Improved theoretical guarantees for Top Two algorithms.
problem Theoretical support for best arm identification with bounded distributions.
method General analysis of Top Two methods, identifying desirable properties and replacing sampling step.
result Theoretical support for Top Two algorithms with bounded distributions.
Algorithm identifies best arm with biased proxy and selective ground truth audits.
problem Fixed-confidence best-arm identification with biased proxy and selective ground truth.
method Propensity-weighted estimator and adaptive auditing algorithm.
result Plug-in Neyman rule achieves near-oracle audit efficiency.
A novel algorithm reduces communication costs in federated best arm identification.
problem Identifying the best arm in a federated multi-armed bandit setup with minimal communication cost.
method Proposes a novel algorithm called FedElim that communicates only in exponential time steps.
result Demonstrates that communication is almost cost-free in FedElim, with a total cost at most 3 times the maximum under its variant.
Unified algorithm for efficient pure exploration using dual variables.
problem Efficiently achieving a specific goal through adaptive experimentation.
method Introducing dual variables to derive optimal allocation conditions, leading to Information-Directed Selection.
result Top-two Thompson sampling attains asymptotic optimality for Gaussian best-arm identification.
BAICS identifies best arm with fairness constraints on subpopulations.
problem Identify the best arm while ensuring fairness across subpopulations.
method Formulated and solved BAICS problem, analyzed complexity, designed algorithm.
result Algorithm's sample complexity matches theoretical lower bound.
New algorithm identifies best arm in rested bandit setting.
problem Best arm identification in rested bandit with decreasing losses.
method Introduced a novel best arm identification problem and analyzed an arm elimination algorithm.
result Regret vanishes as time horizon increases, with convergence rate depending on expected loss function.
Study best arm identification in restless Markov multi-armed bandits with state-dependent transitions.
problem Identify the best arm in a multi-armed bandit with time-varying states.
method Propose a sequential policy to select arms without knowing their exact TPMs.
result Upper and lower bounds on expected time to find the best arm match in a special case.
Study best arm identification with limited precision sampling in bandits.
problem Limited precision sampling in multi-armed bandit problems.
method Proposed a modified tracking-based algorithm to handle non-unique optimal allocations and presented non-asymptotic bounds.
result Asymptotically optimal tracking-based algorithm for best arm identification.
State of the art online learning procedures focus either on selecting the best alternative ("best arm identification") or on minimizing the cost (the "regret"). We merge these two objectives by providing the theoretical analysis of cost minimizing algorithms that are also delta-PAC (with a proven guaranteed bound on th…
TRIPLE efficiently optimizes prompts with a budget constraint.
problem Efficiently selecting good prompts from a pool of candidates.
method TRIPLE connects prompt optimization to best arm identification in MAB, leveraging BAI-FB tools.
result TRIPLE outperforms baselines on multiple tasks with limited budget constraints.
Box Thirding identifies the best arm efficiently under limited samples.
problem Efficiently identifying the best arm with limited sampling.
method Iterative ternary comparison of arms, discarding the weakest and exploring the best.
result Achieves comparable performance to Successive Halving with less predefined parameters.
Study robust best-arm identification in linear bandits with lower bounds and algorithms.
problem Identify a near-optimal robust arm in linear bandits with adversarial actions.
method Propose instance-dependent lower bounds and both static and adaptive bandit algorithms.
result Sample complexity matches the lower bound and algorithms effectively identify robust arms.
Improved knowledge gradient (iKG) outperforms the original KG algorithm in best arm identification problems.
problem Best arm identification (BAI) problem with limitations of the original KG algorithm.
method Follows the one-step look ahead of KG but chooses the measurement that maximizes the probability of selecting the best arm.
result Improved knowledge gradient (iKG) is asymptotically optimal and easier to extend to variant BAI problems.
Optimal top-2 method improves best arm identification with reduced error.
problem Identifying the arm with the highest mean in a set of arms.
method A novel top-2 algorithm that pulls the empirical best arm with probability β and the challenger arm otherwise.
result The proposed algorithm matches the information theoretic lower bound on sample complexity as δ approaches 0.
The paper tackles best arm identification with minimal regret in experiments.
problem Identifying the best arm with minimal regret in experiments.
method Information-theoretic techniques and Double KL-UCB algorithm.
result Achieves asymptotic optimality in identifying the best arm with minimal regret.
Study optimizes best-arm identification with minimax and Bayes strategies.
problem Efficiently identifying the best arm in fixed-budget scenarios.
method Adaptive procedure with two stages: pilot phase and minimax game.
result Single strategy is asymptotically minimax and Bayes optimal.
Optimal strategy identified for minimizing regret in fixed-budget best arm selection.
problem Minimizing expected simple regret in fixed-budget best arm selection.
method Two-Stage (TS)-Hirano-Imbens-Ridder (HIR) strategy using HIR estimator.
result TS-HIR strategy is asymptotically minimax optimal.
BCI system improves word selection efficiency using sequential best-arm identification.
problem Conventional non-adaptive BCI paradigms lead to a lengthy learning process.
method Casted as sequence of best-arm identification tasks in multi-armed bandits, using pre-trained LLMs and STTS algorithm.
result Substantial empirical improvement in word selection efficiency demonstrated.
Top-two algorithm improved for best-k-arm selection.
problem Best-k-arm identification in multi-armed bandits.
method Information-directed selection based on dual variables.
result Top-two Thompson sampling with IDS is asymptotically optimal.
New research shows fixed-budget best-arm identification cannot match static oracle performance.
problem Fixed-budget best-arm identification's performance limitations.
method Analysis of various adaptive and static algorithms for best-arm identification.
result For any algorithm, there exists at least one instance where the error decay rate is at most \((1 + \frac{\log(K)}{8})^{-1}\) times that of the static oracle.
Optimal δ-correct algorithm for heavy-tailed distributions with minimal samples.
problem Identifying the best arm from a set of unknown distributions with heavy tails.
method Developed a δ-correct algorithm that matches lower bounds under mild restrictions.
result The proposed algorithm achieves optimal sample complexity for heavy-tailed distributions.
Optimal best arm identification for multi-objective bandits with fixed error probability.
problem Identifying the best arm for each of multiple objectives with fixed confidence.
method Surrogate proportions to sample arms at each time step, eliminating max-min optimisation.
result Asymptotically optimal algorithm for multi-objective best arm identification.
A new method for identifying the best arm in multi-armed bandits with mediator feedback.
problem Sequential decision-making with unknown mediator policies.
method Best-arm identification under mediators' feedback (BAI-MF) with statistical lower bound and sequential decision-making strategy.
result The proposed algorithm matches the lower bound on sample complexity for identifying the best arm.
New algorithm identifies best arm in rare event scenarios.
problem Identifying the best arm with tiny reward probability.
method Approximated Compound Poisson process for faster algorithms.
result Improved computational efficiency with minor sample complexity increase.
New algorithm for efficiently identifying the best arm in stochastic bandits.
problem Best arm identification in stochastic multi-armed bandits with fixed confidence.
method Sequential probability ratio tests for arm selection.
result Asymptotically optimal sample complexity and guaranteed δ−PAC performance. The paper develops algorithms to minimize misallocation and identify the arm with the highest variance.
problem Minimizing misallocation and identifying the arm with the highest variance from a set of arms.
method Developed novel online algorithms UCB-VV for misallocation minimization and SHVV for fixed budget best arm identification.
result The algorithms achieve optimal performance in terms of misallocation and error probability.
A two-phase algorithm identifies the best arm in sparse linear bandits with fixed budget.
problem Best arm identification in sparse linear bandits with limited budget.
method Lasso and Optimal-Design (Lasso-OD) based linear best-arm identification.
result Lasso-OD achieves significant performance improvement for sparse and high-dimensional linear bandits.
Improved adaptive algorithms for identifying the best arm in MABs with fixed budget.
problem Identifying the best arm in stochastic Multi-Armed Bandits with a fixed sampling budget.
method Established a connection between Large Deviation Principles and adaptive algorithms, improving error probability bounds and devising new algorithms.
result The \sred algorithm outperforms existing algorithms in identifying the best arm.
Optimal best-arm identification in linear bandits reduces sampling budget.
problem Identifying the best arm with fixed confidence in stochastic linear bandits.
method A simple algorithm that tracks an optimal proportion of arm draws, updated as rarely as desired.
result The algorithm's sampling complexity matches known lower bounds, asymptotically almost surely and in expectation.
Sampling from distributions to find the one with the largest mean arises in a broad range of applications, and it can be mathematically modeled as a multi-armed bandit problem in which each distribution is associated with an arm. This paper studies the sample complexity of identifying the best arm (largest mean) in a m…
Opt-BBAI identifies the best arm with minimal batches and pulls, optimizing both sample and batch complexity.
problem Batched best arm identification (BBAI) problem, aiming to minimize policy switches and resource usage.
method Proposed Opt-BBAI algorithm, achieving near-optimal sample and batch complexity in non-asymptotic settings.
result First algorithm to achieve near-optimal sample and batch complexity in non-asymptotic settings.
Open problem: fixed-budget best arm identification complexity.
problem Understanding the complexity of identifying the best arm in a fixed budget setting.
method Analyzing existing results and conjectures in the fixed-confidence setting.
result Open questions remain about the fixed-budget setting.
Efficiently identifies best algorithms for game tasks.
problem Selecting optimal algorithms for game tasks efficiently.
method Best arm identification for multi-armed bandits with confidence intervals.
result Significantly improved performance in simple regret and error probability.
Contextual information helps identify the best arm more efficiently.
problem Best arm identification with contextual covariate information.
method Proposed a context-aware version of the 'Track-and-Stop' strategy.
result Expected number of arm draws matches lower bound asymptotically.
New strategies for identifying the best arm in bandits with decreasing variances.
problem Best arm identification in bandits with time-varying variances.
method Two policies: initial wait followed by continuous sampling, and periodic sampling with weighted average.
result Analytical guarantees and simulations show improved performance over existing methods.
Study optimizes identifying the best arm with fixed rounds and Gaussian outcomes.
problem Designing efficient experiments to identify the best arm with fixed rounds and Gaussian outcomes.
method Developed worst-case lower bounds and the GNA-EBA strategy for optimal identification.
result GNA-EBA strategy is asymptotically worst-case optimal.
Optimal multi-fidelity best-arm identification reduces cost with better accuracy.
problem Finding the best arm with highest mean reward at minimum cost.
method Gradient-based approach with asymptotically optimal cost complexity.
result Asymptotically optimal cost complexity compared to existing methods.
Optimal algorithm for identifying best arm in stochastic linear bandits with fixed confidence.
problem Identifying the best arm in stochastic linear bandits with fixed confidence.
method Extending an algorithm designed for Best Arm Identification to the ε-Thresholding Bandit Problem (TBP). result Asymptotically optimal algorithm for TBP.
New algorithms for streaming bandits with limited memory.
problem Optimizing decisions in a stream of uncertain outcomes with limited memory.
method Developed algorithms for minimizing regret and identifying the best arm under bounded memory constraints.
result Upper and lower bounds on sample complexity for best-arm identification algorithms.
New algorithm optimizes best arm identification with minimal regret.
problem Best arm identification in multi-armed bandit problems.
method Characterized Bayesian simple regret with continuity conditions of prior, proposed a simple algorithm.
result Proposed algorithm achieves rate-optimal Bayesian simple regret.
A new algorithm for identifying the best arm in linear feedback with safety constraints.
problem Identifying the best arm in linear feedback with safety constraints.
method A gap-based algorithm that ensures safety while minimizing sample complexity.
result The algorithm achieves meaningful sample complexity while ensuring safety.
VA-LUCB identifies best arm with variance constraint, achieving optimal sample complexity.
problem Identifying the best arm with variance constraint under fixed confidence.
method Parameter-free algorithm VA-LUCB, analyzing sample complexity and proving lower bounds.
result Optimal sample complexity up to a logarithmic factor in HVA, demonstrated by experiments. Study quantile multi-armed bandits for identifying the best arm with a specified quantile level.
problem Identifying the arm with the highest quantile in multi-armed bandits with private rewards.
method Proposed a (non-private) and differentially private successive elimination algorithms for best-arm identification.
result The proposed algorithms are essentially optimal for quantile bandit problems, with finite sample complexity even for distributions with infinite support-size.
New algorithm reduces sample complexity for multi-task bandits.
problem Optimizing representation and predictor pairs for multi-task bandits.
method OSRL-SC algorithm with sample complexity H(Glog(1/δG)+Xlog(1/δH)). result OSRL-SC algorithm approaches sample complexity lower bounds.
New algorithm optimizes best arm identification in linear bandits.
problem Best arm identification in linear bandits with fixed confidence.
method Algorithm minimizes geometric overlap of confidence sets, is adaptive and efficient.
result Theoretical analysis shows optimality in many cases.
Motivated by drug design, we consider the best-arm identification problem in generalized linear bandits. More specifically, we assume each arm has a vector of covariates, there is an unknown vector of parameters that is common across the arms, and a generalized linear model captures the dependence of rewards on the cov…
GNA optimally identifies the best arm with small gaps.
problem Best arm identification in fixed-budget settings.
method Generalized Neyman Allocation (GNA) for asymptotically locally minimax optimal BAI.
result GNA's worst-case bounds match the lower and upper bounds in the small-gap regime.
Optimal algorithm for identifying best-arm with minimal regret.
problem Identifying the best arm in two treatments with limited budget.
method Neyman allocation based on outcome standard deviations.
result Neyman allocation is minimax optimal for simple regret.