3 citations · 3 across the 9 of their papers we have counts for
5 papers · 1 filter
Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data
Gongxu Luo, Loka Li, Guangyi Chen +2
Interventional causal discovery seeks to identify causal relations by leveraging distributional changes introduced by interventions, even in the presence of latent confounders. Bey…
PersonaX: Multimodal Datasets with LLM-Inferred Behavior Traits
Loka Li, Wong Yu Kang, Minghao Fu +7
Understanding human behavior traits is central to applications in human-computer interaction, computational social science, and personalized AI systems. Such understanding often re…
Causal Representation Learning from Multimodal Biomedical Observations
Yuewen Sun, Lingjing Kong, Guangyi Chen +10
Prevalent in biomedical applications (e.g., human phenotype research), multimodal datasets can provide valuable insights into the underlying physiological mechanisms. However, curr…
DAG: Projected Stochastic Approximation Iteration for DAG Structure Learning
Klea Ziu, Slavomír Hanzely, Loka Li +3
Learning the structure of Directed Acyclic Graphs (DAGs) presents a significant challenge due to the vast combinatorial search space of possible graphs, which scales exponentially…
Federated Causal Discovery from Heterogeneous Data
Loka Li, Ignavier Ng, Gongxu Luo +5
Conventional causal discovery methods rely on centralized data, which is inconsistent with the decentralized nature of data in many real-world situations. This discrepancy has moti…