New method reduces dynamic regret for non-stationary bandits.
problem Non-stationary stochastic multi-armed bandit problem with changing optimal arm.
method Proposes a method achieving near-optimal dynamic regret without prior knowledge of changes.
result Achieves O ~ ( K N ( S + 1 ) ) \widetilde O(\sqrt{K N(S+1)}) O ( K N ( S + 1 ) ) dynamic regret. New algorithm for nonstationary multi-armed bandits with optimal performance.
problem Nonstationary multi-armed bandits with changing model parameters over time.
method Adaptive Resetting Bandit (ADR-bandit) algorithm using adaptive windowing techniques.
result ADR-bandit achieves nearly optimal performance in both abrupt and gradual changes.
We consider the classical stochastic multi-armed bandit problem with a constraint that limits the total cost incurred by switching between actions to be no larger than a given switching budget. For this problem, we prove matching upper and lower bounds on the optimal (i.e., minimax) regret, and provide efficient rate-o…
Multi-armed bandit (MAB) is a class of online learning problems where a learning agent aims to maximize its expected cumulative reward while repeatedly selecting to pull arms with unknown reward distributions. We consider a scenario where the reward distributions may change in a piecewise-stationary fashion at unknown …
The multi-armed bandit (MAB) problem is a classic example of the exploration-exploitation dilemma. It is concerned with maximising the total rewards for a gambler by sequentially pulling an arm from a multi-armed slot machine where each arm is associated with a reward distribution. In static MABs, the reward distributi…
New method tracks significant arm switches to improve bandit algorithms.
problem Adaptive procedures for bandits with unknown changes in reward distribution.
method Proposes a new notion of significant shift to count severe changes.
result Achieves faster rates than previous methods, especially when few changes are severe.
Algorithm reduces decision-making errors in multi-agent bandit problems.
problem Minimizing decision errors in multi-agent multi-armed bandit problems.
method RBO-Coop-UCB algorithm with Bayesian change point detection.
result Expected group regret is upper bounded by O ( K N M log T + K M T log T ) \mathcal{O}(KNM\log T + K\sqrt{MT\log T}) O ( K N M log T + K M T log T ) . Algorithm identifies best arm in piecewise stationary linear bandits with minimal samples.
problem Identifying the best arm in a piecewise stationary linear bandit model with unknown contexts and changepoints.
method Design of PS ε \varepsilon ε BAI + ^+ + algorithm, consisting of PS ε \varepsilon ε BAI and N ε \varepsilon ε BAI subroutines. result PS ε \varepsilon ε BAI + ^+ + achieves optimal sample complexity up to a logarithmic factor. A new framework for risk-aware multi-armed bandits tackles volatile environments.
problem Volatility in healthcare and finance makes naive reward maximization unreliable.
method Risk-aware strategies with adaptive risk measures and change-point detection.
result Finite-time theoretical guarantees and asymptotic regret bound of order i l d e O ( K T T ) ilde O(\sqrt{K_T T}) i l d e O ( K T T ) . New algorithms detect changes in non-stationary MABs for better performance.
problem Non-stationary MAB environments where arm reward distributions change over time.
method Modular Detection Augmented Bandit (DAB) procedures with improved performance lower bounds.
result Modular DAB procedures achieve order-optimal regret bounds for various change detectors and bandit algorithms.
Algorithm optimizes bandit decisions with changing action sets using Gaussian processes.
problem Optimizing decisions in a bandit problem with time-varying action sets.
method Proposes an algorithm called O'CLOK-UCB using Gaussian processes to handle changing action sets and contexts.
result Achieves regret bound of i l d e O ( λ ∗ ( K ) K T γ K T ( ∪ t ≤ T X t ) ) ilde{O}(\sqrt{λ^*(K)KTγ_{KT}(\cup_{t\leq T}\mathcal{X}_t)} ) i l d e O ( λ ∗ ( K ) K T γ K T ( ∪ t ≤ T X t ) ) with high probability. New algorithm reduces dynamic regret in non-stationary dueling bandits using a weighted Borda score.
problem Designing algorithms with low dynamic regret in non-stationary dueling bandits.
method Introducing a novel weighted Borda score framework to analyze the Condorcet problem and establish improved bounds.
result First optimal and adaptive dynamic regret upper bound of i l d e O ( i l d e L 1 / 3 K 1 / 3 T 2 / 3 ) ilde{O}( ilde{L}^{1/3} K^{1/3} T^{2/3} ) i l d e O ( i l d e L 1/3 K 1/3 T 2/3 ) . This paper applies Thompson Sampling to asymmetric α \alpha α -stable bandits for financial and wireless data.
problem Optimizing exploration-exploitation in multi-armed bandits with asymmetric α \alpha α -stable distributions. method Thompson Sampling applied to unknown asymmetric α \alpha α -stable reward distributions. result Demonstrates effectiveness of Thompson Sampling for asymmetric α \alpha α -stable bandits. The multi-armed bandit problem has been extensively studied under the stationary assumption. However in reality, this assumption often does not hold because the distributions of rewards themselves may change over time. In this paper, we propose a change-detection (CD) based framework for multi-armed bandit problems und…
Proposes a new TS algorithm for non-stationary bandits using KS tests.
problem Non-stationary multi-armed bandit problems.
method Active detection of change points using KS tests and adaptive Thompson Sampling.
result Sub-linear regret demonstrated for the two-armed bandit case.
Two novel methods identify influential features in CMABs for better reward distribution.
problem Suboptimal features degrade rewards, interpretability, and efficiency in CMABs.
method Heterogeneous Incremental Effect (HIE) and Heterogeneous Distribution Divergence (HDD) methods.
result Consistent ability to identify influential HTE features, enhancing CMAB performance.
The expected improvement (EI) algorithm is a popular strategy for information collection in optimization under uncertainty. The algorithm is widely known to be too greedy, but nevertheless enjoys wide use due to its simplicity and ability to handle uncertainty and noise in a coherent decision theoretic framework. To pr…
A new algorithm for personalized recommendations adapts to changing user interests.
problem Adapting to time-varying user interests in recommendation systems.
method Contextual bandit approach with models for disjoint and hybrid payoffs.
result Sublinear regret in time length T for abrupt reward changes.
We investigate the piecewise-stationary combinatorial semi-bandit problem. Compared to the original combinatorial semi-bandit problem, our setting assumes the reward distributions of base arms may change in a piecewise-stationary manner at unknown time steps. We propose an algorithm, \texttt{GLR-CUCB}, which incorporat…
Optimized bandit algorithms have heavy-tailed regret distributions that can grow faster than expected.
problem Heavy-tailed regret distributions in optimized bandit algorithms.
method Change-of-measure ideas and UCB algorithm modifications.
result Regret distributions of optimized UCB algorithms have a heavy Cauchy tail, and can grow faster than poly-logarithmically.
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…
In a multi-armed bandit (MAB) problem a gambler needs to choose at each round of play one of K arms, each characterized by an unknown reward distribution. Reward realizations are only observed when an arm is selected, and the gambler's objective is to maximize his cumulative expected earnings over some given horizon of…
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.
Algorithm improves multi-armed bandit performance by transferring reward samples.
problem Sequential multi-armed bandit problem with changing reward distributions.
method UCB algorithm with reward sample transfer.
result Significant improvement in cumulative regret over standard UCB.
In X \mathcal{X} X -armed bandit problem an agent sequentially interacts with environment which yields a reward based on the vector input the agent provides. The agent's goal is to maximise the sum of these rewards across some number of time steps. The problem and its variations have been a subject of numerous studies, su…
Study incentivizes exploration in non-stationary MAB with compensation.
problem Incentivized exploration for non-stationary stochastic bandits with biased feedback.
method Proposed algorithms for abruptly-changing and continuously-changing non-stationary environments.
result Achieves sublinear regret and compensation over time.
The study analyzes batched methods for early stopping in stochastic multi-armed bandits.
problem Early stopping in stochastic multi-armed bandits with fixed confidence.
method Instance-dependent lower bounds and a general batched algorithm with upper bounds.
result Upper and lower bounds on the number of batches and sample complexity.
Mortal bandits have proven to be extremely useful for providing news article recommendations, running automated online advertising campaigns, and for other applications where the set of available options changes over time. Previous work on this problem showed how to regulate exploration of new arms when they have recen…
Study adapts combinatorial semi-bandit for piecewise stationary, causally related rewards.
problem Nonstationary environment with changing base arms' distributions and causal relationships.
method Upper Confidence Bound (UCB) algorithm with change-point detector and group restart strategy.
result Regret upper bound reflecting effects of structural and distribution changes.
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.
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.
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.
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.
Algorithm for bandits with switching costs achieves optimal regret bounds.
problem Optimal regret bounds for stochastic and adversarial bandits with switching costs.
method Adaptation of Tsallis-INF algorithm with no prior knowledge of regime or time horizon.
result Achieves minimax optimal regret bounds in various settings.
LNUCB-TA improves MAB performance by dynamically adjusting exploration rates and recognizing spatiotemporal patterns.
problem Suboptimal performance in environments with rapidly changing reward structures and static exploration rates.
method Hybrid model combining linear and nonlinear estimation, with adaptive k-NN for temporal attention.
result Significantly outperforms state-of-the-art algorithms in cumulative and mean reward, convergence, and robustness.
Optimal algorithm found for collaborative learning in bandits with optimal regret bounds.
problem Minimizing regret in collaborative multi-agent bandit problems.
method Proposed an algorithm with optimal regret bounds for collaborative multi-agent multi-armed bandit model.
result First algorithm with order optimal regret bounds for collaborative bandit model.
Optimism stabilizes Thompson Sampling for adaptive inference in multi-armed bandits.
problem Subtle inferential properties of Thompson Sampling under adaptive data collection.
method Introduced optimism as a key mechanism to restore stability and validity of inference.
result Suitably implemented optimism stabilizes Thompson Sampling and enables asymptotically valid Wald inference.
New strategy optimally identifies best arm in unknown variance Gaussian bandits.
problem Identifying the best arm in two-armed Gaussian bandits with unknown variances.
method Proposes a Neyman Allocation (NA)-Augmented Inverse Probability weighting (AIPW) strategy to estimate variances and draw arms adaptively.
result Demonstrates asymptotic optimality of the proposed strategy 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.
We propose a novel technique for analyzing adaptive sampling called the {\em Simulator}. Our approach differs from the existing methods by considering not how much information could be gathered by any fixed sampling strategy, but how difficult it is to distinguish a good sampling strategy from a bad one given the limit…
Greedy algorithms outperform UCB in many-armed bandit problems.
problem Optimizing multi-armed bandit problems with many arms.
method Subsampled UCB and greedy algorithms comparison.
result Greedy algorithms achieve optimal regret in many-armed bandit problems.
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.
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.
We study the multi-player stochastic multiarmed bandit (MAB) problem in an abruptly changing environment. We consider a collision model in which a player receives reward at an arm if it is the only player to select the arm. We design two novel algorithms, namely, Round-Robin Sliding-Window Upper Confidence Bound\# (RR-…
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.
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.
New algorithm reduces worst-case sample complexity for learning best arm.
problem Identifying a best arm with confidence in multi-armed bandit settings.
method Proposed a new ( ε , δ ) (ε,δ) ( ε , δ ) -PAC learning algorithm for multi-armed bandits. result Algorithm achieves optimal sample complexity for ( ε , δ ) (ε,δ) ( ε , δ ) -learning. Adaptive smooth non-stationary bandits achieve optimal regret rates without knowing parameters.
problem Smooth non-stationary bandits with Hölder class rewards.
method Established optimal dynamic regret rate and adaptive algorithm.
result Optimal dynamic regret can be attained adaptively without knowing Hölder exponent and coefficient.