New algorithms minimize simple and cumulative regret in contextual bandits.
problem Minimizing simple and cumulative regret in contextual bandit settings.
method Proposed new algorithms using conformal arm sets (CASs).
result Near-optimal minimax guarantees for simple regret and state-of-the-art guarantees for cumulative regret.
Enhances conformal prediction for better uncertainty estimates in armed conflict fatalities.
problem Lack of individual-level uncertainty estimates in existing forecasting models.
method Introduces bin-conditional conformal prediction (BCCP) to improve coverage rates across subsets of the outcome variable.
result Demonstrates improved local coverage and well-calibrated uncertainty estimates across various ranges of fatalities.
ACP-UCB1 ranks arms based on upper-tail performance, improving stochastic bandit algorithms.
problem Stochastic bandit algorithms often favor arms with strong upper-tail performance, which is not well-addressed by classical mean-reward criteria.
method ACP-UCB1 combines an adaptive conformal estimate of the upper endpoint with a UCB-type optimism bonus.
result ACP-UCB1 achieves logarithmic upper-quantile regret with per-arm contribution \(O(
icefrac{\log n}{Δ_j^{\mathrm{ACP}}})\).
BCCP uses bandit feedback to provide reliable predictions with limited labeled data.
problem Limited labeled data and bandit feedback challenge online set-valued classification.
method BCCP uses stochastic gradient descent to train model and make set-valued inferences with unbiased estimation of true label.
result BCCP offers coverage guarantees on a class-specific granularity.
This paper applies Thompson Sampling to asymmetric α-stable bandits for financial and wireless data.
problem Optimizing exploration-exploitation in multi-armed bandits with asymmetric α-stable distributions. method Thompson Sampling applied to unknown asymmetric α-stable reward distributions. result Demonstrates effectiveness of Thompson Sampling for asymmetric α-stable bandits. Proposes online conformal prediction method with adversarial semi-bandit feedback.
problem Online uncertainty quantification with adversarial semi-bandit feedback.
method Formulates online conformal prediction as an adversarial bandit problem and uses regret minimization.
result Achieves long-run coverage guarantee with adversarial semi-bandit feedback.
Bayesian optimization enhanced with conformal prediction for better outcome reliability.
problem Uncertainty and model misspecification in Bayesian optimization.
method Conformal prediction to provide coverage guarantees and Bayesian optimization to select queries.
result Significant improvement in query coverage without sacrificing sample-efficiency.
Improved best-arm identification in correlated multi-armed bandits.
problem Best-arm identification in multi-armed bandits with correlated rewards.
method Proposed C-LUCB algorithm that exploits upper bounds on conditional rewards.
result Significant reduction in sample complexity for best-arm identification.
Optimal best-arm identification with known number of optimal arms.
problem Identifying the best arm in a multi-armed bandit with multiple optimal arms under fixed confidence.
method Deriving a new information-theoretic lower bound and proposing a modified stopping rule.
result Achieving asymptotic instance-optimality with a new lower bound and new stopping rule.
New algorithm identifies best arm in non-stationary linear bandits with improved complexity.
problem Best arm identification in non-stationary linear bandits with adversarial parameters.
method Proposed Adjacent-optimal design and extsfAdjacent−BAI algorithm. result Error probability matches arm-set-dependent lower bound up to constants.
Sequential screening and dynamic regret in multi-armed bandits with arriving arms
problem Sequential experimentation with expanding arm set
method UCB-AA with preliminary screening
result Regret bounds depend on arrival process
A new multi-armed bandit framework with credal sets for uncertain outcomes.
problem Optimizing decisions under uncertainty with unknown outcomes.
method Introduces a novel multi-armed bandit framework with credal sets and defines regret as lower prevision.
result Upper bounds on regret for certain hypothesis classes and lower bounds for special cases.
New algorithms identify best arm with less pulls, adapting to arm covariances.
problem Best arm identification under dependent and correlated arm distributions.
method Adaptive algorithms estimating arm covariances to minimize pulls.
result Substantial improvement in best arm identification over standard setting.
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…
Paper tackles outlier detection in multi-armed bandits, achieving high accuracy with reduced exploration costs.
problem Detecting outlier arms in multi-armed bandit settings.
method Proposes GOLD algorithm based on upper confidence bounds to identify generic outlier arms.
result Achieves 98% accuracy with 83% reduction in exploration cost compared to state-of-the-art techniques.
We consider a stochastic bandit problem with infinitely many arms. In this setting, the learner has no chance of trying all the arms even once and has to dedicate its limited number of samples only to a certain number of arms. All previous algorithms for this setting were designed for minimizing the cumulative regret o…
Study optimal arms in combinatorial bandits with semi-bandit feedback and finite budget.
problem Finding optimal arms in combinatorial bandits with semi-bandit feedback and finite budget constraints.
method Proposes a generic algorithm covering various arm elimination strategies and derives lower bounds.
result Demonstrates sufficient and necessary budget requirements for finding the best arm.
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.
New algorithm eliminates arms to minimize regret in complex bandit problems.
problem Minimizing regret in combinatorial bandit problems with explicit exploration.
method Introduces a novel arm elimination scheme that partitions arms into three categories and incorporates explicit exploration.
result Achieves near-optimal regret in combinatorial multi-armed and linear contextual bandit problems.
New algorithm identifies best target arm with known additive relationship between source and target MAB instances.
problem Identifying the best arm in a target MAB instance when only source arms can be pulled and there's a known additive relationship between the two.
method Proposes an LUCB-style algorithm to identify an ε-optimal target arm with high probability.
result Theoretical analysis highlights aspects of the transfer learning problem and recovers the LUCB algorithm for single domain BAI as a special case.
New algorithm for identifying optimal arms in stochastic bandit problems.
problem Optimal arm identification in stochastic bandit problems with many arms.
method Characterized optimal learning rates and provided algorithms with matching bounds.
result Lower bounds and matching upper bounds for cumulative regret and best-arm identification.
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.
Proposes DEXP3.M for unknown delay in multi-arm bandit with multiple play.
problem Unknown delays in adversarial multi-armed bandit with multiple play.
method DEXP3.M algorithm addressing the challenge of associating feedback losses to arms.
result Regret bound is only slightly worse than single play setting.
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.
Motivated by models of human decision making proposed to explain commonly observed deviations from conventional expected value preferences, we formulate two stochastic multi-armed bandit problems with distorted probabilities on the reward distributions: the classic K-armed bandit and the linearly parameterized bandit…
We study a strategic version of the multi-armed bandit problem, where each arm is an individual strategic agent and we, the principal, pull one arm each round. When pulled, the arm receives some private reward va and can choose an amount xa to pass on to the principal (keeping va−xa for itself). All non-pulle…
This work optimizes identifying good arms in nonparametric multi-armed bandits.
problem Efficiently identifying arms with high means in nonparametric settings.
method Combining reward-maximizing sampling with a nonparametric sequential test for anytime-valid labeling.
result Achieves minimax optimal stopping times for identifying arms above a threshold.
A new algorithm balances global reward and group constraints in federated multi-armed bandits.
problem Maximizing global reward while protecting client privacy in federated learning.
method Combinatorial contextual bandit with group constraints, using a two-output Gaussian process.
result TCGP-UCB incurs low regret, balancing super arm reward and group reward constraints.
We consider the problem of near-optimal arm identification in the fixed confidence setting of the infinitely armed bandit problem when nothing is known about the arm reservoir distribution. We (1) introduce a PAC-like framework within which to derive and cast results; (2) derive a sample complexity lower bound for near…
Given a vector of probability distributions, or arms, each of which can be sampled independently, we consider the problem of identifying the partition to which this vector belongs from a finitely partitioned universe of such vector of distributions. We study this as a pure exploration problem in multi armed bandit sett…
In this paper, we introduce Ballooning Multi-Armed Bandits (BL-MAB), a novel extension of the classical stochastic MAB model. In the BL-MAB model, the set of available arms grows (or balloons) over time. In contrast to the classical MAB setting where the regret is computed with respect to the best arm overall, the regr…
We consider the problem of \textit{best arm identification} with a \textit{fixed budget T}, in the K-armed stochastic bandit setting, with arms distribution defined on [0,1]. We prove that any bandit strategy, for at least one bandit problem characterized by a complexity H, will misidentify the best arm with pr…
Algorithm identifies best arm in combinatorial bandits with semi-bandit feedback.
problem Identifying the best arm in combinatorial bandits with semi-bandit feedback.
method Interpreted as a sequential zero-sum game, developed a CombGame meta-algorithm with finite time guarantees.
result First computationally efficient algorithm that is asymptotically optimal and has competitive empirical performance.
New algorithm improves best arm identification in Bayesian settings.
problem Finding the arm with the highest mean in unknown distributions.
method Developed a variant of successive elimination algorithm.
result Achieved optimal performance in Bayesian setting with logarithmic gap.
Motivated by the task of hyperparameter optimization, we introduce the non-stochastic best-arm identification problem. Within the multi-armed bandit literature, the cumulative regret objective enjoys algorithms and analyses for both the non-stochastic and stochastic settings while to the best of our knowledge, the best…
We consider the stochastic multi-armed bandit (MAB) problem in a setting where a player can pay to pre-observe arm rewards before playing an arm in each round. Apart from the usual trade-off between exploring new arms to find the best one and exploiting the arm believed to offer the highest reward, we encounter an addi…
New method for identifying best arm in batched multi-armed bandit problems.
problem Identifying the best arm in multi-armed bandit problems where arms are sampled in batches.
method General linear programming framework for best arm identification in batched multi-armed bandit problems.
result Demonstrated good performance in numerical studies compared to UCB-type or Thompson sampling methods.
Bayesian algorithm improves best-arm identification within fixed budget.
problem Maximizing probability of identifying optimal arm within fixed budget.
method Proposes Bayesian elimination algorithm and derives upper bound on misidentification probability.
result Upper bound on misidentification probability reflects prior quality and matches lower bound.
APGAI identifies good arms anytime with fixed budget.
problem Identifying a good arm with a fixed sampling budget.
method An anytime algorithm for good arm identification in stochastic bandits.
result APGAI achieves efficient detection of good arms with upper bounds on probability of error and sampling complexity.
Paper tackles good arm identification in stochastic bandits.
problem Identifying good arms with minimal samples.
method Proposes DGAI, a differentiable algorithm to improve sample complexity.
result DGAI outperforms baseline algorithms in synthetic and real-world datasets.
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 new algorithm for selecting top-k arms in extreme contextual bandits with improved efficiency.
problem Selecting top-k arms from a large set with contextual information and limited rewards.
method Proposes an algorithm for both non-extreme and extreme settings, using Inverse Gap Weighting and arm hierarchy models.
result Achieves improved regret guarantees for extreme settings with significant computational and statistical efficiency.
We propose a generalization of the best arm identification problem in stochastic multi-armed bandits (MAB) to the setting where every pull of an arm is associated with delayed feedback. The delay in feedback increases the effective sample complexity of standard algorithms, but can be offset if we have access to partial…
We propose a novel formulation of group fairness with biased feedback in the contextual multi-armed bandit (CMAB) setting. In the CMAB setting, a sequential decision maker must, at each time step, choose an arm to pull from a finite set of arms after observing some context for each of the potential arm pulls. In our mo…
A new algorithm reduces suboptimal arm selection in correlated bandits.
problem Structured bandits with correlated rewards.
method Confidence-based phased algorithm.
result Regret is uniformly bounded in certain structures.
Proposes a max-utility arm selection strategy for reducing cumulative regret in sequential query recommendations.
problem Reduces cumulative regret in sequential query recommendations for closed loop interactive learning settings.
method Proposes a max-utility arm selection strategy based on the maximum utility of arms.
result Improves cumulative regret substantially compared to baseline algorithms and random selection.
We consider stochastic multi-armed bandits where the expected reward is a unimodal function over partially ordered arms. This important class of problems has been recently investigated in (Cope 2009, Yu 2011). The set of arms is either discrete, in which case arms correspond to the vertices of a finite graph whose stru…
Study best arm identification with safety constraints in bandit problems.
problem Real-world decision-making with safety constraints.
method Analyzed linear and monotonic reward and safety constraints, proposed algorithms.
result Guaranteed safe learning in both linear and general reward/safety constraint settings.