Reeb, David

8 publications

AISTATS 2025 Tighter Confidence Bounds for Sequential Kernel Regression Hamish Flynn, David Reeb

NeurIPS 2023 Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures Hamish Flynn, David Reeb, Melih Kandemir, Jan R Peters

UAI 2023 Validation of Composite Systems by Discrepancy Propagation David Reeb, Kanil Patel, Karim Said Barsim, Martin Schiegg, Sebastian Gerwinn

ICLR 2022 SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning Manuel Nonnenmacher, Thomas Pfeil, Ingo Steinwart, David Reeb

ICML 2022 Utilizing Expert Features for Contrastive Learning of Time-Series Representations Manuel T Nonnenmacher, Lukas Oldenburg, Ingo Steinwart, David Reeb

ECML-PKDD 2021 Which Minimizer Does My Neural Network Converge to? Manuel Nonnenmacher, David Reeb, Ingo Steinwart

NeurIPS 2020 Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties Jakob Lindinger, David Reeb, Christoph Lippert, Barbara Rakitsch

NeurIPS 2018 Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds David Reeb, Andreas Doerr, Sebastian Gerwinn, Barbara Rakitsch