Biwei Huang

According to our database, Biwei Huang authored at least 60 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Causal-learn: Causal Discovery in Python.

Yujia Zheng, Biwei Huang, Wei Chen, Joseph D. Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang

J. Mach. Learn. Res., 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

J. Mach. Learn. Res., 2024

Identifiability Analysis of Linear ODE Systems with Hidden Confounders.

Yuanyuan Wang, Biwei Huang, Wei Huang, Xi Geng, Mingming Gong

CoRR, 2024

Revisiting Differentiable Structure Learning: Inconsistency of ℓ₁ Penalty and Beyond.

Kaifeng Jin, Ignavier Ng, Kun Zhang, Biwei Huang

CoRR, 2024

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, Yi-An Ma, Biwei Huang

CoRR, 2024

Rethinking State Disentanglement in Causal Reinforcement Learning.

Haiyao Cao, Zhen Zhang, Panpan Cai, Yuhang Liu, Jinan Zou, Ehsan Abbasnejad, Biwei Huang, Mingming Gong, Anton van den Hengel, Javen Qinfeng Shi

CoRR, 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

CoRR, 2024

Learning Discrete Concepts in Latent Hierarchical Models.

Lingjing Kong, Guangyi Chen, Biwei Huang, Eric P. Xing, Yuejie Chi, Kun Zhang

CoRR, 2024

Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learning.

Guanglin Zhou, Zhongyi Han, Shiming Chen, Biwei Huang, Liming Zhu, Salman Khan, Xinbo Gao, Lina Yao

CoRR, 2024

Identifiable Latent Neural Causal Models.

Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi

CoRR, 2024

Federated Causal Discovery from Heterogeneous Data.

Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang

CoRR, 2024

Revealing Multimodal Contrastive Representation Learning through Latent Partial Causal Models.

Yuhang Liu, Zhen Zhang, Dong Gong, Biwei Huang, Mingming Gong, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi

CoRR, 2024

Natural Counterfactuals With Necessary Backtracking.

Guang-Yuan Hao, Jiji Zhang, Biwei Huang, Hao Wang, Kun Zhang

CoRR, 2024

HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization.

Guanglin Zhou, Zhongyi Han, Shiming Chen, Biwei Huang, Liming Zhu, Tongliang Liu, Lina Yao, Kun Zhang

CoRR, 2024

Boosting Efficiency in Task-Agnostic Exploration through Causal Knowledge.

Yupei Yang, Biwei Huang, Shikui Tu, Lei Xu

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Optimal Kernel Choice for Score Function-based Causal Discovery.

Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Score-Based Causal Discovery of Latent Variable Causal Models.

Ignavier Ng, Xinshuai Dong, Haoyue Dai, Biwei Huang, Peter Spirtes, Kun Zhang

Proceedings of the Forty-first International Conference on Machine Learning, 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

Proceedings of the Forty-first International Conference on Machine Learning, 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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Federated Causal Discovery from Heterogeneous Data.

Longkang Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang

Proceedings of the Twelfth International Conference on Learning Representations, 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

Proceedings of the Twelfth International Conference on Learning Representations, 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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Structure Learning with Continuous Optimization: A Sober Look and Beyond.

Ignavier Ng, Biwei Huang, Kun Zhang

Proceedings of the Causal Learning and Reasoning, 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

Proceedings of the Causal Learning and Reasoning, 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

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment.

Ziyan Wang, Yali Du, Yudi Zhang, Meng Fang, Biwei Huang

CoRR, 2023

Learning World Models with Identifiable Factorization.

Yu-Ren Liu, Biwei Huang, Zheng-Mao Zhu, Hong-Long Tian, Mingming Gong, Yang Yu, Kun Zhang

CoRR, 2023

Advancing Counterfactual Inference through Quantile Regression.

Shaoan Xie, Biwei Huang, Bin Gu, Tongliang Liu, Kun Zhang

CoRR, 2023

GRD: A Generative Approach for Interpretable Reward Redistribution in Reinforcement Learning.

Yudi Zhang, Yali Du, Biwei Huang, Ziyan Wang, Jun Wang, Meng Fang, Mykola Pechenizkiy

CoRR, 2023

Generator Identification for Linear SDEs with Additive and Multiplicative Noise.

Yuanyuan Wang, Xi Geng, Wei Huang, Biwei Huang, Mingming Gong

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning World Models with Identifiable Factorization.

Yuren Liu, Biwei Huang, Zhengmao Zhu, Hong-Long Tian, Mingming Gong, Yang Yu, Kun Zhang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Identification of Nonlinear Latent Hierarchical Models.

Lingjing Kong, Biwei Huang, Feng Xie, Eric P. Xing, Yuejie Chi, Kun Zhang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach.

Yudi Zhang, Yali Du, Biwei Huang, Ziyan Wang, Jun Wang, Meng Fang, Mykola Pechenizkiy

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Identifying Latent Causal Content for Multi-Source Domain Adaptation.

Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Kun Zhang, Javen Qinfeng Shi

CoRR, 2022

Weight-variant Latent Causal Models.

Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi

CoRR, 2022

Latent Hierarchical Causal Structure Discovery with Rank Constraints.

Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Factored Adaptation for Non-Stationary Reinforcement Learning.

Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Identification of Linear Non-Gaussian Latent Hierarchical Structure.

Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 2022

AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning.

Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders.

Wei Chen, Kun Zhang, Ruichu Cai, Biwei Huang, Joseph D. Ramsey, Zhifeng Hao, Clark Glymour

CoRR, 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

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Causal Discovery from Heterogeneous/Nonstationary Data.

Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf

J. Mach. Learn. Res., 2020

Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation.

Chaochao Lu, Biwei Huang, Ke Wang, José Miguel Hernández-Lobato, Kun Zhang, Bernhard Schölkopf

CoRR, 2020

Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs.

Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang

CoRR, 2020

Domain Adaptation As a Problem of Inference on Graphical Models.

Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Clark Glymour

CoRR, 2020

Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs.

Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Domain Adaptation as a Problem of Inference on Graphical Models.

Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Qingsong Liu, Clark Glymour

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets.

Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data.

Biwei Huang, Kun Zhang, Ruben Sanchez-Romero, Joseph D. Ramsey, Madelyn Glymour, Clark Glymour

CoRR, 2019

Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering.

Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric P. Xing, Clark Glymour

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models.

Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Causal Generative Domain Adaptation Networks.

Mingming Gong, Kun Zhang, Biwei Huang, Clark Glymour, Dacheng Tao, Kayhan Batmanghelich

CoRR, 2018

Multi-domain Causal Structure Learning in Linear Systems.

AmirEmad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Generalized Score Functions for Causal Discovery.

Biwei Huang, Kun Zhang, Yizhu Lin, Bernhard Schölkopf, Clark Glymour

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

2017

Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination.

Kun Zhang, Biwei Huang, Jiji Zhang, Clark Glymour, Bernhard Schölkopf

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows.

Biwei Huang, Kun Zhang, Jiji Zhang, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf

Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

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

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

2015

Towards Robust and Specific Causal Discovery from fMRI.

Kun Zhang, Biwei Huang, Bernhard Schölkopf, Michel Besserve, Masataka Watanabe, Dajiang Zhu

CoRR, 2015

Identification of Time-Dependent Causal Model: A Gaussian Process Treatment.

Biwei Huang, Kun Zhang, Bernhard Schölkopf

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015