Huang, Biwei

56 publications

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

NeurIPS 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

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

NeurIPS 2025 Causality Meets Locality: Provably Generalizable and Scalable Policy Learning for Networked Systems Hao Liang, Shuqing Shi, Yudi Zhang, Biwei Huang, Yali Du

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

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

TMLR 2025 MACCA: Offline Multi-Agent Reinforcement Learning with Causal Credit Assignment Ziyan Wang, Yali Du, Yudi Zhang, Meng Fang, Biwei Huang

ICML 2025 MissScore: High-Order Score Estimation in the Presence of Missing Data Wenqin Liu, Haoze Hou, Erdun Gao, Biwei Huang, Qiuhong Ke, Howard Bondell, Mingming Gong

ICLR 2025 Modeling Unseen Environments with Language-Guided Composable Causal Components in Reinforcement Learning Xinyue Wang, Biwei Huang

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

TMLR 2025 Reinforcement Learning for Causal Discovery Without Acyclicity Constraints Bao Duong, Hung Le, Biwei Huang, Thin Nguyen

NeurIPS 2025 Towards General Continuous Memory for Vision-Language Models Wenyi Wu, Zixuan Song, Kun Zhou, Yifei Shao, Zhiting Hu, 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

NeurIPSW 2024 A Causality-Inspired Spatial-Temporal Return Decomposition Approach for Multi-Agent Reinforcement Learning Yudi Zhang, Yali Du, Biwei Huang, Meng Fang, Mykola Pechenizkiy

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 An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series Qiang Huang, Chuizheng Meng, Defu Cao, Biwei Huang, Yi Chang, Yan Liu

IJCAI 2024 Boosting Efficiency in Task-Agnostic Exploration Through Causal Knowledge Yupei Yang, Biwei Huang, Shikui Tu, Lei Xu

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

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

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

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

NeurIPS 2024 Identifiability Analysis of Linear ODE Systems with Hidden Confounders Yuanyuan Wang, Biwei Huang, Wei Huang, Xi Geng, Mingming Gong

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

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

NeurIPSW 2024 MACCA: Offline Multi-Agent Reinforcement Learning with Causal Credit Assignment Ziyan Wang, Yali Du, Yudi Zhang, Meng Fang, Biwei Huang

NeurIPS 2024 Natural Counterfactuals with Necessary Backtracking Guang-Yuan Hao, Jiji Zhang, Biwei Huang, Hao Wang, 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 Optimal Kernel Choice for Score Function-Based Causal Discovery Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong

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

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 2023 Generator Identification for Linear SDEs with Additive and Multiplicative Noise Yuanyuan Wang, Xi Geng, Wei Huang, Biwei Huang, Mingming Gong

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

NeurIPS 2023 Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach Yudi Zhang, Yali Du, Biwei Huang, Ziyan Wang, Jun Wang, Meng Fang, Mykola Pechenizkiy

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

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

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

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

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

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

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

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

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

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

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

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

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