activity
20232025
most citedEmerging Synergies in Causality and Deep Generative Models: A Survey

7 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2025

Permutation-Based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data

Xinshuai Dong, Ignavier Ng, Boyang Sun +6

Recent advances have shown that statistical tests for the rank of cross-covariance matrices play an important role in causal discovery. These rank tests include partial correlation…

cs.LG2024

BayesAgent: Bayesian Agentic Reasoning Under Uncertainty via Verbalized Probabilistic Graphical Modeling

Hengguan Huang, Xing Shen, Songtao Wang +5

Human cognition excels at transcending sensory input and forming latent representations that structure our understanding of the world. While Large Language Model (LLM) agents demon…

cs.LG2024

Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees

Guang-Yuan Hao, Hengguan Huang, Haotian Wang +2

Active learning (AL) aims to improve model performance within a fixed labeling budget by choosing the most informative data points to label. Existing AL focuses on the single-domai…

cs.AI2024

Natural Counterfactuals With Necessary Backtracking

Guang-Yuan Hao, Jiji Zhang, Biwei Huang +2

Counterfactual reasoning is pivotal in human cognition and especially important for providing explanations and making decisions. While Judea Pearl's influential approach is theoret…

cs.LG2023★ 7 cited

Emerging Synergies in Causality and Deep Generative Models: A Survey

Guanglin Zhou, Shaoan Xie, Guang-Yuan Hao +7

In the field of artificial intelligence (AI), the quest to understand and model data-generating processes (DGPs) is of paramount importance. Deep generative models (DGMs) have prov…