2 citations · 2 across the 11 of their papers we have counts for
12 papers
Hessian Rank Constraint for Learning Structure of Nonlinear Latent Variable Models
Zijian Li, Ruichu Cai, Feng Xie +7
Uncovering latent variables and their causal relations from observed data is a fundamental yet challenging problem. Existing methods often rely on restrictive assumptions, such as…
Analogical Deep Research: Retrieving and Integrating Historical Analogies for Foresight Analysis
Yongqiang Chen, Guangyi Chen, Yuewen Sun +1
Systematic comparisons between current situations and structurally similar past events in the historical, i.e., historical analogies, is among the most powerful tools for foresight…
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…
Beyond the Black Box: Identifiable Interpretation and Control in Generative Models via Causal Minimality
Lingjing Kong, Shaoan Xie, Guangyi Chen +4
Deep generative models, while revolutionizing fields like image and text generation, largely operate as opaque ``black boxes'', hindering human understanding, control, and alignmen…
Towards Identifiability of Hierarchical Temporal Causal Representation Learning
Zijian Li, Minghao Fu, Junxian Huang +5
Modeling hierarchical latent dynamics behind time series data is critical for capturing temporal dependencies across multiple levels of abstraction in real-world tasks. However, ex…
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…
Generative Framework for Personalized Persuasion: Inferring Causal, Counterfactual, and Latent Knowledge
Donghuo Zeng, Roberto Legaspi, Yuewen Sun +4
We hypothesize that optimal system responses emerge from adaptive strategies grounded in causal and counterfactual knowledge. Counterfactual inference allows us to create hypotheti…
Causal Discovery and Counterfactual Reasoning to Optimize Persuasive Dialogue Policies
Donghuo Zeng, Roberto Legaspi, Yuewen Sun +4
Tailoring persuasive conversations to users leads to more effective persuasion. However, existing dialogue systems often struggle to adapt to dynamically evolving user states. This…
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…
On the Parameter Identifiability of Partially Observed Linear Causal Models
Xinshuai Dong, Ignavier Ng, Biwei Huang +5
Linear causal models are important tools for modeling causal dependencies and yet in practice, only a subset of the variables can be observed. In this paper, we examine the paramet…
Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome
Donghuo Zeng, Roberto S. Legaspi, Yuewen Sun +4
Customizing persuasive conversations related to the outcome of interest for specific users achieves better persuasion results. However, existing persuasive conversation systems rel…