Past Talks in Joint Analysis Seminar

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Past Talks in Post Graduate Seminar

11-28-2022, 11:30 AM

Jingzhao Zhang (Tsinghua University):
On the (Non)smoothness of Neural Network Training

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11-07-2022, 12:30 PM

Maxime Nguegnang (RWTH Aachen University):
TBA

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10-31-2022, 10:00 AM

Zhiyuan Li (Toyota Technological Institute at Chicago):
Understanding Gradient Descent on Edge of Stability in Deep Learning

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10-24-2022, 12:30 PM

Jeremy M. Cohen (Carnegie Mellon University):
The dynamics of optimization in deep learning

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09-07-2022, 03:00 PM

Ekkehard Schnoor (RWTH Aachen University):
Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm

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07-15-2022, 10:30 AM

Daniel Soudry (Technion):
On stability and efficiency in deep learning

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07-11-2022, 12:30 PM

Frederik Hoppe (RWTH Aachen University):
Confidence intervals for compressive MRI

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07-04-2022, 12:30 PM

Robert J. Kunsch (RWTH Aachen University):
How much randomness is needed for high-confidence Monte Carlo integration?

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06-13-2022, 12:30 PM

Laura Paul (RWTH Aachen University):
Covariance Estimation for Massive MIMO

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06-07-2022, 04:30 PM

Mikhail Belkin (UCSD):
TBA

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05-30-2022, 12:30 PM

Leonardo Galli (RWTH Aachen University):
A Polyak Nonmonotone Stochastic Gradient Descent Method for Training Deep Neural Networks

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05-09-2022, 12:30 PM

Josef Teichmann (ETH Zürich):
Machine Learning in Finance via randomization

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04-25-2022, 12:30 PM

Francis Bach (INRIA):
Statistics, Machine Learning, and Optimization with Kernel Sums-of-Squares

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04-11-2022, 12:30 PM

Hung-Hsu Chou (RWTH Aachen University):
More is Less: Inducing Sparsity via Overparameterization

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04-04-2022, 04:30 PM

Sharan Vaswani (Simon Fraser University):
Towards Noise-adaptive, Problem-adaptive Stochastic Gradient Descent

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02-07-2022, 12:30 PM

Shahar Mendelson (University of Warwick):
Uniform preservation of tails

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01-31-2022, 04:30 PM

Mark Schmidt (University of British Columbia):
Faster Algorithms for Deep Learning?

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01-17-2022, 12:30 PM

Lyudmila Grigoryeva (University of Warwick):
Deep Learning for Discrete-time Pricing and Calibration

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01-10-2022, 04:30 PM

Katya Scheinberg (Cornell University):
Stochastic Oracles and Where to Find Them

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12-13-2021, 12:30 PM

Volkan Cevher (EPFL):
Optimization challenges in adversarial machine learning

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12-06-2021, 12:30 PM

Stefan Kunis (Universität Osnabrück):
Super-resolution of points and curves

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11-22-2021, 12:30 PM

Guido F. Montufar (UCLA):
Geometry of Linear Convolutional Networks

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11-15-2021, 12:30 PM

Ekkehard Schnoor (RWTH Aachen University):
Generalization Error Bounds for Iterative Recovery Algorithms Unfolded as Neural Networks

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11-08-2021, 12:30 PM

Afonso S. Bandeira (ETH Zürich):
Noncommutative Matrix Concentration Inequalities

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10-25-2021, 12:30 PM

Noam Razin (Tel Aviv University):
Generalization in Deep Learning Through the Lens of Implicit Rank Minimization

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10-04-2021, 12:30 PM

Jonathan Siegel (Penn State University):
The Approximation Theory of Shallow Neural Networks

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09-27-2021, 12:30 PM

Dominik Stoeger (USC):
Small random initialization is akin to spectral learning:
Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction

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09-20-2021, 03:30 PM

Joan Bruna (NYU):
On the role of data structure in learning guarantees

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08-23-2021, 09:00 AM

Yi Ma (UC Berkeley):
White-Box Deep (Convolution) Networks from First Principles

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07-26-2021, 12:30 PM

Gabin Maxime Nguegnang (RWTH Aachen University):
Convergence of Gradient Descent in Learning Deep Liner Neural Network

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07-19-2021, 12:30 PM

Johannes Lederer (Ruhr-University Bochum):
Sparse Deep Learning

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07-12-2021, 12:30 PM

Juan-Pablo Ortega Lahuerta (Nanyang Technological University):
Reservoir Computing and the Learning of Dynamic Processes

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07-05-2021, 12:30 PM

Frederik Hoppe (RWTH Aachen University):
Debiased Lasso: Confidence intervals for the Lasso

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06-28-2021, 12:30 PM

Vicky Kouni (RWTH Aachen University):
Spark Deficient Gabor Frames for Inverse Problems

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06-14-2021, 12:30 PM

Alessandro Rudi (INRIA and École Normale Supérieure):
Finding Global Minima via Kernel Approximations

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06-07-2021, 12:30 PM

Leonardo Galli (RWTH Aachen University):
Robustness and Generalization in Training Deep Neural Networks

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05-10-2021, 12:30 PM

Hung-Hsu Chou (RWTH Aachen University):
Exploration and Exploitation: Understanding Deep Neural Network via Matrix Factorization

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05-03-2021, 12:30 PM

Lénaïc Chizat (Université Paris-Saclay):
Analysis of Gradient Descent on Wide Two-Layer ReLU Neural Networks

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04-15-2021, 04:30 PM

Panayotis Mertikopoulos (Univ. Grenoble Alpes, CNRS):
Games, dynamics, and (min-max) optimization

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04-08-2021, 04:30 PM

Robert Kunsch (RWTH Aachen University):
A new rotation equivariant neural network for 3D point clouds - proof of concept

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04-01-2021, 04:30 PM

Michael Schaub (RWTH Aachen University):
Learning from signals on graphs with unobserved edges

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03-25-2021, 04:30 PM

Yeonjong Shin (Brown University):
Towards a mathematical understanding of modern machine learning: theory, algorithms, and applications

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03-18-2021, 04:30 PM

Marc Goerigk (Uni Siegen):
Robust Optimization with Deep Neural Networks

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03-04-2021, 10:00 AM

Rayan Saab (UCSD):
A greedy algorithm for quantizing neural networks

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02-25-2021, 04:30 PM

Arthur Ulysse Jacot-Guillarmod (EPFL):
Neural Tangent Kernel: Convergence and Generalization of DNNs

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02-18-2021, 04:30 PM

Lukas Gonon (LMU):
Deep ReLU network expression rates for option prices in high-dimensional, exponential Levy models

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02-11-2021, 04:30 PM

Harald Oberhauser (Oxford):
Random Paths, Random Tensors, and some Machine Learning

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02-04-2021, 04:30 PM

Felix Krahmer (TUM):
Sketching with Kerdock's Crayons: Sparse transforms for arbitrary linear maps

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01-21-2021, 04:30 PM

Massimo Fornasier (TUM):
Identification of deep feed forward neural networks

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01-14-2021, 04:30 PM

Youness Boutaib (RWTH Aachen University):
Path classification with continuous-time recurrent neural networks

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12-17-2020, 04:30 PM

Martin Genzel (Utrecht University):
Robust Solutions to (Non-)Linear Inverse Problems: From Compressed Sensing to Deep Learning

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12-03-2020, 04:30 PM

Michael Perlmutter (UCLA):
Understanding Neural Networks via the Scattering Transform

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