Previous Events

19.03.21 Stefanie Jegelka (Machine Learning Group at MIT, USA)
Representation and Learning in Graph Neural Networks
04.11.20 Stefan Hollands (Leipzig University)
(In)determinism Inside Black Holes
06.10.20 Constantino Tsallis (Centro Brasileiro de Pesquisas Fisicas, Brazil)
Modern statistical mechanics – Approaching complexity in natural, artificial and social sciences
04.08.20 Marco Mondelli (IST Austria)
Understanding Gradient Descent for Over-parameterized Deep Neural Networks
20.07.20 Franca Hoffmann (California Institute of Technology)
Kalman-Wasserstein Gradient Flows
14.05.20 Lior Pachter (California Institute of Technology)
10.12.19 Mikhail Belkin (The Ohio State University, USA)
14.11.19 Kathlén Kohn (KTH Royal Institute of Technology, Stockholm)
The geometry of neural networks
23.10.19 Věra Kůrková (Institute of Computer Science, Czech Academy of Sciences, Czech Republic)
Lower Bounds on Complexity of Shallow Networks
18.09.19 Vladimir Temlyakov (University of South Carolina)
Supervised learning and sampling error of integral norms in function classes
16.07.19 Lamiae Azizi (The University of Sydney)
A Mathematical trip into the Data Science realm
28.05.19 Nicolas Garcia Trillos (Department of Statistics, University of Wisconsin-Madison, USA)
The use of geometry to learn from data, and the learning of geometry from data.
10.04.19 Gabriel Peyré (CNRS and Ecole Normale Supérieure, Paris, France)
Computational Optimal Transport for Data Sciences
07.03.19 Stefania Petra (Universität Heidelberg)
Compressed Sensing - From Theory To Practice
14.02.19 Felix Krahmer (Technische Universität München)
Blind deconvolution with randomness - convex geometry and algorithmic approaches
28.01.19 Nils Bertschinger (Frankfurt Institute for Advanced Studies - FIAS, Germany)
A geometric structure underlying stock correlations
08.11.18 Benjamin Fehrmann (University of Oxford)
Convergence rates for mean field stochastic gradient descent algorithms
27.09.18 Max von Renesse (Universität Leipzig)
Topics in Deterministic and Stochastic Dynamical Systems on Wasserstein Space
14.08.18 Afonso Bandeira (Courant Institute of Mathematical Sciences, New York)
Statistical estimation under group actions: The Sample Complexity of Multi-Reference Alignment
11.07.18 Harald Oberhauser (University of Oxford)
Learning laws of stochastic processes
18.06.18 Anna Seigal (University of California, Berkeley)
Structured Tensors and the Geometry of Data
02.05.18 Steffen Lauritzen (University of Copenhagen, Denmark)
Max-linear Bayesian networks
24.04.18 Benjamin Recht (University of California, Berkeley)
The statistical foundations of learning to control

Administrative Contact

Katharina Matschke

MPI for Mathematics in the Sciences Contact via Mail