AI Center - Research Seminar Series - Francis Bach

The talk is organized by the EPFL AI Center as part of the its main Research seminar series.

Title
Optimization in Machine Learning: From Convexity to Non-Convexity

Abstract
Optimization algorithms—such as gradient descent and its stochastic variants—are fundamental tools in modern machine learning. Over the past fifteen years, the research landscape has evolved significantly: the early emphasis on convex optimization with strong theoretical guarantees (particularly for linear models) has gradually shifted toward the challenges of non-convex optimization, which underpins more complex models like neural networks and often lacks such guarantees. In this talk, I will survey key theoretical insights and empirical findings from both domains, highlighting the role of convexity—whether explicit or implicit—in shaping our understanding of optimization. I will also discuss emerging directions for future research, both within machine learning and in the broader context of optimization theory.

Bio
Francis Bach is a researcher at Inria, leading since 2011 the machine learning team which is part of the Computer Science department at Ecole Normale Supérieure. He graduated from Ecole Polytechnique in 1997 and completed his Ph.D. in Computer Science at U.C. Berkeley in 2005, working with Professor Michael Jordan. He spent two years in the Mathematical Morphology group at Ecole des Mines de Paris; then he joined the computer vision project-team at Inria/Ecole Normale Supérieure from 2007 to 2010. Francis Bach is primarily interested in machine learning, and especially in sparse methods, kernel-based learning, neural networks, and large-scale optimization. He published the book "Learning Theory from First Principles" through MIT Press in 2024. He obtained in 2009 a Starting Grant and in 2016 a Consolidator Grant from the European Research Council, and received the Inria young researcher prize in 2012, the ICML test-of-time award in 2014 and 2019, the NeurIPS test-of-time award in 2021, as well as the Lagrange prize in continuous optimization in 2018, and the Jean-Jacques Moreau prize in 2019. He was elected in 2020 at the French Academy of Sciences. In 2015, he was program co-chair of the International Conference in Machine learning (ICML), general chair in 2018, and president of its board between 2021 and 2023; he was co-editor-in-chief of the Journal of Machine Learning Research between 2018 and 2023.