110 citations · 168 across the 18 of their papers we have counts for
4 papers · 2 filters
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice
Jonas Rothfuss, Martin Josifoski, Vincent Fortuin +1
Meta-Learning aims to speed up the learning process on new tasks by acquiring useful inductive biases from datasets of related learning tasks. While, in practice, the number of rel…
Instance-Dependent Generalization Bounds via Optimal Transport
Songyan Hou, Parnian Kassraie, Anastasis Kratsios +2
Existing generalization bounds fail to explain crucial factors that drive the generalization of modern neural networks. Since such bounds often hold uniformly over all parameters,…
Lifelong Bandit Optimization: No Prior and No Regret
Felix Schur, Parnian Kassraie, Jonas Rothfuss +1
Machine learning algorithms are often repeatedly applied to problems with similar structure over and over again. We focus on solving a sequence of bandit optimization tasks and dev…
Meta-Learning Hypothesis Spaces for Sequential Decision-making
Parnian Kassraie, Jonas Rothfuss, Andreas Krause
Obtaining reliable, adaptive confidence sets for prediction functions (hypotheses) is a central challenge in sequential decision-making tasks, such as bandits and model-based reinf…