PROGRAM

Title: TBD

Abstract: TBD

Title: TBD

Abstract: TBD

Title: Causal representation learning and causal generative AI

Abstract: Causality is a fundamental notion in science, engineering, and even machine learning and AI. Uncovering the causal process underlying observed data naturally helps answer 'why' and 'what-if' questions, informs optimal decisions, and enables adaptive prediction. In many scenarios, observed variables, such as image pixels and questionnaire responses, are often reflections of the underlying hidden causal variables rather than being causal variables themselves. Causal representation learning aims to reveal the underlying hidden causal variables and their relations. In this talk, we show how the modularity property of causal systems makes it possible to recover the underlying causal representations from observational data with identifiability guarantees: under suitable assumptions, the learned representations align with the underlying causal process. We further demonstrate how identifiable causal representation learning can directly benefit generative AI, using image generation / editing, text generation, and extrapolated data generation as illustrative examples.

Title: TBD

Abstract: TBD

Title: TBD

Abstract: TBD

Title: Seeing Domain Generalization in Foundation Models: A Nearest-Neighbor View

Abstract: Foundation models and vision-language models have changed the landscape of domain generalization. Trained on large and diverse web-scale data, they often appear robust across datasets, styles, and visual domains. But are they truly domain-general, or have they learned a broader set of correlations? In this talk, I will look at domain generalization through the geometry of learned representations. Using nearest-neighbor structure, clustering, and visualization, I will discuss when foundation-model embeddings organize images by semantic content, and when they remain entangled with domain, style, background, prompt vocabulary, or transformation-specific cues. The central message of the talk is that domain generalization in the foundation-model era is not only a question of better objectives or larger pretraining data. It is also a question of inspection: how can we visualize, cluster, query, and audit learned representation spaces before deploying models in open-world settings? A nearest-neighbor view provides a practical lens for understanding both the strengths and the hidden failure modes of modern domain-general models.