Biwei Huang

发表

Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering

Pengtao Xie, Eric P. Xing, Kun Zhang, 2019, NeurIPS.

Causal Generative Domain Adaptation Networks pdf

Dacheng Tao, Mingming Gong, Clark Glymour, 2018, ArXiv.

Causal Discovery and Hidden Driving Force Estimation from Nonstationary/Heterogeneous Data

C. Glymour, B. Schölkopf, Kun Zhang, 2019 .

Material for “ Causal Discovery and Forecasts in Nonstationary Environments with State-Space Models ”

C. Glymour, Mingming Gong, Kun Zhang, 2019 .

ING FOR CONTROL WITH STRUCTURAL CONSTRAINTS

C. Glymour, B. Schölkopf, Chaochao Lu, 2022 .

Action-Sufficient State Representation Learning for Control with Structural Constraints

José Miguel Hernández-Lobato, C. Glymour, B. Schölkopf, 2021, ICML.

TETRAD - A TOOLBOX FOR CAUSAL DISCOVERY

C. Glymour, E. Barnes, Kun Zhang, 2018 .

Identifying Latent Causal Content for Multi-Source Domain Adaptation

Mingming Gong, Biwei Huang, J. Shi, 2022, ArXiv.

Weight-variant Latent Causal Models

A. V. Hengel, Mingming Gong, Dong Gong, 2022, ArXiv.

Identifying Weight-Variant Latent Causal Models

A. V. Hengel, Mingming Gong, Biwei Huang, 2022, 2208.14153.

Identifying Latent Causal Content for Multi-Source Domain Adaptation

Mingming Gong, Biwei Huang, J. Shi, 2022, ArXiv.

TRANSFER REINFORCEMENT LEARNING

Biwei Huang, Fan Feng, Kun Zhang, 2022 .

Factored Adaptation for Non-Stationary Reinforcement Learning

Biwei Huang, Sara Magliacane, Fan Feng, 2022, NeurIPS.

Latent Hierarchical Causal Structure Discovery with Rank Constraints

C. Glymour, Biwei Huang, Feng Xie, 2022, NeurIPS.

Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods

Clark Glymour, Ruben Sanchez-Romero, Biwei Huang, 2019, Network Neuroscience.

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

Clark Glymour, Kun Zhang, Joseph Ramsey, 2019, ArXiv.

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

Kun Zhang, Clark Glymour, Mingming Gong, 2020, AAAI.

Domain Adaptation As a Problem of Inference on Graphical Models pdf

Kun Zhang, Petar Stojanov, Clark Glymour, 2020, NeurIPS.

Causal Discovery from Heterogeneous/Nonstationary Data pdf

Bernhard Schölkopf, Kun Zhang, Clark Glymour, 2019, J. Mach. Learn. Res..

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

Bernhard Schölkopf, Clark Glymour, Kun Zhang, 2017, IJCAI.

Discovery and Visualization of Nonstationary Causal Models

Kun Zhang, Clark Glymour, Jiji Zhang, 2015, 1509.08056.

On Causal Discovery in the Presence of Changing Causal Models

Kun Zhang, Clark Glymour, Jiji Zhang, 2015 .

Towards Robust and Specific Causal Discovery from fMRI pdf

Bernhard Schölkopf, Dajiang Zhu, Kun Zhang, 2015, ArXiv.

Generalized Score Functions for Causal Discovery

Bernhard Schölkopf, Clark Glymour, Kun Zhang, 2018, KDD.

Multi-domain Causal Structure Learning in Linear Systems

Kun Zhang, AmirEmad Ghassami, Negar Kiyavash, 2018, NeurIPS.

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

Chaochao Lu, Biwei Huang, Kun Zhang, 2021, ICLR.

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

Bernhard Schölkopf, Clark Glymour, Jiji Zhang, 2017, 2017 IEEE International Conference on Data Mining (ICDM).

Causal Discovery of Feedback Networks with Functional Magnetic Resonance Imaging

Kun Zhang, Clark Glymour, Joseph Ramsey, 2018, bioRxiv.

Identification of Linear Non-Gaussian Latent Hierarchical Structure

Yangbo He, Z. Geng, Biwei Huang, 2022, ICML.

Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs

C. Glymour, Kun Zhang, Biwei Huang, 2020, NeurIPS.

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

Kun Zhang, Clark Glymour, Biwei Huang, 2019, ICML.

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

Ruichu Cai, Clark Glymour, Zhifeng Hao, 2021, ArXiv.

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

Jos'e Miguel Hern'andez-Lobato, José Miguel Hernández-Lobato, Kun Zhang, 2020, ArXiv.

Structure Learning with Continuous Optimization: A Sober Look and Beyond

Biwei Huang, Ignavier Ng, Kun Zhang, 2023, CLeaR.

Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes.

Clark Glymour, Jiji Zhang, Bernhard Scholkopf, 2019 .

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

Ruichu Cai, Clark Glymour, Zhifeng Hao, 2020, ArXiv.

DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding

Lei Xu, Shikui Tu, Biwei Huang, 2021, AAAI.

Identification of Nonlinear Latent Hierarchical Models

E. Xing, Yuejie Chi, Biwei Huang, 2023, ArXiv.

Learning World Models with Identifiable Factorization

Mingming Gong, Biwei Huang, Yang Yu, 2023, NeurIPS.

Advancing Counterfactual Inference through Quantile Regression

Shaoan Xie, Biwei Huang, Kun Zhang, 2023, ArXiv.

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

Ziyan Wang, Jun Wang, Mykola Pechenizkiy, 2023, ArXiv.

Identifying Weight-Variant Latent Causal Models

Mingming Gong, Biwei Huang, Kun Zhang, 2022, 2208.14153.

Emerging Synergies in Causality and Deep Generative Models: A Survey

Shaoan Xie, Xiwei Xu, Biwei Huang, 2023, 2301.12351.

Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods

C. Glymour, Kun Zhang, Biwei Huang, 2019, Network Neuroscience.

Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach

Jun Wang, Mykola Pechenizkiy, Biwei Huang, 2023, NeurIPS.

Emerging Synergies in Causality and Deep Generative Models: A Survey

Shaoan Xie, Xiwei Xu, Biwei Huang, 2023, 2301.12351.

Domain Adaptation As a Problem of Inference on Graphical Models

C. Glymour, Mingming Gong, Kun Zhang, 2020, NeurIPS.

A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables

P. Spirtes, Biwei Huang, Ignavier Ng, 2023, ArXiv.

Identification of Nonlinear Latent Hierarchical Models

E. Xing, Yuejie Chi, Biwei Huang, 2023, NeurIPS.

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables

C. Glymour, Biwei Huang, Zhen Chen, 2023, ArXiv.

HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization

Biwei Huang, Guanglin Zhou, Liming Zhu, 2024, ArXiv.

ACAMDA: Improving Data Efficiency in Reinforcement Learning through Guided Counterfactual Data Augmentation

Biwei Huang, Yuewen Sun, Erli Wang, 2024, AAAI.

Identifiable Latent Neural Causal Models

Biwei Huang, Javen Qinfeng Shi, A. Hengel, 2024, ArXiv.

Advancing Counterfactual Inference through Nonlinear Quantile Regression

Shaoan Xie, Biwei Huang, Bin Gu, 2023, 2306.05751.

On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection

C. Glymour, Kun Zhang, Jiji Zhang, 2016, UAI.

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

Bernhard Schölkopf, Kun Zhang, Biwei Huang, 2015, IJCAI.

On the Parameter Identifiability of Partially Observed Linear Causal Models

Biwei Huang, Ignavier Ng, Xinshuai Dong, 2024, 2407.16975.

Revealing Multimodal Contrastive Representation Learning through Latent Partial Causal Models

Biwei Huang, Yuhang Liu, Javen Qinfeng Shi, 2024, ArXiv.

Natural Counterfactuals With Necessary Backtracking

Biwei Huang, Guang-Yuan Hao, Hao Wang, 2024, ArXiv.

Identifying Weight-Variant Latent Causal Models

Mingming Gong, Biwei Huang, Kun Zhang, 2022, 2208.14153.