Biwei Huang
Assistant Professor
Develops causal discovery methods with theoretical guarantees and causality-inspired machine learning.
Biwei Huang's lab at UCSD works on probabilistic & causal inference, learning theory & optimization and data management & big data. 35 papers since 2023, 32 at top venues, mostly NeurIPS and ICLR. 5 PhD students and 8 master's students. Applications close .
At a glance
- papers, 2023–2026
- 9/ year
- at T1 venues
- 91%
- PhD students
- 5
- MS students
- 8
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 11 papers2025 · T1 11
- 20 papers2024 · T1 17
- 4 papers2023 · T1 4
T1 venuesOtherVenues: NeurIPS, ICLR, ICML, JMLR, AAAI
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference17 papers
- Learning theory & optimization9 papers
- causal discovery9 papers
- Data management & big data3 papers
- theoretical0 papers
Paper list
From the lab homepage.
202511 papers
- ICLR (T1)A Skewness-based Criterion for Addressing Heterogeneous Noise in Causal Discovery
- NeurIPS (T1)Activation Control for Efficient Eliciting Long Chain-of-Thought Ability of Language Models
- ICLR (T1)Analytic DAG Constraints for Differential DAG Learning
- NeurIPS (T1)Causality Meets Locality: Provably Generalizable and Scalable Policy Learning for Network Systems
- ICLR (T1)Differential Causal Discovery for Latent Hierarchical Causal Models
- TMLR (T1)Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation
- ICML (T1)MissScore: High-Order Score Estimation in the Presence of Missing Data
- ICLR (T1)Modeling Unseen Environments with Language-Guided Composable Causal Components in Reinforcement Learning
- NeurIPS (T1)Practical Kernel Selection for Kernel-Based Conditional Independence Test
- NeurIPS (T1)Towards General Continuous Memory for Vision-Language Models
- ICLR (T1)Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations
202420 papers
- ICLR (T1)A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
- AAAI (T1)ACAMDA: Improving Data Efficiency in Reinforcement Learning Through Guided Counterfactual Data Augmentation
- ICML (T1)An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series
- IJCAI (T1)Boosting Efficiency in Task-Agnostic Exploration Through Causal Knowledge
- CLeaRCausal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach
- JMLR (T1)Causal-learn: Causal Discovery in Python
- ICLR (T1)Federated Causal Discovery from Heterogeneous Data
- JMLR (T1)Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
- TMMHVCP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization
- NeurIPS (T1)Identifiability Analysis of Linear ODE Systems with Hidden Confounders
- ICLR (T1)Identifiable Latent Polynomial Causal Models through the Lens of Change
- NeurIPS (T1)Identifying Latent State-Transition Processes for Individualized Reinforcement Learning
20234 papers
- NeurIPS (T1)Generator Identification for Linear SDEs with Additive and Multiplicative Noise
- NeurIPS (T1)Identification of Nonlinear Latent Hierarchical Models
- NeurIPS (T1)Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach
- NeurIPS (T1)Learning World Models with Identifiable Factorization
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UCSDFall 2027
UCSD CSE Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
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- Fee
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Not yet confirmed for this intake.
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