8 papers
On the Recoverability of Causal Relations from Bulk Gene Expression Data
Gongxu Luo, Boyang Sun, Kun Zhang
Bulk gene expression profiling, which aggregates pooled RNA across cells within a biological sample, remains important in the single-cell era because it is typically less noisy, mo…
Multimodal LLMs under Pairwise Modalities
Yan Li, Yunlong Deng, Yuewen Sun +3
Despite the impressive results achieved by multimodal large language models (MLLMs), their training typically relies on jointly curated multimodal data, requiring substantial human…
A Dialogue between Causal and Traditional Representation Learning: Toward Mutual Benefits in a Unified Formulation
Yan Li, Yuewen Sun, Shaoan Xie +4
Causal representation learning (CRL) and traditional representation learning have largely developed along different trajectories. Traditional representation learning has been drive…
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…
Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders
Gongxu Luo, Haoyue Dai, Loka Li +3
Gene regulatory network inference (GRNI) aims to discover how genes causally regulate each other from gene expression data. It is well-known that statistical dependencies in observ…