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)

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)

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)

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

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

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

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

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

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

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)

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)

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

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

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

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)

Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach. - Yudi Zhang 007; Yali Du 001; Biwei Huang; Ziyan Wang; Jun Wang 012; Meng Fang; Mykola Pechenizkiy (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)

Generator Identification for Linear SDEs with Additive and Multiplicative Noise. - Yuanyuan Wang 010; Xi Geng; Wei Huang; Biwei Huang; Mingming Gong (NeurIPS, 2023)

GRD: A Generative Approach for Interpretable Reward Redistribution in Reinforcement Learning. - Yudi Zhang 007; Yali Du 001; Biwei Huang; Ziyan Wang; Jun Wang 012; Meng Fang; Mykola Pechenizkiy (CoRR, 2023)

Advancing Counterfactual Inference through Quantile Regression. - Shaoan Xie; Biwei Huang; Bin Gu 001; Tongliang Liu; 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)

Learning and Using Causal Knowledge: A Further Step Towards a Higher-Level Intelligence - Biwei Huang (2023)

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)

Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach. - Wenqin Liu; Biwei Huang; Erdun Gao; Qiuhong Ke; Howard D. Bondell; Mingming Gong (CLeaR, 2024)

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

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)

An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series. - Qiang Huang; Chuizheng Meng; Defu Cao; Biwei Huang; Yi Chang 001; Yan Liu 002 (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)

Boosting Efficiency in Task-Agnostic Exploration through Causal Knowledge. - Yupei Yang; Biwei Huang; Shikui Tu; Lei Xu 001 (IJCAI, 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)

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)

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)

Identifiability Analysis of Linear ODE Systems with Hidden Confounders. - Yuanyuan Wang 010; Biwei Huang; Wei Huang; Xi Geng; Mingming Gong (NeurIPS, 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)

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)

Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learning. - Guanglin Zhou; Zhongyi Han; Shiming Chen 002; Biwei Huang; Liming Zhu 001; Salman Khan 001; Xinbo Gao 001; Lina Yao 001 (CoRR, 2024)

Rethinking State Disentanglement in Causal Reinforcement Learning. - Haiyao Cao; Zhen Zhang 008; Panpan Cai; Yuhang Liu 002; Jinan Zou; Ehsan Abbasnejad; Biwei Huang; Mingming Gong; Anton van den Hengel; Javen Qinfeng Shi (CoRR, 2024)

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

Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models. - Xichen Guo; Zheng Li; Biwei Huang; Yan Zeng; Zhi Geng; Feng Xie (CoRR, 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)

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)

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)

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)

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

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning. - Xinyue Wang; Biwei Huang (ICLR, 2025)

Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations. - Yupei Yang; Biwei Huang; Fan Feng; Xinyue Wang; Shikui Tu; Lei Xu 001 (ICLR, 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)

Reinforcement Learning for Causal Discovery without Acyclicity Constraints. - Bao Duong; Hung Le 002; Biwei Huang; Thin Nguyen (Trans. Mach. Learn. Res., 2025)

MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment. - Ziyan Wang; Yali Du 001; Yudi Zhang 007; Meng Fang; Biwei Huang (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)

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)

I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data? - Yuhang Liu 002; Dong Gong; Erdun Gao; Zhen Zhang 008; Biwei Huang; Mingming Gong; Anton van den Hengel; Javen Qinfeng Shi (CoRR, 2025)

Causal-Copilot: An Autonomous Causal Analysis Agent. - Xinyue Wang; Kun Zhou; Wenyi Wu; Har Simrat Singh; Fang Nan; Songyao Jin; Aryan Philip; Saloni Patnaik; Hou Zhu; Shivam Singh; Parjanya Prajakta Prashant; Qian Shen; et al. (CoRR, 2025)

A Fast Kernel-based Conditional Independence test with Application to Causal Discovery. - Oliver Schacht; Biwei Huang (CoRR, 2025)

Towards General Continuous Memory for Vision-Language Models. - Wenyi Wu; Zixuan Song; Kun Zhou; Yifei Shao; Zhiting Hu; Biwei Huang (CoRR, 2025)

Activation Control for Efficiently Eliciting Long Chain-of-thought Ability of Language Models. - Zekai Zhao; Qi Liu; Kun Zhou; Zihan Liu; Yifei Shao; Zhiting Hu; Biwei Huang (CoRR, 2025)

Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networks. - Songyao Jin; Biwei Huang (CoRR, 2025)

DEPTH: Hallucination-Free Relation Extraction via Dependency-Aware Sentence Simplification and Two-tiered Hierarchical Refinement. - Yupei Yang; Fan Feng; Lin Yang; Wanxi Deng; Lin Qu; Biwei Huang; Shikui Tu; Lei Xu (CoRR, 2025)