Biwei Huang

C-World: A Computer Use Agent Environment Creator ACL 2026 DreamSAC: Learning Hamiltonian World Models via Symmetry Exploration CVPR 2026 Identifying Weight-Variant Latent Causal Models JMLR 2026 Revisiting Differentiable Structure Learning: Inconsistency of L1 Penalty and Beyond AAAI 2026 Analytic DAG Constraints for Differentiable DAG Learning ICLR 2025 A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery ICLR 2025 Differentiable Causal Discovery for Latent Hierarchical Causal Models ICLR 2025 MissScore: High-Order Score Estimation in the Presence of Missing Data ICML 2025 Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning ICLR 2025 Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations ICLR 2025 ACAMDA: Improving Data Efficiency in Reinforcement Learning through Guided Counterfactual Data Augmentation AAAI 2024 An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series ICML 2024 A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables ICLR 2024 Boosting Efficiency in Task-Agnostic Exploration through Causal Knowledge IJCAI 2024 Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach CLeaR 2024 Causal-learn: Causal Discovery in Python JMLR 2024 Federated Causal Discovery from Heterogeneous Data ICLR 2024 Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables JMLR 2024 Identifiability Analysis of Linear ODE Systems with Hidden Confounders NeurIPS 2024 Identifiable Latent Polynomial Causal Models through the Lens of Change ICLR 2024 Identifying Latent State-Transition Processes for Individualized Reinforcement Learning NeurIPS 2024 Learning Discrete Concepts in Latent Hierarchical Models NeurIPS 2024 Natural Counterfactuals With Necessary Backtracking NeurIPS 2024 On Causal Discovery in the Presence of Deterministic Relations NeurIPS 2024 On the Parameter Identifiability of Partially Observed Linear Causal Models NeurIPS 2024 Optimal Kernel Choice for Score Function-based Causal Discovery ICML 2024 Score-Based Causal Discovery of Latent Variable Causal Models ICML 2024 Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability ICLR 2024 Structure Learning with Continuous Optimization: A Sober Look and Beyond CLeaR 2024 Generator Identification for Linear SDEs with Additive and Multiplicative Noise NeurIPS 2023 Identification of Nonlinear Latent Hierarchical Models NeurIPS 2023 Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach NeurIPS 2023 Learning World Models with Identifiable Factorization NeurIPS 2023 Action-Sufficient State Representation Learning for Control with Structural Constraints ICML 2022 AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning ICLR 2022 Factored Adaptation for Non-Stationary Reinforcement Learning NeurIPS 2022 Identification of Linear Non-Gaussian Latent Hierarchical Structure ICML 2022 Latent Hierarchical Causal Structure Discovery with Rank Constraints NeurIPS 2022 DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding AAAI 2021 Causal Discovery from Heterogeneous/Nonstationary Data JMLR 2020 Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets AAAI 2020 Domain Adaptation as a Problem of Inference on Graphical Models NeurIPS 2020 Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs NeurIPS 2020 Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models ICML 2019 Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering NeurIPS 2019 Multi-domain Causal Structure Learning in Linear Systems NeurIPS 2018 Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination IJCAI 2017 Identification of Time-Dependent Causal Model: A Gaussian Process Treatment IJCAI 2015