Processes for Individualized Reinforcement Learning. - Yuewen Sun; Biwei Huang; Yu Yao 005; Donghuo Zeng; Xinshuai Dong; Songyao Jin; Boyang Sun; Roberto Legaspi; Kazushi Ikeda; Peter Spirtes; Kun Zhang 001 (NeurIPS, 2024)

Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome. - Donghuo Zeng; Roberto Sebastian Legaspi; Yuewen Sun; Xinshuai Dong; Kazushi Ikeda; Peter Spirtes; Kun Zhang 001 (PERSUASIVE, 2024)

MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification. - Tianjun Yao; Jiaqi Sun; Defu Cao; Kun Zhang 001; Guangyi Chen 002 (WWW, 2024)

Ada-FFL: Adaptive computing fairness federated learning. - Yue Cong; Jing Qiu 002; Kun Zhang 001; Zhongyang Fang; Chengliang Gao; Shen Su; Zhihong Tian (CAAI Trans. Intell. Technol., 2024)

On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors. - Xinshuai Dong; Haoyue Dai; Yewen Fan; Songyao Jin; Sathyamoorthy Rajendran; Kun Zhang 001 (CoRR, 2024)

Calibration-then-Calculation: A Variance Reduced Metric Framework in Deep Click-Through Rate Prediction Models. - Yewen Fan; Nian Si; Xiangchen Song; Kun Zhang 001 (CoRR, 2024)

Revealing Multimodal Contrastive Representation Learning through Latent Partial Causal Models. - Yuhang Liu 002; Zhen Zhang 008; Dong Gong; Biwei Huang; Mingming Gong; Anton van den Hengel; Kun Zhang 001; Javen Qinfeng Shi (CoRR, 2024)

Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models. - Loka Li; Guangyi Chen 002; Yusheng Su; Zhenhao Chen; Yixuan Zhang 001; Eric P. Xing; Kun Zhang 001 (CoRR, 2024)

When and How: Learning Identifiable Latent States for Nonstationary Time Series Forecasting. - Zijian Li 001; Ruichu Cai; Zhenhui Yang; Haiqin Huang; Guangyi Chen 002; Yifan Shen 004; Zhengming Chen; Xiangchen Song; Zhifeng Hao; Kun Zhang 001 (CoRR, 2024)

A Randomized Controlled Trial on Anonymizing Reviewers to Each Other in Peer Review Discussions. - Charvi Rastogi; Xiangchen Song; Zhijing Jin 001; Ivan Stelmakh; Hal Daumé III; Kun Zhang 001; Nihar B. Shah (CoRR, 2024)

Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing Framework. - Jingling Li; Zeyu Tang 002; Xiaoyu Liu; Peter Spirtes; Kun Zhang 001; Liu Leqi; Yang Liu 018 (CoRR, 2024)

Identifiable Latent Neural Causal Models. - Yuhang Liu 002; Zhen Zhang 008; Dong Gong; Mingming Gong; Biwei Huang; Anton van den Hengel; Kun Zhang 001; Javen Qinfeng Shi (CoRR, 2024)

On the Identification of Temporally Causal Representation with Instantaneous Dependence. - Zijian Li 001; Yifan Shen 004; Kaitao Zheng; Ruichu Cai; Xiangchen Song; Mingming Gong; Zhifeng Hao; Zhengmao Zhu; Guangyi Chen 002; Kun Zhang 001 (CoRR, 2024)

From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals. - Ruichu Cai; Zhifang Jiang; Zijian Li 001; Weilin Chen 001; Xuexin Chen; Zhifeng Hao; Yifan Shen 004; Guangyi Chen 002; Kun Zhang 001 (CoRR, 2024)

Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight. - Zhiqiang Xie; Yujia Zheng 001; Lizi Ottens; Kun Zhang 001; Christos Kozyrakis; Jonathan Mace (CoRR, 2024)

Causal Discovery in Linear Models with Unobserved Variables and Measurement Error. - Yuqin Yang; Mohamed S. Nafea; Negar Kiyavash; Kun Zhang 001; AmirEmad Ghassami (CoRR, 2024)

Generalized Encouragement-Based Instrumental Variables for Counterfactual Regression. - Anpeng Wu; Kun Kuang; Ruoxuan Xiong; Xiangwei Chen; Zexu Sun; Fei Wu 001; Kun Zhang 001 (CoRR, 2024)

Continual Learning of Nonlinear Independent Representations. - Boyang Sun; Ignavier Ng; Guangyi Chen 002; Yifan Shen 004; Qirong Ho; Kun Zhang 001 (CoRR, 2024)

CT4D: Consistent Text-to-4D Generation with Animatable Meshes. - Ce Chen; Shaoli Huang; Xuelin Chen; Guangyi Chen 002; Xiaoguang Han 001; Kun Zhang 001; Mingming Gong (CoRR, 2024)

Adjusting Pretrained Backbones for Performativity. - Berker Demirel; Lingjing Kong; Kun Zhang 001; Theofanis Karaletsos; Celestine Mendler-Dünner; Francesco Locatello (CoRR, 2024)

Causality for Large Language Models. - Anpeng Wu; Kun Kuang; Minqin Zhu; Yingrong Wang; Yujia Zheng 001; Kairong Han; Baohong Li; Guangyi Chen 002; Fei Wu 001; Kun Zhang 001 (CoRR, 2024)

Revisiting Differentiable Structure Learning: Inconsistency of ℓ1 Penalty and Beyond. - Kaifeng Jin; Ignavier Ng; Kun Zhang 001; Biwei Huang (CoRR, 2024)

ψDAG: Projected Stochastic Approximation Iteration for DAG Structure Learning. - Klea Ziu; Slavomír Hanzely; Loka Li; Kun Zhang 001; Martin Takác 001; Dmitry Kamzolov (CoRR, 2024)

Reducing Hyperparameter Tuning Costs in ML, Vision and Language Model Training Pipelines via Memoization-Awareness. - Abdelmajid Essofi; Ridwan Salahuddeen; Munachiso Nwadike; Elnura Zhalieva; Kun Zhang 001; Eric P. Xing; Willie Neiswanger; Qirong Ho (CoRR, 2024)

Causal Representation Learning from Multimodal Biological Observations. - Yuewen Sun; Lingjing Kong; Guangyi Chen 002; Loka Li; Gongxu Luo; Zijian Li 001; Yixuan Zhang 001; Yujia Zheng 001; Mengyue Yang; Petar Stojanov; Eran Segal; Eric P. Xing; et al. (CoRR, 2024)

Hierarchical damage correlations for old photo restoration. - Weiwei Cai; Xuemiao Xu; Jiajia Xu; Huaidong Zhang; Haoxin Yang; Kun Zhang 001; Shengfeng He (Inf. Fusion, 2024)

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables. - Feng Xie 002; Biwei Huang; Zhengming Chen; Ruichu Cai; Clark Glymour; Zhi Geng; Kun Zhang 001 (J. Mach. Learn. Res., 2024)

Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations. - Yuanyuan Wang 010; Wei Huang; Mingming Gong; Xi Geng; Tongliang Liu; Kun Zhang 001; Dacheng Tao (J. Mach. Learn. Res., 2024)

Causal-learn: Causal Discovery in Python. - Yujia Zheng 001; Biwei Huang; Wei Chen 103; Joseph D. Ramsey; Mingming Gong; Ruichu Cai; Shohei Shimizu; Peter Spirtes; Kun Zhang 001 (J. Mach. Learn. Res., 2024)

Delving Into Important Samples of Semi-Supervised Old Photo Restoration: A New Dataset and Method. - Weiwei Cai; Huaidong Zhang; Xuemiao Xu; Chenshu Xu; Kun Zhang 001; Shengfeng He (IEEE Trans. Multim., 2024)

Transferable Time-Series Forecasting Under Causal Conditional Shift. - Zijian Li 001; Ruichu Cai; Tom Z. J. Fu; Zhifeng Hao; Kun Zhang 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Graph Domain Adaptation: A Generative View. - Ruichu Cai; Fengzhu Wu; Zijian Li 001; Pengfei Wei 001; Lingling Yi; Kun Zhang 001 (ACM Trans. Knowl. Discov. Data, 2024)

IEEE Transactions on Neural Networks and Learning Systems Special Issue on Causal Discovery and Causality-Inspired Machine Learning. - Kun Zhang 001; Ilya Shpitser; Sara Magliacane; Davide Bacciu; Fei Wu 001; Changshui Zhang; Peter Spirtes (IEEE Trans. Neural Networks Learn. Syst., 2024)

Explainable Recommender With Geometric Information Bottleneck. - Hanqi Yan; Lin Gui 003; Menghan Wang; Kun Zhang 001; Yulan He 001 (IEEE Trans. Knowl. Data Eng., 2024)

Fully Deformable Network for Multiview Face Image Synthesis. - Cheng Xu; Keke Li; Xuandi Luo; Xuemiao Xu; Shengfeng He; Kun Zhang 001 (IEEE Trans. Neural Networks Learn. Syst., 2024)

Causal Representation Learning from General Environments under Nonparametric Mixing. - Ignavier Ng; Shaoan Xie; Xinshuai Dong; Peter Spirtes; Kun Zhang 001 (AISTATS, 2025)

Type Information-Assisted Self-Supervised Knowledge Graph Denoising. - Jiaqi Sun; Yujia Zheng 001; Xinshuai Dong; Haoyue Dai; Kun Zhang 001 (AISTATS, 2025)

Nonparametric Factor Analysis and Beyond. - Yujia Zheng 001; Yang Liu 018; Jiaxiong Yao; Yingyao Hu; Kun Zhang 001 (AISTATS, 2025)

SmartCLIP: Modular Vision-language Alignment with Identification Guarantees. - Shaoan Xie; Lingjing; Yujia Zheng 001; Yu Yao 005; Zeyu Tang 002; Eric P. Xing; Guangyi Chen 002; Kun Zhang 001 (CVPR, 2025)

Infant Cry Detection Using Causal Temporal Representation. - Minghao Fu 002; Danning Li; Aryan Gadhiya; Benjamin Lambright; Mohamed Alowais; Mohab Bahnassy; Saad El Dine Elletter; Hawau Olamide Toyin; Haiyan Jiang; Kun Zhang 001; Hanan Aldarmaki (ICASSP, 2025)

Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning. - Zijian Li 001; Shunxing Fan; Yujia Zheng 001; Ignavier Ng; Shaoan Xie; Guangyi Chen 002; Xinshuai Dong; Ruichu Cai; Kun Zhang 001 (ICLR, 2025)

On the Identification of Temporal Causal Representation with Instantaneous Dependence. - Zijian Li 001; Yifan Shen 004; Kaitao Zheng; Ruichu Cai; Xiangchen Song; Mingming Gong; Guangyi Chen 002; Kun Zhang 001 (ICLR, 2025)

Analytic DAG Constraints for Differentiable DAG Learning. - Zhen Zhang 008; Ignavier Ng; Dong Gong; Yuhang Liu 002; Mingming Gong; Biwei Huang; Kun Zhang 001; Anton van den Hengel; Javen Qinfeng Shi (ICLR, 2025)

Noisy Test-Time Adaptation in Vision-Language Models. - Chentao Cao; Zhun Zhong; Zhanke Zhou; Tongliang Liu; Yang Liu 018; Kun Zhang 001; Bo Han 003 (ICLR, 2025)

When Selection Meets Intervention: Additional Complexities in Causal Discovery. - Haoyue Dai; Ignavier Ng; Jianle Sun; Zeyu Tang 002; Gongxu Luo; Xinshuai Dong; Peter Spirtes; Kun Zhang 001 (ICLR, 2025)

Identification of Intermittent Temporal Latent Process. - Yuke Li; Yujia Zheng 001; Guangyi Chen 002; Kun Zhang 001; Heng Huang (ICLR, 2025)

Prompting Fairness: Integrating Causality to Debias Large Language Models. - Jingling Li; Zeyu Tang 002; Xiaoyu Liu; Peter Spirtes; Kun Zhang 001; Liu Leqi; Yang Liu 018 (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 001; Mingming Gong; Yian Ma; Biwei Huang (ICLR, 2025)

Flow: Modularized Agentic Workflow Automation. - Boye Niu; Yiliao Song; Kai Lian; Yifan Shen 004; Yu Yao 005; Kun Zhang 001; Tongliang Liu (ICLR, 2025)

Differentiable Causal Discovery for Latent Hierarchical Causal Models. - Parjanya Prajakta Prashant; Ignavier Ng; Kun Zhang 001; Biwei Huang (ICLR, 2025)

Causal Representation Learning from Multimodal Biomedical Observations. - Yuewen Sun; Lingjing Kong; Guangyi Chen 002; Loka Li; Gongxu Luo; Zijian Li 001; Yixuan Zhang 001; Yujia Zheng 001; Mengyue Yang; Petar Stojanov; Eran Segal; Eric P. Xing; et al. (ICLR, 2025)

Causal Graph Transformer for Treatment Effect Estimation Under Unknown Interference. - Anpeng Wu; Haiyi Qiu; Zhengming Chen; Zijian Li 001; Ruoxuan Xiong; Fei Wu 001; Kun Zhang 001 (ICLR, 2025)

Learning Graph Invariance by Harnessing Spuriosity. - Tianjun Yao; Yongqiang Chen 002; Kai Hu 010; Tongliang Liu; Kun Zhang 001; Zhiqiang Shen (ICLR, 2025)

A Robust Method to Discover Causal or Anticausal Relation. - Yu Yao 005; Yang Zhou; Bo Han 003; Mingming Gong; Kun Zhang 001; Tongliang Liu (ICLR, 2025)

Classifying Treatment Responders: Bounds and Algorithms. - Anpeng Wu; Haoxuan Li 001; Chunyuan Zheng 001; Kun Kuang; Kun Zhang 001 (KDD (1), 2025)

Generative Framework for Personalized Persuasion: Inferring Causal, Counterfactual, and Latent Knowledge. - Donghuo Zeng; Roberto Legaspi; Yuewen Sun; Xinshuai Dong; Kazushi Ikeda; Peter Spirtes; Kun Zhang 001 (UMAP, 2025)

Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals. - Ruichu Cai; Zhifan Jiang; Kaitao Zheng; Zijian Li 001; Weilin Chen 001; Xuexin Chen; Yifan Shen 004; Guangyi Chen 002; Zhifeng Hao; Kun Zhang 001 (WWW, 2025)

Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation. - Yuhang Liu 002; Zhen Zhang 008; Dong Gong; Mingming Gong; Biwei Huang; Anton van den Hengel; Kun Zhang 001; Javen Qinfeng Shi (Trans. Mach. Learn. Res., 2025)

Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges. - Usman Gohar; Zeyu Tang 002; Jialu Wang; Kun Zhang 001; Peter Spirtes; Yang Liu 018; Lu Cheng 001 (Trans. Mach. Learn. Res., 2025)

HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization. - Guanglin Zhou; Zhongyi Han; Shiming Chen 002; Biwei Huang; Liming Zhu 001; Tongliang Liu; Lina Yao 001; Kun Zhang 001 (IEEE Trans. Multim., 2025)

Flow: A Modular Approach to Automated Agentic Workflow Generation. - Boye Niu; Yiliao Song; Kai Lian; Yifan Shen 004; Yu Yao 005; Kun Zhang 001; Tongliang Liu (CoRR, 2025)

Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders. - Gongxu Luo; Haoyue Dai; Boyang Sun; Loka Li; Biwei Huang; Petar Stojanov; Kun Zhang 001 (CoRR, 2025)

Identification of Nonparametric Dynamic Causal Structure and Latent Process in Climate System. - Minghao Fu 002; Biwei Huang; Zijian Li 001; Yujia Zheng 001; Ignavier Ng; Yingyao Hu; Kun Zhang 001 (CoRR, 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 001 (CoRR, 2025)

Controllable Video Generation with Provable Disentanglement. - Yifan Shen 004; Peiyuan Zhu 001; Zijian Li 001; Shaoan Xie; Zeyu Tang 002; Namrata Deka; Zongfang Liu; Guangyi Chen 002; Kun Zhang 001 (CoRR, 2025)

Reflection-Window Decoding: Text Generation with Selective Refinement. - Zeyu Tang 002; Zhenhao Chen; Loka Li; Xiangchen Song; Yunlong Deng; Yifan Shen 004; Guangyi Chen 002; Peter Spirtes; Kun Zhang 001 (CoRR, 2025)

Learning Counterfactual Outcomes Under Rank Preservation. - Peng Wu 012; Haoxuan Li 001; Chunyuan Zheng 001; Yan Zeng 002; Jiawei Chen; Yang Liu 018; Ruocheng Guo; Kun Zhang 001 (CoRR, 2025)

Can Large Language Models Help Experimental Design for Causal Discovery? - Junyi Li; Yongqiang Chen 002; Chenxi Liu; Qianyi Cai; Tongliang Liu; Bo Han 003; Kun Zhang 001; Hui Xiong 001 (CoRR, 2025)

Causal Discovery and Counterfactual Reasoning to Optimize Persuasive Dialogue Policies. - Donghuo Zeng; Roberto Legaspi; Yuewen Sun; Xinshuai Dong; Kazushi Ikeda; Peter Spirtes; Kun Zhang 001 (CoRR, 2025)

ProtoGS: Efficient and High-Quality Rendering with 3D Gaussian Prototypes. - Zhengqing Gao; Dongting Hu; Jia-Wang Bian; Huan Fu; Yan Li 099; Tongliang Liu; Mingming Gong; Kun Zhang 001 (CoRR, 2025)

Alignclip: navigating the misalignments for robust vision-language generalization. - Zhongyi Han; Gongxu Luo; Hao Sun; Yaqian Li; Bo Han 003; Mingming Gong; Kun Zhang 001; Tongliang Liu (Mach. Learn., 2025)

Offline model-based reinforcement learning with causal structured world models. - Zhengmao Zhu; Hong-Long Tian; Xionghui Chen; Kun Zhang 001; Yang Yu 001 (Frontiers Comput. Sci., 2025)

On the Thinking-Language Modeling Gap in Large Language Models. - Chenxi Liu; Yongqiang Chen 002; Tongliang Liu; James Cheng; Bo Han 003; Kun Zhang 001 (CoRR, 2025)

The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representations. - Dingling Yao; Shimeng Huang; Riccardo Cadei; Kun Zhang 001; Francesco Locatello (CoRR, 2025)

Does Representation Intervention Really Identify Desired Concepts and Elicit Alignment? - Hongzheng Yang; Yongqiang Chen 002; Zeyu Qin; Tongliang Liu; Chaowei Xiao; Kun Zhang 001; Bo Han 003 (CoRR, 2025)

Position: Mechanistic Interpretability Should Prioritize Feature Consistency in SAEs. - Xiangchen Song; Aashiq Muhamed; Yujia Zheng 001; Lingjing Kong; Zeyu Tang 002; Mona T. Diab; Virginia Smith; Kun Zhang 001 (CoRR, 2025)

A Sample Efficient Conditional Independence Test in the Presence of Discretization. - Boyang Sun; Yu Yao 005; Xinshuai Dong; Zongfang Liu; Tongliang Liu; Yumou Qiu; Kun Zhang 001 (CoRR, 2025)

Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering. - Tianjun Yao; Haoxuan Li 001; Zhiqiang Shen; Pan Li; Tongliang Liu; Kun Zhang 001 (CoRR, 2025)

Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants. - Weilin Chen 001; Wanyang Gu; Linjun Peng; Ruichu Cai; Zhifeng Hao; Kun Zhang 001 (CoRR, 2025)

Should Bias Always be Eliminated? A Principled Framework to Use Data Bias for OOD Generation. - Yan Li 099; Guangyi Chen 002; Yunlong Deng; Zijian Li 001; Zeyu Tang 002; Anpeng Wu; Kun Zhang 001 (CoRR, 2025)

SmartCLIP: Modular Vision-language Alignment with Identification Guarantees. - Shaoan Xie; Lingjing Kong; Yujia Zheng 001; Yu Yao 005; Zeyu Tang 002; Eric P. Xing; Guangyi Chen 002; Kun Zhang 001 (CoRR, 2025)

Algorithmic Fairness amid Social Determinants: Reflection, Characterization, and Approach. - Zeyu Tang 002; Alex John London; Atoosa Kasirzadeh; Sanmi Koyejo; Peter Spirtes; Kun Zhang 001 (CoRR, 2025)