18 citations · 31 across the 8 of their papers we have counts for
8 papers
Scale-free adaptive planning for deterministic dynamics & discounted rewards
Peter L. Bartlett, Victor Gabillon, Jennifer Healey +1
We address the problem of planning in an environment with deterministic dynamics and stochastic rewards with discounted returns. The optimal value function is not known, nor are th…
Adaptive multi-fidelity optimization with fast learning rates
Come Fiegel, Victor Gabillon, Michal Valko
In multi-fidelity optimization, biased approximations of varying costs of the target function are available. This paper studies the problem of optimizing a locally smooth function…
Best of both worlds: Stochastic & adversarial best-arm identification
Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon +2
We study bandit best-arm identification with arbitrary and potentially adversarial rewards. A simple random uniform learner obtains the optimal rate of error in the adversarial sce…
Derivative-Free & Order-Robust Optimisation
Victor Gabillon, Rasul Tutunov, Michal Valko +1
In this paper, we formalise order-robust optimisation as an instance of online learning minimising simple regret, and propose Vroom, a zero'th order optimisation algorithm capable…
MANAS: Multi-Agent Neural Architecture Search
Vasco Lopes, Fabio Maria Carlucci, Pedro M Esperança +6
The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a g…
A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption
Peter L. Bartlett, Victor Gabillon, Michal Valko
We study the problem of optimizing a function under a \emph{budgeted number of evaluations}. We only assume that the function is \emph{locally} smooth around one of its global opti…
Hit-and-Run for Sampling and Planning in Non-Convex Spaces
Yasin Abbasi-Yadkori, Peter L. Bartlett, Victor Gabillon +1
We propose the Hit-and-Run algorithm for planning and sampling problems in non-convex spaces. For sampling, we show the first analysis of the Hit-and-Run algorithm in non-convex sp…
Approximate Modified Policy Iteration
Bruno Scherrer, Victor Gabillon, Mohammad Ghavamzadeh +1
Modified policy iteration (MPI) is a dynamic programming (DP) algorithm that contains the two celebrated policy and value iteration methods. Despite its generality, MPI has not bee…