Search Results for author: David Reeb

Found 9 papers, 4 papers with code

PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison

no code implementations29 Nov 2022 Hamish Flynn, David Reeb, Melih Kandemir, Jan Peters

In many of these applications, principled algorithms with strong performance guarantees would be very much appreciated.

Decision Making

Validation of Composite Systems by Discrepancy Propagation

no code implementations21 Oct 2022 David Reeb, Kanil Patel, Karim Barsim, Martin Schiegg, Sebastian Gerwinn

Assessing the validity of a real-world system with respect to given quality criteria is a common yet costly task in industrial applications due to the vast number of required real-world tests.

Experimental Design

Utilizing Expert Features for Contrastive Learning of Time-Series Representations

1 code implementation23 Jun 2022 Manuel Nonnenmacher, Lukas Oldenburg, Ingo Steinwart, David Reeb

We therefore devise ExpCLR, a novel contrastive learning approach built on an objective that utilizes expert features to encourage both properties for the learned representation.

Contrastive Learning Representation Learning +1

SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning

1 code implementation NeurIPS 2021 Manuel Nonnenmacher, Thomas Pfeil, Ingo Steinwart, David Reeb

We validate SOSP-H by comparing it to our second method SOSP-I that uses a well-established Hessian approximation, and to numerous state-of-the-art methods.

Which Minimizer Does My Neural Network Converge To?

no code implementations4 Nov 2020 Manuel Nonnenmacher, David Reeb, Ingo Steinwart

The loss surface of an overparameterized neural network (NN) possesses many global minima of zero training error.

Wide Neural Networks are Interpolating Kernel Methods: Impact of Initialization on Generalization

no code implementations25 Sep 2019 Manuel Nonnenmacher, David Reeb, Ingo Steinwart

The recently developed link between strongly overparametrized neural networks (NNs) and kernel methods has opened a new way to understand puzzling features of NNs, such as their convergence and generalization behaviors.

Generalization Bounds

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