255 citations · 2.4k across the 127 of their papers we have counts for
15 papers · 2 filters
Autonomous exploration for navigating in non-stationary CMPs
Pratik Gajane, Ronald Ortner, Peter Auer +1
We consider a setting in which the objective is to learn to navigate in a controlled Markov process (CMP) where transition probabilities may abruptly change. For this setting, we p…
Adaptive Exploration in Linear Contextual Bandit
Botao Hao, Tor Lattimore, Csaba Szepesvari
Contextual bandits serve as a fundamental model for many sequential decision making tasks. The most popular theoretically justified approaches are based on the optimism principle.…
Efron-Stein PAC-Bayesian Inequalities
Ilja Kuzborskij, Csaba Szepesvári
We prove semi-empirical concentration inequalities for random variables which are given as possibly nonlinear functions of independent random variables. These inequalities describe…
Exploration-Enhanced POLITEX
Yasin Abbasi-Yadkori, Nevena Lazic, Csaba Szepesvari +1
We study algorithms for average-cost reinforcement learning problems with value function approximation. Our starting point is the recently proposed POLITEX algorithm, a version of…
PAC-Bayes with Backprop
Omar Rivasplata, Vikram M Tankasali, Csaba Szepesvari
We explore the family of methods "PAC-Bayes with Backprop" (PBB) to train probabilistic neural networks by minimizing PAC-Bayes bounds. We present two training objectives, one deri…
Behaviour Suite for Reinforcement Learning
Ian Osband, Yotam Doron, Matteo Hessel +11
This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short. bsuite is a collection of carefully-designed experiments that investigate core capabiliti…