217 citations · 568 across the 7 of their papers we have counts for
8 papers
Training of Quantum Circuits on a Hybrid Quantum Computer
D. Zhu, N. M. Linke, M. Benedetti +10
Generative modeling is a flavor of machine learning with applications ranging from computer vision to chemical design. It is expected to be one of the techniques most suited to tak…
Distributed Clustering of Linear Bandits in Peer to Peer Networks
Nathan Korda, Balazs Szorenyi, Shuai Li
We provide two distributed confidence ball algorithms for solving linear bandit problems in peer to peer networks with limited communication capabilities. For the first, we assume…
On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence
Nathaniel Korda, L. A. Prashanth
We provide non-asymptotic bounds for the well-known temporal difference learning algorithm TD(0) with linear function approximators. These include high-probability bounds as well a…
Finite-Time Analysis of Kernelised Contextual Bandits
Michal Valko, Nathaniel Korda, Remi Munos +2
We tackle the problem of online reward maximisation over a large finite set of actions described by their contexts. We focus on the case when the number of actions is too big to sa…
Thompson Sampling for 1-Dimensional Exponential Family Bandits
Nathaniel Korda, Emilie Kaufmann, Remi Munos
Thompson Sampling has been demonstrated in many complex bandit models, however the theoretical guarantees available for the parametric multi-armed bandit are still limited to the B…
Fast gradient descent for drifting least squares regression, with application to bandits
Nathaniel Korda, Prashanth L. A., Rémi Munos
Online learning algorithms require to often recompute least squares regression estimates of parameters. We study improving the computational complexity of such algorithms by using…
Concentration bounds for temporal difference learning with linear function approximation: The case of batch data and uniform sampling
L. A. Prashanth, Nathaniel Korda, Rémi Munos
We propose a stochastic approximation (SA) based method with randomization of samples for policy evaluation using the least squares temporal difference (LSTD) algorithm. Our propos…
Thompson Sampling: An Asymptotically Optimal Finite Time Analysis
Emilie Kaufmann, Nathaniel Korda, Rémi Munos
The question of the optimality of Thompson Sampling for solving the stochastic multi-armed bandit problem had been open since 1933. In this paper we answer it positively for the ca…