arXiv research

Study quantile multi-armed bandits for identifying the best arm with a specified quantile level.

The paper tackles best arm identification in contaminated bandits with optimal error guarantees and sample complexity.

Proposes a new semi-parametric framework for batched bandits with covariates.

We present a provably optimal differentially private algorithm for the stochastic multi-arm bandit problem, as opposed to the private analogue of the UCB-algorithm [Mishra and Thakurta, 2015; Tossou and Dimitrakakis, 2016] which doesn't meet the recently discovered lower-bound of $Ω\left(\frac{K\log(T)}ε \right)$ [Shar…

New bandit problem for finding best group of arms with worst mean reward.

New algorithm improves best arm identification in Bayesian settings.

We consider a multi-armed bandit problem in a setting where each arm produces a noisy reward realization which depends on an observable random covariate. As opposed to the traditional static multi-armed bandit problem, this setting allows for dynamically changing rewards that better describe applications where side inf…

New algorithm finds high-reward combinatorial sets with fewest pulls.

A scheme robust to action erasures improves MAB performance.

In this paper, we study the multi-armed bandit problem in the batched setting where the employed policy must split data into a small number of batches. While the minimax regret for the two-armed stochastic bandits has been completely characterized in \cite{perchet2016batched}, the effect of the number of arms on the re…

New algorithm eliminates arms to minimize regret in complex bandit problems.

A novel algorithm reduces communication costs in federated best arm identification.

New algorithm for non-stationary bandits with slow drifts.

We consider the best-arm identification problem in multi-armed bandits, which focuses purely on exploration. A player is given a fixed budget to explore a finite set of arms, and the rewards of each arm are drawn independently from a fixed, unknown distribution. The player aims to identify the arm with the largest expe…

The dueling bandit problem is a variation of the classical multi-armed bandit in which the allowable actions are noisy comparisons between pairs of arms. This paper focuses on a new approach for finding the "best" arm according to the Borda criterion using noisy comparisons. We prove that in the absence of structural a…

In many platforms, user arrivals exhibit a self-reinforcing behavior: future user arrivals are likely to have preferences similar to users who were satisfied in the past. In other words, arrivals exhibit positive externalities. We study multiarmed bandit (MAB) problems with positive externalities. We show that the self…

New study shows non-adaptive trials can be outperformed by adaptive designs in treatment selection.

Algorithm reduces regret in multi-player bandits with unknown collision rewards.

Bayesian algorithm improves best-arm identification within fixed budget.

We study the problem of identifying the top $m$ arms in a multi-armed bandit game. Our proposed solution relies on a new algorithm based on successive rejects of the seemingly bad arms, and successive accepts of the good ones. This algorithmic contribution allows to tackle other multiple identifications settings that w…

New algorithm identifies best arm in rested bandit setting.

Paper introduces Decentralized Non-stationary Competing Bandits ( exttt{DNCB}) for dynamic matching markets.

New algorithms identify Pareto optimal sets in multi-objective bandit problems.

In this paper, we study the stochastic version of the one-sided full information bandit problem, where we have $K$ arms $[K] = \{1, 2, \ldots, K\}$, and playing arm $i$ would gain reward from an unknown distribution for arm $i$ while obtaining reward feedback for all arms $j \ge i$. One-sided full information bandit ca…

Algorithm identifies best arm in bandit game with variance consideration.

A new algorithm optimizes local objectives in federated learning with heterogeneous clients.

Study on individual regret in cooperative MAB with agents communicating over a graph.

We introduce the functional bandit problem, where the objective is to find an arm that optimises a known functional of the unknown arm-reward distributions. These problems arise in many settings such as maximum entropy methods in natural language processing, and risk-averse decision-making, but current best-arm identif…

New algorithm reduces worst-case sample complexity for learning best arm.

In the Best-$k$-Arm problem, we are given $n$ stochastic bandit arms, each associated with an unknown reward distribution. We are required to identify the $k$ arms with the largest means by taking as few samples as possible. In this paper, we make progress towards a complete characterization of the instance-wise sample…

New algorithm finds best feasible arm in grouped bandits.

We study stochastic multi-armed bandits with many players. The players do not know the number of players, cannot communicate with each other and if multiple players select a common arm they collide and none of them receive any reward. We consider the static scenario, where the number of players remains fixed, and the d…

Sequential screening and dynamic regret in multi-armed bandits with arriving arms

Study on information evolution in interactive decision making using multi-armed bandits.

Algorithm identifies the best arm in linear bandits with high probability.

We study a multiplayer stochastic multi-armed bandit problem in which players cannot communicate, and if two or more players pull the same arm, a collision occurs and the involved players receive zero reward. We consider the challenging heterogeneous setting, in which different arms may have different means for differe…

Paper tackles best mixed arm identification with cost constraints in bandit models.

This work introduces reward teaching for federated multi-armed bandits to guide clients towards global optimality.

Improved elimination strategies for adaptive bandit identification reduce sample complexity and computational burden.

New algorithm identifies best arm in semiparametric bandits with near optimal efficiency.

The paper develops a theory for identifying the best arm in non-parametric multi-armed bandits with a fixed budget.

New model for display advertising with stochastic and adversarial components.

Study optimizes best-arm identification with minimax and Bayes strategies.

A federated learning algorithm tackles unknown contexts in multi-arm bandits.

Improved adaptive algorithms for identifying the best arm in MABs with fixed budget.

BLAE solves batched linear bandits with optimal regret and practical performance.

Proper balance between exploitation and exploration is what makes good decisions, which achieve high rewards like payoff or evolutionary fitness. The Infomax principle postulates that maximization of information directs the function of diverse systems, from living systems to artificial neural networks. While specific a…

New algorithm for identifying optimal arms in stochastic bandit problems.