3 citations · 9 across the 31 of their papers we have counts for
4 papers · 1 filter
Ensemble sampling for linear bandits: small ensembles suffice
David Janz, Alexander E. Litvak, Csaba Szepesvári
We provide the first useful and rigorous analysis of ensemble sampling for the stochastic linear bandit setting. In particular, we show that, under standard assumptions, for a -…
Exploration via linearly perturbed loss minimisation
David Janz, Shuai Liu, Alex Ayoub +1
We introduce exploration via linear loss perturbations (EVILL), a randomised exploration method for structured stochastic bandit problems that works by solving for the minimiser of…
Stochastic Gradient Descent for Gaussian Processes Done Right
Jihao Andreas Lin, Shreyas Padhy, Javier Antorán +5
As is well known, both sampling from the posterior and computing the mean of the posterior in Gaussian process regression reduces to solving a large linear system of equations. We…
Online RL in Linearly -Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore
Gellért Weisz, András György, Csaba Szepesvári
We consider online reinforcement learning (RL) in episodic Markov decision processes (MDPs) under the linear -realizability assumption, where it is assumed that the action-val…