153 citations · 362 across the 29 of their papers we have counts for
13 papers · 1 filter
Learning with Good Feature Representations in Bandits and in RL with a Generative Model
Tor Lattimore, Csaba Szepesvari, Gellert Weisz
The construction by Du et al. (2019) implies that even if a learner is given linear features in that approximate the rewards in a bandit with a uniform error of ,…
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.…
Gated Linear Networks
Joel Veness, Tor Lattimore, David Budden +8
This paper presents a new family of backpropagation-free neural architectures, Gated Linear Networks (GLNs). What distinguishes GLNs from contemporary neural networks is the distri…
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…
Iterative Budgeted Exponential Search
Malte Helmert, Tor Lattimore, Levi H. S. Lelis +2
We tackle two long-standing problems related to re-expansions in heuristic search algorithms. For graph search, A* can require expansions, where is the number of sta…
Exploration by Optimisation in Partial Monitoring
Tor Lattimore, Csaba Szepesvari
We provide a simple and efficient algorithm for adversarial -action -outcome non-degenerate locally observable partial monitoring game for which the -round minimax regret…