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2020 – today
- 2024
[j3]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. J. Mach. Learn. Res. 25: 60:1-60:8 (2024)- [j2]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. J. Mach. Learn. Res. 25: 191:1-191:61 (2024) - [c32]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: 15193-15201 - [c31]Ignavier Ng, Biwei Huang, Kun Zhang:
Structure Learning with Continuous Optimization: A Sober Look and Beyond. CLeaR 2024: 71-105 - [c30]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: 1237-1263 - [c29]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 - [c28]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 - [c27]Longkang Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang:
Federated Causal Discovery from Heterogeneous Data. ICLR 2024 - [c26]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 - [c25]Qiang Huang, Chuizheng Meng, Defu Cao, Biwei Huang, Yi Chang, Yan Liu:
An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series. ICML 2024 - [c24]Ignavier Ng, Xinshuai Dong, Haoyue Dai, Biwei Huang, Peter Spirtes, Kun Zhang:
Score-Based Causal Discovery of Latent Variable Causal Models. ICML 2024 - [c23]Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong:
Optimal Kernel Choice for Score Function-based Causal Discovery. ICML 2024 - [c22]Yupei Yang, Biwei Huang, Shikui Tu, Lei Xu:
Boosting Efficiency in Task-Agnostic Exploration through Causal Knowledge. IJCAI 2024: 5344-5352 - [i43]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 abs/2401.09716 (2024) - [i42]Guang-Yuan Hao, Jiji Zhang, Biwei Huang, Hao Wang, Kun Zhang:
Natural Counterfactuals With Necessary Backtracking. CoRR abs/2402.01607 (2024) - [i41]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 abs/2402.06223 (2024) - [i40]Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen
, Tongliang Liu, Bin Gu, Kun Zhang:
Federated Causal Discovery from Heterogeneous Data. CoRR abs/2402.13241 (2024) - [i39]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 abs/2403.15711 (2024) - [i38]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 abs/2405.12217 (2024) - [i37]Lingjing Kong, Guangyi Chen, Biwei Huang, Eric P. Xing, Yuejie Chi, Kun Zhang:
Learning Discrete Concepts in Latent Hierarchical Models. CoRR abs/2406.00519 (2024) - [i36]Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong:
Optimal Kernel Choice for Score Function-based Causal Discovery. CoRR abs/2407.10132 (2024) - [i35]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 abs/2407.16975 (2024) - [i34]Yupei Yang, Biwei Huang, Shikui Tu, Lei Xu:
Boosting Efficiency in Task-Agnostic Exploration through Causal Knowledge. CoRR abs/2407.20506 (2024) - [i33]Qiang Huang, Chuizheng Meng, Defu Cao, Biwei Huang, Yi Chang, Yan Liu:
An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series. CoRR abs/2408.08815 (2024) - [i32]Haiyao Cao, Zhen Zhang, Panpan Cai, Yuhang Liu, Jinan Zou, Ehsan Abbasnejad, Biwei Huang, Mingming Gong, Anton van den Hengel, Javen Qinfeng Shi:
Rethinking State Disentanglement in Causal Reinforcement Learning. CoRR abs/2408.13498 (2024) - [i31]Yingyu Lin, Yuxing Huang, Wenqin Liu, Haoran Deng, Ignavier Ng, Kun Zhang, Mingming Gong, Yi-An Ma, Biwei Huang:
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery. CoRR abs/2410.06407 (2024) - [i30]Kaifeng Jin, Ignavier Ng, Kun Zhang, Biwei Huang:
Revisiting Differentiable Structure Learning: Inconsistency of ℓ1 Penalty and Beyond. CoRR abs/2410.18396 (2024) - [i29]Yuanyuan Wang, Biwei Huang, Wei Huang, Xi Geng, Mingming Gong:
Identifiability Analysis of Linear ODE Systems with Hidden Confounders. CoRR abs/2410.21917 (2024) - [i28]Xichen Guo, Zheng Li, Biwei Huang, Yan Zeng, Zhi Geng, Feng Xie:
Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models. CoRR abs/2411.12184 (2024) - [i27]Parjanya Prashant, Ignavier Ng, Kun Zhang, Biwei Huang:
Differentiable Causal Discovery For Latent Hierarchical Causal Models. CoRR abs/2411.19556 (2024) - 2023
- [c21]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 - [c20]Lingjing Kong, Biwei Huang, Feng Xie, Eric P. Xing, Yuejie Chi, Kun Zhang:
Identification of Nonlinear Latent Hierarchical Models. NeurIPS 2023 - [c19]Yuren Liu, Biwei Huang, Zhengmao Zhu, Hong-Long Tian, Mingming Gong, Yang Yu, Kun Zhang:
Learning World Models with Identifiable Factorization. NeurIPS 2023 - [c18]Yuanyuan Wang, Xi Geng, Wei Huang, Biwei Huang, Mingming Gong:
Generator Identification for Linear SDEs with Additive and Multiplicative Noise. NeurIPS 2023 - [i26]Ignavier Ng, Biwei Huang, Kun Zhang:
Structure Learning with Continuous Optimization: A Sober Look and Beyond. CoRR abs/2304.02146 (2023) - [i25]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 abs/2305.18427 (2023) - [i24]Shaoan Xie, Biwei Huang, Bin Gu, Tongliang Liu, Kun Zhang:
Advancing Counterfactual Inference through Quantile Regression. CoRR abs/2306.05751 (2023) - [i23]Yu-Ren Liu, Biwei Huang, Zheng-Mao Zhu, Hong-Long Tian, Mingming Gong, Yang Yu, Kun Zhang:
Learning World Models with Identifiable Factorization. CoRR abs/2306.06561 (2023) - [i22]Lingjing Kong, Biwei Huang, Feng Xie, Eric P. Xing, Yuejie Chi, Kun Zhang:
Identification of Nonlinear Latent Hierarchical Models. CoRR abs/2306.07916 (2023) - [i21]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 abs/2307.16405 (2023) - [i20]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 abs/2308.06718 (2023) - [i19]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 abs/2310.15580 (2023) - [i18]Yuanyuan Wang, Xi Geng, Wei Huang, Biwei Huang, Mingming Gong:
Generator Identification for Linear SDEs with Additive and Multiplicative Noise. CoRR abs/2310.19491 (2023) - [i17]Ziyan Wang, Yali Du, Yudi Zhang, Meng Fang, Biwei Huang:
MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment. CoRR abs/2312.03644 (2023) - [i16]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 abs/2312.11001 (2023) - 2022
- [c17]Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang:
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning. ICLR 2022 - [c16]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: 9260-9279 - [c15]Feng Xie, Biwei Huang, Zhengming Chen, Yangbo He, Zhi Geng, Kun Zhang:
Identification of Linear Non-Gaussian Latent Hierarchical Structure. ICML 2022: 24370-24387 - [c14]Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane:
Factored Adaptation for Non-Stationary Reinforcement Learning. NeurIPS 2022 - [c13]Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang:
Latent Hierarchical Causal Structure Discovery with Rank Constraints. NeurIPS 2022 - [i15]Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane:
Factored Adaptation for Non-Stationary Reinforcement Learning. CoRR abs/2203.16582 (2022) - [i14]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 abs/2208.14153 (2022) - [i13]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 abs/2208.14161 (2022) - [i12]Biwei Huang, Charles Jia Han Low, Feng Xie, Clark Glymour, Kun Zhang:
Latent Hierarchical Causal Structure Discovery with Rank Constraints. CoRR abs/2210.01798 (2022) - 2021
- [c12]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: 643-650 - [i11]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 abs/2103.14238 (2021) - [i10]Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang:
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning. CoRR abs/2107.02729 (2021) - [i9]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 abs/2110.05721 (2021) - 2020
- [j1]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: 89:1-89:53 (2020) - [c11]Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour:
Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets. AAAI 2020: 10153-10161 - [c10]Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Qingsong Liu, Clark Glymour:
Domain Adaptation as a Problem of Inference on Graphical Models. NeurIPS 2020 - [c9]Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang:
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. NeurIPS 2020 - [i8]Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Clark Glymour:
Domain Adaptation As a Problem of Inference on Graphical Models. CoRR abs/2002.03278 (2020) - [i7]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 abs/2010.04917 (2020) - [i6]Chaochao Lu, Biwei Huang, Ke Wang, José Miguel Hernández-Lobato, Kun Zhang, Bernhard Schölkopf:
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation. CoRR abs/2012.09092 (2020)
2010 – 2019
- 2019
- [c8]Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour:
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. ICML 2019: 2901-2910 - [c7]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: 13510-13521 - [i5]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 abs/1902.10073 (2019) - [i4]Biwei Huang, Kun Zhang, Jiji Zhang, Joseph D. Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf:
Causal Discovery from Heterogeneous/Nonstationary Data. CoRR abs/1903.01672 (2019) - [i3]Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour:
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. CoRR abs/1905.10857 (2019) - 2018
- [c6]Biwei Huang, Kun Zhang, Yizhu Lin, Bernhard Schölkopf, Clark Glymour:
Generalized Score Functions for Causal Discovery. KDD 2018: 1551-1560 - [c5]AmirEmad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang:
Multi-domain Causal Structure Learning in Linear Systems. NeurIPS 2018: 6269-6279 - [i2]Mingming Gong, Kun Zhang, Biwei Huang, Clark Glymour, Dacheng Tao, Kayhan Batmanghelich:
Causal Generative Domain Adaptation Networks. CoRR abs/1804.04333 (2018) - 2017
- [c4]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: 913-918 - [c3]Kun Zhang, Biwei Huang, Jiji Zhang, Clark Glymour, Bernhard Schölkopf:
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination. IJCAI 2017: 1347-1353 - 2016
- [c2]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 - 2015
- [c1]Biwei Huang, Kun Zhang, Bernhard Schölkopf:
Identification of Time-Dependent Causal Model: A Gaussian Process Treatment. IJCAI 2015: 3561-3568 - [i1]Kun Zhang, Biwei Huang, Bernhard Schölkopf, Michel Besserve, Masataka Watanabe, Dajiang Zhu:
Towards Robust and Specific Causal Discovery from fMRI. CoRR abs/1509.08056 (2015)
Coauthor Index
[j3] [j2] [c32] [c31] [c29] [c28] [c27] [c26] [c24] [c23] [i43] [i41] [i40] [i39] [i37] [i36] [i35] [i31] [i27] [c20] [c19] [i26] [i24] [i23] [i22] [i21] [i20] [i19] [i16] [c17] [c16] [c15] [c14] [c13] [i15] [i14] [i13] [i12] [c12] [i11] [i10] [i9] [j1] [c11] [c10] [c9] [i8] [i7] [i6] [c8] [c7] [i5] [i4] [i3] [c6] [c5] [i2] [c4] [c3] [c2] [c1] [i1]
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