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Found 14 papers, 2 papers with code
On the Existence of a Complexity in Fixed Budget Bandit Identification
We show that there is no such complexity for several fixed budget identification tasks including Bernoulli best arm identification with two arms: there is no single algorithm that attains everywhere the best possible rate.
Dealing with Unknown Variances in Best-Arm Identification
The problem of identifying the best arm among a collection of items having Gaussian rewards distribution is well understood when the variances are known.
Top Two Algorithms Revisited
Top Two algorithms arose as an adaptation of Thompson sampling to best arm identification in multi-armed bandit models (Russo, 2016), for parametric families of arms.
Choosing Answers in $\varepsilon$-Best-Answer Identification for Linear Bandits
In pure-exploration problems, information is gathered sequentially to answer a question on the stochastic environment.
On Elimination Strategies for Bandit Fixed-Confidence Identification
Elimination algorithms for bandit identification, which prune the plausible correct answers sequentially until only one remains, are computationally convenient since they reduce the problem size over time.
Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification
In this work, we first derive a tractable lower bound on the sample complexity of any $\delta$-correct algorithm for the general Top-m identification problem.
Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits
In the fixed budget thresholding bandit problem, an algorithm sequentially allocates a budgeted number of samples to different distributions.
Gamification of Pure Exploration for Linear Bandits
We investigate an active pure-exploration setting, that includes best-arm identification, in the context of linear stochastic bandits.
Structure Adaptive Algorithms for Stochastic Bandits
We study reward maximisation in a wide class of structured stochastic multi-armed bandit problems, where the mean rewards of arms satisfy some given structural constraints, e. g. linear, unimodal, sparse, etc.
Non-Asymptotic Pure Exploration by Solving Games
Pure exploration (aka active testing) is the fundamental task of sequentially gathering information to answer a query about a stochastic environment.
Pure Exploration with Multiple Correct Answers
We present a new algorithm which extends Track-and-Stop to the multiple-answer case and has asymptotic sample complexity matching the lower bound.
Bridging the gap between regret minimization and best arm identification, with application to A/B tests
State of the art online learning procedures focus either on selecting the best alternative ("best arm identification") or on minimizing the cost (the "regret").
Bandits with Side Observations: Bounded vs. Logarithmic Regret
We consider the classical stochastic multi-armed bandit but where, from time to time and roughly with frequency $\epsilon$, an extra observation is gathered by the agent for free.
Combinatorial semi-bandit with known covariance
We introduce a way to quantify the dependency structure of the problem and design an algorithm that adapts to it.
