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Center for Interdisciplinary Research (ZiF), Bielefeld University
Plenarsaal
conference
04.08.21 07.08.21

Conference on Mathematics of Machine Learning

The conference will take place in a hybrid format, allowing both in person and online attendance. The submission of contributed talks and posters is now closed.

In recent years, machine learning algorithms have seen an unprecedented success in a variety of fields, ranging from science and engineering to medicine and social sciences. As a result, artificial intelligence has been identified as one of the key technologies for future social and economic advance. With its rising importance also in failure sensitive systems, for example in medical devices and autonomous driving, the need for a systematic understanding of the functionality of machine learning algorithms and for guarantees on their accuracy and precision becomes vital. This calls for the development of a mathematical understanding of the structures and mechanism underlying the success of machine learning techniques. This endeavor has seen a significant increase of interest and efforts in recent years, crucially relying on interdisciplinary interaction, communicating mathematical progress and empirical research. The proposed conference is aimed as a contribution to this rapidly developing field, by bringing together experts from various mathematical areas with shared interest in applications to machine learning and experts from fields such as computer science and biology.

Please see also the poster overview at Poster Session

Speakers

Yasaman Bahri

Google Brain, USA

Pierre Baldi

University of California, Irvine, USA

Peter Bartlett

University of California, Berkeley, USA

Minh Hà Quang

RIKEN-AIP, Japan

Matthias Hein

University of Tübingen, Germany

Stefanie Jegelka

Massachusetts Institute of Technology, USA

Arnulf Jentzen

Universität Münster, Germany

Kathlén Kohn

KTH - Royal Institute of Technology, Sweden

Věra Kůrková

Czech Academy of Sciences, Czech Republic

Jason Lee

Princeton University, USA

Stanley Osher

University of California, Los Angeles, USA

Jeffrey Pennington

Google Brain, USA

Stefano Soatto

University of California, Los Angeles, USA

Ingo Steinwart

University of Stuttgart, Germany

Dmitry Yarotsky

Skoltech, Russia

Lenka Zdeborová

EPFL - École Polytechnique Fédérale de Lausanne, Switzerland

Program

10:00 - 10:30
10:30 - 11:00
11:00 - 12:00
12:00 - 12:30
12:30 - 13:00
13:00 - 14:00
14:00 - 14:30
14:30 - 15:00 Florent Krzakala (EPFL (Lausanne), Switzerland)
Generalization & Overparametrization in Machine Learning: Rigorous Insights from Simple Models
15:00 - 15:30
15:30 - 16:00 Oxana Manita (Eindhoven University of Technology, Netherlands)
Dropout regularization viewed from the large deviations perspective
16:00 - 16:30
16:30 - 17:00
17:00 - 18:00
18:00 - 19:00
19:00 - 00:00
09:30 - 10:30
10:30 - 11:00
11:00 - 12:00
12:00 - 12:30
12:30 - 14:00
14:00 - 15:00
Poster Session
15:00 - 15:30
15:30 - 16:30
16:30 - 17:00
17:00 - 18:00
18:00 - 19:00
09:30 - 10:30
10:30 - 11:00
11:00 - 12:00
12:00 - 12:30 Luigi Malagò (Transylvanian Institute of Neuroscience (TINS) (Cluj-Napoca), Romania)
Lagrangian and Hamiltonian Mechanics for Probabilities on the Statistical Bundle
12:30 - 13:00
13:00 - 14:00
14:00 - 14:30
14:30 - 15:00
15:00 - 15:30
15:30 - 16:30
16:30 - 17:00
17:00 - 17:30
17:30 - 18:00
18:00 - 19:00
19:00 - 00:00
09:30 - 10:30
10:30 - 11:00
11:00 - 12:00
12:00 - 12:30
12:30 - 13:00
13:00 - 14:00
14:00 - 14:30 Guido Montúfar (Max Planck Institute for Mathematics in the Sciences (Leipzig), Germany)
Implicit bias of gradient descent for mean squared error regression with wide neural networks
14:30 - 15:00
15:00 - 15:30
15:30 - 16:00
16:00 - 16:30
16:30 - 17:00
17:00 - 18:00
18:00 - 19:00

Scientific Organizers

Benjamin Gess

Max Planck Institute for Mathematics in the Sciences and Universität Bielefeld

Guido Montúfar

Max Planck Institute for Mathematics in the Sciences and UCLA

Nihat Ay

Hamburg University of Technology