Quantum Subdivision Capacities and Continuous-Time Quantum Coding. - Alexander Müller-Hermes; David Reeb; Michael M. Wolf (IEEE Trans. Inf. Theory, 2015)

Tight Bound on Relative Entropy by Entropy Difference. - David Reeb; Michael M. Wolf (IEEE Trans. Inf. Theory, 2015)

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

Bounds on Information Combining With Quantum Side Information. - Christoph Hirche; David Reeb (IEEE Trans. Inf. Theory, 2018)

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

Which Minimizer Does My Neural Network Converge To? - Manuel Nonnenmacher; David Reeb; Ingo Steinwart (ECML/PKDD (3), 2021)

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

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

PAC-Bayesian lifelong learning for multi-armed bandits. - Hamish Flynn; David Reeb; Melih Kandemir; Jan Peters 001 (Data Min. Knowl. Discov., 2022)

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

Validation of composite systems by discrepancy propagation. - David Reeb; Kanil Patel; Karim Said Barsim; Martin Schiegg; Sebastian Gerwinn (UAI, 2023)

A refinement of Reznick's Positivstellensatz with applications to quantum information theory. - Alexander Müller-Hermes; Ion Nechita; David Reeb (Quantum, 2023)

PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison. - Hamish Flynn; David Reeb; Melih Kandemir; Jan Peters 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Tighter Confidence Bounds for Sequential Kernel Regression. - Hamish Flynn; David Reeb (AISTATS, 2025)