Biwei Huang

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 65

Top Topics

Causal Discovery

Reinforcement Learning

Continuous Optimization

Hierarchical Models

Top Venues

Publications

Yupei Yang, Biwei Huang, Shikui Tu, Lei Xu
Boosting Efficiency in Task-Agnostic Exploration through Causal Knowledge. CoRR (2024)
Ignavier Ng, Biwei Huang, Kun Zhang
Structure Learning with Continuous Optimization: A Sober Look and Beyond. CLeaR (2024)
Lingjing Kong, Guangyi Chen, Biwei Huang, Eric P. Xing, Yuejie Chi, Kun Zhang
Learning Discrete Concepts in Latent Hierarchical Models. CoRR (2024)
Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi
Identifiable Latent Neural Causal Models. CoRR (2024)
Yuewen Sun, Erli Wang, Biwei Huang, Chaochao Lu, Lu Feng, Changyin Sun, Kun Zhang
ACAMDA: Improving Data Efficiency in Reinforcement Learning through Guided Counterfactual Data Augmentation. AAAI (2024)
Xinshuai Dong, Ignavier Ng, Biwei Huang, Yuewen Sun, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang
On the Parameter Identifiability of Partially Observed Linear Causal Models. CoRR (2024)
Wenqin Liu, Biwei Huang, Erdun Gao, Qiuhong Ke, Howard D. Bondell, Mingming Gong
Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach. CLeaR (2024)
Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables. ICLR (2024)
Longkang Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang
Federated Causal Discovery from Heterogeneous Data. ICLR (2024)
Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang
Federated Causal Discovery from Heterogeneous Data. CoRR (2024)
Yuhang Liu, Zhen Zhang, Dong Gong, Biwei Huang, Mingming Gong, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi
Revealing Multimodal Contrastive Representation Learning through Latent Partial Causal Models. CoRR (2024)
Guang-Yuan Hao, Jiji Zhang, Biwei Huang, Hao Wang, Kun Zhang
Natural Counterfactuals With Necessary Backtracking. CoRR (2024)
Songyao Jin, Feng Xie, Guangyi Chen, Biwei Huang, Zhengming Chen, Xinshuai Dong, Kun Zhang
Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability. ICLR (2024)
Guanglin Zhou, Zhongyi Han, Shiming Chen, Biwei Huang, Liming Zhu, Salman Khan, Xinbo Gao, Lina Yao
Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learning. CoRR (2024)
Guanglin Zhou, Zhongyi Han, Shiming Chen, Biwei Huang, Liming Zhu, Tongliang Liu, Lina Yao, Kun Zhang
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization. CoRR (2024)
Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi
Identifiable Latent Polynomial Causal Models through the Lens of Change. ICLR (2024)
Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong
Optimal Kernel Choice for Score Function-based Causal Discovery. CoRR (2024)
Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables. CoRR (2023)
Ignavier Ng, Biwei Huang, Kun Zhang
Structure Learning with Continuous Optimization: A Sober Look and Beyond. CoRR (2023)
Yuren Liu, Biwei Huang, Zhengmao Zhu, Hong-Long Tian, Mingming Gong, Yang Yu, Kun Zhang
Learning World Models with Identifiable Factorization. NeurIPS (2023)
Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi
Identifiable Latent Polynomial Causal Models Through the Lens of Change. CoRR (2023)
Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables. CoRR (2023)
Yudi Zhang, Yali Du, Biwei Huang, Ziyan Wang, Jun Wang, Meng Fang, Mykola Pechenizkiy
Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach. NeurIPS (2023)
Yudi Zhang, Yali Du, Biwei Huang, Ziyan Wang, Jun Wang, Meng Fang, Mykola Pechenizkiy
GRD: A Generative Approach for Interpretable Reward Redistribution in Reinforcement Learning. CoRR (2023)
Yu-Ren Liu, Biwei Huang, Zheng-Mao Zhu, Hong-Long Tian, Mingming Gong, Yang Yu, Kun Zhang
Learning World Models with Identifiable Factorization. CoRR (2023)
Yuanyuan Wang, Xi Geng, Wei Huang, Biwei Huang, Mingming Gong
Generator Identification for Linear SDEs with Additive and Multiplicative Noise. CoRR (2023)
Lingjing Kong, Biwei Huang, Feng Xie, Eric P. Xing, Yuejie Chi, Kun Zhang
Identification of Nonlinear Latent Hierarchical Models. CoRR (2023)
Yujia Zheng, Biwei Huang, Wei Chen, Joseph D. Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang
Causal-learn: Causal Discovery in Python. CoRR (2023)
Yuanyuan Wang, Xi Geng, Wei Huang, Biwei Huang, Mingming Gong
Generator Identification for Linear SDEs with Additive and Multiplicative Noise. NeurIPS (2023)
Shaoan Xie, Biwei Huang, Bin Gu, Tongliang Liu, Kun Zhang
Advancing Counterfactual Inference through Quantile Regression. CoRR (2023)
Lingjing Kong, Biwei Huang, Feng Xie, Eric P. Xing, Yuejie Chi, Kun Zhang
Identification of Nonlinear Latent Hierarchical Models. NeurIPS (2023)
Ziyan Wang, Yali Du, Yudi Zhang, Meng Fang, Biwei Huang
MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment. CoRR (2023)
Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang
Latent Hierarchical Causal Structure Discovery with Rank Constraints. CoRR (2022)
Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi
Weight-variant Latent Causal Models. CoRR (2022)
Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Kun Zhang, Javen Qinfeng Shi
Identifying Latent Causal Content for Multi-Source Domain Adaptation. CoRR (2022)
Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane
Factored Adaptation for Non-Stationary Reinforcement Learning. NeurIPS (2022)
Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang
Action-Sufficient State Representation Learning for Control with Structural Constraints. ICML (2022)
Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning. ICLR (2022)
Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang
Latent Hierarchical Causal Structure Discovery with Rank Constraints. NeurIPS (2022)
Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang
Identification of Linear Non-Gaussian Latent Hierarchical Structure. ICML (2022)
Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane
Factored Adaptation for Non-Stationary Reinforcement Learning. CoRR (2022)
Wei Chen, Kun Zhang, Ruichu Cai, Biwei Huang, Joseph D. Ramsey, Zhifeng Hao, Clark Glymour
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders. CoRR (2021)
Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang
Action-Sufficient State Representation Learning for Control with Structural Constraints. CoRR (2021)
Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning. CoRR (2021)
Zhicheng Wang, Biwei Huang, Shikui Tu, Kun Zhang, Lei Xu
DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding. AAAI (2021)
Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Qingsong Liu, Clark Glymour
Domain Adaptation as a Problem of Inference on Graphical Models. NeurIPS (2020)
Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Clark Glymour
Domain Adaptation As a Problem of Inference on Graphical Models. CoRR (2020)
Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang
Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs. CoRR (2020)
Chaochao Lu, Biwei Huang, Ke Wang, José Miguel Hernández-Lobato, Kun Zhang, Bernhard Schölkopf
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation. CoRR (2020)
Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour
Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets. AAAI (2020)
Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf
Causal Discovery from Heterogeneous/Nonstationary Data. J. Mach. Learn. Res. 21 (2020)
Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. NeurIPS (2020)
Biwei Huang, Kun Zhang, Ruben Sanchez-Romero, Joseph D. Ramsey, Madelyn Glymour, Clark Glymour
Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data. CoRR (2019)
Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf
Causal Discovery from Heterogeneous/Nonstationary Data. CoRR (2019)
Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric P. Xing, Clark Glymour
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering. NeurIPS (2019)
Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. CoRR (2019)
Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. ICML (2019)
Mingming Gong, Kun Zhang, Biwei Huang, Clark Glymour, Dacheng Tao, Kayhan Batmanghelich
Causal Generative Domain Adaptation Networks. CoRR (2018)
Biwei Huang, Kun Zhang, Yizhu Lin, Bernhard Schölkopf, Clark Glymour
Generalized Score Functions for Causal Discovery. KDD (2018)
AmirEmad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang
Multi-domain Causal Structure Learning in Linear Systems. NeurIPS (2018)
Kun Zhang, Biwei Huang, Jiji Zhang, Clark Glymour, Bernhard Schölkopf
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination. IJCAI (2017)
Biwei Huang, Kun Zhang, Jiji Zhang, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf
Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows. ICDM (2017)
Kun Zhang, Jiji Zhang, Biwei Huang, Bernhard Schölkopf, Clark Glymour
On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection. UAI (2016)
Kun Zhang, Biwei Huang, Bernhard Schölkopf, Michel Besserve, Masataka Watanabe, Dajiang Zhu
Towards Robust and Specific Causal Discovery from fMRI. CoRR (2015)
Biwei Huang, Kun Zhang, Bernhard Schölkopf
Identification of Time-Dependent Causal Model: A Gaussian Process Treatment. IJCAI (2015)