3 citations · 7 across the 12 of their papers we have counts for
7 papers · 1 filter
LITE: Efficiently Estimating Gaussian Probability of Maximality
Nicolas Menet, Jonas Hübotter, Parnian Kassraie +1
We consider the problem of computing the probability of maximality (PoM) of a Gaussian random vector, i.e., the probability for each dimension to be maximal. This is a key challeng…
Progressive Entropic Optimal Transport Solvers
Parnian Kassraie, Aram-Alexandre Pooladian, Michal Klein +3
Optimal transport (OT) has profoundly impacted machine learning by providing theoretical and computational tools to realign datasets. In this context, given two large point clouds…
Anytime Model Selection in Linear Bandits
Parnian Kassraie, Nicolas Emmenegger, Andreas Krause +1
Model selection in the context of bandit optimization is a challenging problem, as it requires balancing exploration and exploitation not only for action selection, but also for mo…
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…
Neural Contextual Bandits without Regret
Parnian Kassraie, Andreas Krause
Contextual bandits are a rich model for sequential decision making given side information, with important applications, e.g., in recommender systems. We propose novel algorithms fo…