Biwei Huang

Biwei Huang

Carnegie Mellon University

H-index: 20

North America-United States

About Biwei Huang

Biwei Huang, With an exceptional h-index of 20 and a recent h-index of 20 (since 2020), a distinguished researcher at Carnegie Mellon University, specializes in the field of Causality, Machine Learning, Computational Neuroscience.

His recent articles reflect a diverse array of research interests and contributions to the field:

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

Causal-learn: Causal discovery in python

Identifiable Latent Neural Causal Models

Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach

Structure learning with continuous optimization: A sober look and beyond

Federated Causal Discovery from Heterogeneous Data

Interpretable reward redistribution in reinforcement learning: A causal approach

Generator Identification for Linear SDEs with Additive and Multiplicative Noise

Biwei Huang Information

University

Position

___

Citations(all)

1220

Citations(since 2020)

1175

Cited By

245

hIndex(all)

20

hIndex(since 2020)

20

i10Index(all)

25

i10Index(since 2020)

23

Email

University Profile Page

Google Scholar

Biwei Huang Skills & Research Interests

Causality

Machine Learning

Computational Neuroscience

Top articles of Biwei Huang

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

2024

Causal-learn: Causal discovery in python

Journal of Machine Learning Research

2024

Identifiable Latent Neural Causal Models

arXiv preprint arXiv:2403.15711

2024/3/23

Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach

2024/3/15

Biwei Huang
Biwei Huang

H-Index: 10

Mingming Gong
Mingming Gong

H-Index: 9

Structure learning with continuous optimization: A sober look and beyond

2024/3/15

Biwei Huang
Biwei Huang

H-Index: 10

Kun Zhang
Kun Zhang

H-Index: 13

Federated Causal Discovery from Heterogeneous Data

arXiv preprint arXiv:2402.13241

2024/2/20

Interpretable reward redistribution in reinforcement learning: A causal approach

NeurIPS 2023: Advances in Neural Information Processing Systems

2024/2/13

Generator Identification for Linear SDEs with Additive and Multiplicative Noise

Advances in Neural Information Processing Systems

2024/2/13

Identification of nonlinear latent hierarchical models

2023/12

Learning World Models with Identifiable Factorization

arXiv preprint arXiv:2306.06561

2023/6/11

Revealing Multimodal Contrastive Representation Learning through Latent Partial Causal Models

arXiv preprint arXiv:2402.06223

2024/2/9

Natural Counterfactuals With Necessary Backtracking

arXiv preprint arXiv:2402.01607

2024/2/2

HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization

arXiv preprint arXiv:2401.09716

2024/1/18

A versatile causal discovery framework to allow causally-related hidden variables

ICLR 2024

2023/12/18

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

arXiv preprint arXiv:2312.03644

2023/12/6

Identifiable Latent Polynomial Causal Models Through the Lens of Change

arXiv preprint arXiv:2310.15580

2023/10/24

Analytic DAG Constraints for Differentiable DAG Learning

2023/10/13

Identifiable Latent Causal Content for Domain Adaptation under Latent Covariate Shift

2023/10/13

Optimal Kernel Choice for Score Function-based Causal Discovery

2023/10/13

Decoupling Intrinsic and Measurement Trends: A Crucial Consideration in Time Series Causal Discovery

2023/10/13

See List of Professors in Biwei Huang University(Carnegie Mellon University)

Co-Authors

academic-engine