46 citations · 165 across the 13 of their papers we have counts for
13 papers
Spectral bandits
Tomáš Kocák, Rémi Munos, Branislav Kveton +2
Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this work, we study a bandit problem where the payoffs of arms are smooth on a graph.…
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…
Variational Regret Bounds for Reinforcement Learning
Pratik Gajane, Ronald Ortner, Peter Auer
We consider undiscounted reinforcement learning in Markov decision processes (MDPs) where both the reward functions and the state-transition probabilities may vary (gradually or ab…
A Sliding-Window Algorithm for Markov Decision Processes with Arbitrarily Changing Rewards and Transitions
Pratik Gajane, Ronald Ortner, Peter Auer
We consider reinforcement learning in changing Markov Decision Processes where both the state-transition probabilities and the reward functions may vary over time. For this problem…
Online Learning with Randomized Feedback Graphs for Optimal PUE Attacks in Cognitive Radio Networks
Monireh Dabaghchian, Amir Alipour-Fanid, Kai Zeng +2
In a cognitive radio network, a secondary user learns the spectrum environment and dynamically accesses the channel where the primary user is inactive. At the same time, a primary…
An algorithm with nearly optimal pseudo-regret for both stochastic and adversarial bandits
Peter Auer, Chao-Kai Chiang
We present an algorithm that achieves almost optimal pseudo-regret bounds against adversarial and stochastic bandits. Against adversarial bandits the pseudo-regret is $O(K\sqrt{n \…