18 citations · 34 across the 10 of their papers we have counts for
10 papers
Sharp Optimal Algorithm for Derivative-Free Stochastic Convex Optimization in One Dimension
Alexandra Carpentier, Chloé Rouyer, Alexandre Tsybakov +1
Stochastic convex optimization is a classical problem with well-understood guarantees under first-order feedback. In contrast, for zero-order optimization with noisy function evalu…
Gradient-free stochastic optimization of derivatives under strong convexity
Arya Akhavan, Sirine Louati, Alexandre B. Tsybakov
We consider the problem of minimizing the -th order partial derivative of an unknown function along a fixed coordinate direction , based on noisy queri…
Gradient-free stochastic optimization for additive models
Arya Akhavan, Alexandre B. Tsybakov
We address the problem of zero-order optimization from noisy observations for an objective function satisfying the Polyak-Łojasiewicz or the strong convexity condition. Additionall…
A conversion theorem and minimax optimality for continuum contextual bandits
Arya Akhavan, Karim Lounici, Massimiliano Pontil +1
We study the contextual continuum bandits problem, where the learner sequentially receives a side information vector and has to choose an action in a convex set, minimizing a funct…
Gradient-free optimization of highly smooth functions: improved analysis and a new algorithm
Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil +1
This work studies minimization problems with zero-order noisy oracle information under the assumption that the objective function is highly smooth and possibly satisfies additional…
Estimating the minimizer and the minimum value of a regression function under passive design
Arya Akhavan, Davit Gogolashvili, Alexandre B. Tsybakov
We propose a new method for estimating the minimizer and the minimum value of a smooth and strongly convex regression function from the observations co…
Group Meritocratic Fairness in Linear Contextual Bandits
Riccardo Grazzi, Arya Akhavan, John Isak Texas Falk +2
We study the linear contextual bandit problem where an agent has to select one candidate from a pool and each candidate belongs to a sensitive group. In this setting, candidates' r…
A gradient estimator via L1-randomization for online zero-order optimization with two point feedback
Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil +1
This work studies online zero-order optimization of convex and Lipschitz functions. We present a novel gradient estimator based on two function evaluations and randomization on the…
Distributed Zero-Order Optimization under Adversarial Noise
Arya Akhavan, Massimiliano Pontil, Alexandre B. Tsybakov
We study the problem of distributed zero-order optimization for a class of strongly convex functions. They are formed by the average of local objectives, associated to different no…
Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits
Arya Akhavan, Massimiliano Pontil, Alexandre B. Tsybakov
We study the problem of zero-order optimization of a strongly convex function. The goal is to find the minimizer of the function by a sequential exploration of its values, under me…