Past talk · AI/ML Seminar Series
Causal Representation Learning: Discovery of the Hidden World
Professor and Director, Center for Integrative AI, Mohamed bin Zayed University of Artificial Intelligence
- Date & time
- Friday, April 5, 2024 · 3:00 PM
- Location
- Donald Bren Hall 2011
Abstract
Causality is a fundamental notion in science, engineering, and even in machine learning. Uncovering the causal process behind observed data can naturally help answer why and how questions, inform optimal decisions, and achieve adaptive prediction. In many scenarios, observed variables (such as image pixels and questionnaire results) are often reflections of the underlying causal variables rather than being the causal variables themselves. Causal representation learning aims to reveal the underlying high-level hidden causal variables and their relations. The modularity property of a causal system implies properties of minimal changes and independent changes of causal representations, and in this talk, we show how such properties make it possible to recover the underlying causal representations from observational data with identifiability guarantees. Various problem settings are considered, involving i.i.d. data, temporal data, or data with distribution shift as input.
About the speaker
Kun Zhang is currently on leave from Carnegie Mellon University (CMU), where he is an associate professor of philosophy and an affiliate faculty in the machine learning department; he is working as a professor and the acting chair of the machine learning department and the director of the Center for Integrative AI at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). He develops methods for making causality transparent and investigates machine learning problems including transfer learning, representation learning, and reinforcement learning from a causal perspective. He was a co-founder and general & program co-chair of the first Conference on Causal Learning and Reasoning (CLeaR 2022), a program co-chair of UAI 2022, and a general co-chair of UAI 2023.