Kolloquium SoSe 2024

Dr. Krikamol Muandet

CISPA Helmholtz Center for Information Security

Wann: Mo, 24.06.2024, 16:15 Uhr

Wo: Konrad-Zuse-Hörsaal (Raum B-201)

Thema

On Imprecise Generalisation: From Invariance to Heterogeneity

Sprache: English

Abstract

The ability to generalise knowledge across diverse environments stands as a fundamental aspect of both biological and artificial intelligence (AI). In recent years, significant advancements have been made in out-of-domain (OOD) generalisation, including the development of new algorithmic tools, theoretical advancements, and the creation of large-scale benchmark datasets. However, unlike in-domain (IID) generalisation, OOD generalisation lacks a precise definition, leading to ambiguity in learning objectives.

In this talk, I aim to clarify this ambiguity by arguing that OOD generalisation is challenging because it involves not only learning from empirical data but also deciding among various notions of generalisation. The intersection of learning and decision-making poses new challenges in modern machine learning, where distinct roles exist between machine learners (e.g., ML engineers) and model operators (e.g., doctors).

To address these challenges, I will introduce the concept of imprecise learning, drawing connections to imprecise probability, and discuss our upcoming ICML2024 paper in the context of domain generalisation (DG) problems. By exploring the synergy between learning algorithms and decision-making processes, this talk aims to shed light on the complexities of OOD generalisation and pave the way for future advancements in the field.

Paper: https://arxiv.org/abs/2404.04669

Bio

Krikamol Muandet is a chief scientist and tenure-track faculty member at CISPA Helmholtz Center for Information Security, Saarbrücken, Germany. Before joining CISPA, he was a research group leader in the Empirical Inference Department at the Max Planck Institute for Intelligent Systems (MPI-IS), Tübingen, Germany. He was a lecturer in the Department of Mathematics at Mahidol University, Bangkok, Thailand. He received his Ph.D. in computer science from the University of Tübingen in 2015 working mainly with Prof. Bernhard Schölkopf. He received his master's degree in machine learning from University College London (UCL), the United Kingdom where he worked mostly with Prof. Yee Whye Teh at Gatsby Computational Neuroscience Unit. He served as a publication chair of AISTATS 2021 and as an area chair for ICLR 2023, AISTATS 2022, NeurIPS 2021, NeurIPS 2020, NeurIPS 2019, and ICML 2019, among others.

Website: https://ri-lab.org/