Unified formulation bridges adversarial and nonstationary bandits.
problem Handling time-varying reward distributions in multi-armed bandit problems.
method Unified oracle that switches between adversarial and nonstationary bandit oracles based on window size.
result Optimal regret achieved with matching lower bound.
Study on Pareto optimality in multi-objective bandit problems.
problem Pareto optimality in multi-objective multi-armed bandit problems.
method Formulated adversarial multi-objective multi-armed bandit, defined Pareto regrets, presented algorithms, established upper and lower bounds.
result New algorithms are optimal in adversarial settings and nearly optimal in stochastic settings.
Study on reward poisoning attacks on CMAB, revealing attackability depends on adversary's knowledge.
problem Reward poisoning attacks on Combinatorial Multi-Armed Bandits (CMAB).
method Provided a sufficient and necessary condition for attackability, devised an attack algorithm.
result Attackability of CMAB depends on adversary's knowledge of the bandit instance.
Unified analysis of perturbation-based strategies in stochastic and adversarial bandit problems.
problem Optimality of perturbation-based strategies in multi-armed bandit problems.
method Unified regret analysis for stochastic and adversarial settings, using perturbations of sub-Weibull and bounded support.
result Unified bounds for perturbations in both stochastic and adversarial settings, with optimal perturbations of Frechet-type.
Algorithm improves online learning in adversarial bandits.
problem Online learning in adversarial multi-armed bandits with non-uniform best arm distribution.
method Online-within-online setup, inner and outer learners, leveraging non-uniform empirical distribution of best arms.
result Improves regret bounds for non-uniform best arm distributions.
New model for display advertising with stochastic and adversarial components.
problem Display advertising with stochastic and adversarial click-through-rates.
method Adversarial scaling model; two algorithms tested: action elimination and mirror descent.
result Two algorithms are robust to adversarial scaling.
Book introduces multi-armed bandits for decision-making under uncertainty.
problem Decision-making under uncertainty with limited information.
method Self-contained chapters covering various types of bandits.
result Provides a comprehensive introduction to multi-armed bandits.
The paper connects discrete choice models to multi-armed bandit algorithms with sublinear regret bounds.
problem Optimizing user choices in a multi-armed bandit setting.
method Establishes connections between discrete choice models and multi-armed bandit algorithms, providing sublinear regret bounds and novel algorithms.
result Sublinear regret bounds for a family of algorithms, including the Exp3 algorithm.
This paper extends FTPL algorithms for bandits beyond bounded hazard rate assumptions.
problem Adversarial multi-armed bandit problem with perturbations.
method Introduces new regret bounds for FTPL algorithms without bounded hazard rate assumption.
result Gaussian distribution leads to near optimal regret, up to logarithmic factors.
New algorithm reduces regret in corrupted bandits.
problem Stochastic multi-armed bandits with adversarial corruption.
method A new algorithm that is agnostic to corruption levels.
result Regret is nearly optimal and can handle significant corruption.
The paper provides robustness guarantees for mode estimation in bandits.
problem Understanding robustness in mode estimation under adversarial data contamination.
method Simple randomization and theoretical analysis of multi-armed bandits.
result Regret guarantees for various modal bandit problems.
New study on regret lower bounds for multi-agent multi-armed bandit problems.
problem Understanding the limits of performance in multi-agent multi-armed bandit problems.
method Comprehensive study on different settings, establishing tight lower bounds.
result First comprehensive study on regret lower bounds across various settings.
Improved ε ε ε -greedy handles strategic bidding in PPC auctions.
problem Strategic bidding in PPC auctions with personalization and corruptions.
method Extended ε ε ε -greedy to handle strategic arms in contextual multi-arm bandit. result ε ε ε -greedy is robust to adversarial corruptions and degrades linearly with corruption. New algorithm optimizes multi-armed bandit performance in stochastic and adversarial settings.
problem Optimizing multi-armed bandit performance in both stochastic and adversarial environments.
method Follow-the-regularized-leader method with adaptive learning rates.
result First BOBW algorithm with gap-variance-dependent regret bounds in adversarial settings.
Improved FTRL algorithm for multi-armed bandits with various regularizers and multiple optimal arms.
problem Designing adaptive multi-armed bandit algorithms that perform optimally in both stochastic and adversarial settings.
method Follow-the-Regularized-Leader (FTRL) algorithm with a broad family of regularizers and a new learning rate schedule.
result Uniqueness of optimal arm assumption is unnecessary for FTRL with a broad family of regularizers.
A new federated bandit problem with multiple adversaries, solved with a near-optimal algorithm.
problem Non-stochastic federated multi-armed bandit problem with multiple adversaries.
method Proposed a near-optimal federated bandit algorithm called FEDEXP3.
result Guaranteed sub-linear regret without exchanging sequences of selected arm identities or loss sequences among agents.
A budget-constrained multi-armed bandit problem with multiple plays is analyzed for both stochastic and adversarial settings.
problem Optimizing decisions in a multi-armed bandit problem with a budget constraint for multiple plays.
method Upper Confidence Bound (UCB) algorithm for stochastic case and an extension of Exp3 algorithm for adversarial case.
result Achieved regret bounds for both stochastic and adversarial settings.
Adaptive MAB algorithms handle composite, anonymous feedback without reward interval knowledge.
problem Multi-armed bandit with composite and anonymous feedback, especially without reward interval size knowledge.
method Proposed adaptive algorithms for stochastic and adversarial cases, without reward interval knowledge.
result First algorithm for adversarial case handling non-oblivious adversary and unknown reward interval size.
The paper shows optimal robustness against adversarial corruption in sequential decision-making problems.
problem Optimal robustness to adversarial corruption in online decision-making problems.
method Investigates prediction with expert advice and multi-armed bandit problems, focusing on algorithms with decreasing learning rates and second-order regret bounds.
result Optimal robustness can be expressed by a square-root dependency on the amount of corruption, achieving O ( log N Δ + C log N Δ ) O(\frac{\log N}{\Delta} + \sqrt{\frac{C \log N}{\Delta}}) O ( Δ l o g N + Δ C l o g N ) -regret. New RL algorithm tackles adversarial RMAB with unknown transitions and bandit feedback.
problem Learning in episodic RMAB with unknown transition functions and adversarial rewards.
method Developed a novel RL algorithm with a biased reward estimator and an index policy.
result Achieved i l d e O ( H T ) ilde{\mathcal{O}}(H\sqrt{T}) i l d e O ( H T ) regret bound for adversarial RMAB. Solves open problem in adversarial partial monitoring, classifying all games.
problem Classifying all finite adversarial partial monitoring games.
method Simplified and improved existing algorithms, proved upper and lower bounds.
result Complete classification of finite adversarial partial monitoring games.
Adversaries can manipulate bandit algorithms to control chosen actions.
problem Manipulating stochastic bandit algorithms to influence chosen actions.
method Proposes an attack against ε ε ε -greedy and UCB algorithms without knowing mean rewards. result Attackers can control actions with logarithmic effort, making it easy to hijack behavior.
Unified meta-algorithm improves average performance across similar tasks in adversarial bandits.
problem Improving performance across multiple similar tasks in adversarial bandit settings.
method Unified meta-algorithm for multi-armed bandits and bandit linear optimization, tuning initialization, step-size, and entropy parameters.
result Unified meta-algorithm yields setting-specific guarantees for MAB and BLO, improving task-averaged regret.
New algorithms tackle adversarial multi-player bandits with forced-collision communication.
problem No-sensing adversarial multi-player multi-armed bandits (MP-MAB) problem.
method Adversary-Adaptive Collision-Communication (A2C2) algorithms, attackability-aware and unaware settings, information-theoretic tools, error-correction coding.
result Asymptotic attackability-dependent sublinear regret achieved, with or without knowing attackability.
INF-clip optimizes heavy-tailed MAB problems with improved performance.
problem Optimizing multi-armed bandit problems with heavy-tailed rewards.
method INF-clip algorithm for adversarial and stochastic heavy-tailed MAB settings.
result INF-clip is optimal for linear and non-linear heavy-tailed stochastic MAB problems.
New algorithm reduces policy regret in tallying bandits.
problem Measuring online learning performance against adaptive adversaries.
method Tallying bandit model, efficient algorithm with complete policy regret guarantee.
result Achieves a complete policy regret guarantee of i l d e O ( m K T ) ilde{\mathcal{O}}(mK\sqrt{T}) i l d e O ( m K T ) . New algorithms for uncoordinated spectrum access with multi-user multi-armed bandits.
problem Uncoordinated spectrum access with unknown number of users and channels.
method Developed algorithms for stochastic and adversarial settings, combining Exp3.P for dynamic scenarios.
result Sub-linear regret guarantees for both stochastic and adversarial cases, even when users outnumber channels.
Multi-armed bandit problems are the most basic examples of sequential decision problems with an exploration-exploitation trade-off. This is the balance between staying with the option that gave highest payoffs in the past and exploring new options that might give higher payoffs in the future. Although the study of band…
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.
Optimal semi-bandit algorithm for both stochastic and adversarial environments.
problem Optimal semi-bandit algorithm for both stochastic and adversarial environments.
method Developed a general semi-bandit algorithm that achieves O ( log T ) \mathcal{O}(\log T) O ( log T ) regret for stochastic and O ( T ) \mathcal{O}(\sqrt{T}) O ( T ) regret for adversarial environments without regime or T T T knowledge. result First algorithm to achieve optimal O ( log T ) \mathcal{O}(\log T) O ( log T ) and O ( T ) \mathcal{O}(\sqrt{T}) O ( T ) regret simultaneously for stochastic and adversarial environments. New algorithm optimizes dueling bandits for both stochastic and adversarial preferences.
problem Optimizing decision-making in environments where only relative preferences are observed.
method Proposed a reduction from dueling bandits to multi-armed bandits, achieving optimal regret bounds.
result First best-of-both-world result for dueling bandits, optimal regret bound for Condorcet-winner benchmark.
A study on incentivizing strategic arms to share rewards in a multi-armed bandit problem.
problem Designing an algorithm to encourage strategic arms to share their rewards with a principal.
method An algorithm that induces a game among the arms where each arm has a dominant strategy, ensuring the principal sees expected reward μ ′ T − o ( T ) μ'T - o(T) μ ′ T − o ( T ) . result An algorithm that ensures the principal sees expected reward μ ′ T − o ( T ) μ'T - o(T) μ ′ T − o ( T ) , even when arms are strategic or a mix of strategic and non-strategic. New algorithms tackle RKHS bandits with reduced complexity and improved performance.
problem Adversarial and stochastic RKHS bandit problems with high computational complexity.
method Combining approximation theory with misspecified linear bandit methods.
result First general algorithm for adversarial RKHS bandit problem.
A multi-player bandit system resists adversarial attacks with near-optimal regret.
problem Adversaries attempt to manipulate rewards in a multi-player multi-armed bandit game.
method Players communicate a single bit to resist attacks, achieving near-optimal regret.
result Achieves near-optimal regret of O ( log 1 + δ T + W ) O(\log^{1+δ}T + W) O ( log 1 + δ T + W ) , where W W W is the total time of adversarial attacks. 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.
This paper investigates stochastic and adversarial combinatorial multi-armed bandit problems. In the stochastic setting under semi-bandit feedback, we derive a problem-specific regret lower bound, and discuss its scaling with the dimension of the decision space. We propose ESCB, an algorithm that efficiently exploits t…
New algorithm handles bandit problems under translations and scales.
problem Adversarial multi-armed bandit problems with arbitrary translations and scales.
method Innovative online algorithm invariant to translations and scales, using universal prediction.
result Second-order regret bounds, unaffected by affine transformations of losses.
Two new algorithms improve performance in adversarial bandits with unbounded losses.
problem Adversarial Multi-Armed Bandits with unbounded losses.
method Developed UMAB-NN and UMAB-G for non-negative and general unbounded losses respectively.
result UMAB-NN achieves the first adaptive and scale-free regret bound for non-negative unbounded losses.
Doubling tricks help improve multi-armed bandit algorithms, but their effectiveness depends on the horizon length.
problem Improving the performance of multi-armed bandit algorithms using doubling tricks.
method Analyzed geometric and exponential doubling tricks for different horizon lengths.
result Geometric doubling tricks can conserve regret bounds in O ( T ) O(\sqrt{T}) O ( T ) , but not in O ( log T ) O(\log T) O ( log T ) . Exponential doubling tricks can conserve bounds in O ( log T ) O(\log T) O ( log T ) . New algorithm bounds MAB regret for unknown scale and magnitude of losses.
problem Adversarial Multi Armed Bandits with unknown scale and magnitude of losses.
method Design a bandit Follow The Regularized Leader (FTRL) algorithm with adaptive learning rate.
result First MAB bounds that adapt to L 2 L_2 L 2 and L 1 L_1 L 1 norms of losses. New algorithm optimizes multi-armed bandits with low computational cost.
problem Optimizing multi-armed bandits with low computational cost.
method Proposes a new FTPL algorithm with optimistic principle for ambiguity.
result Unified regret analysis and low computational costs.
New algorithm tackles multi-armed bandit with arbitrary delays and general bounded losses.
problem Scale-free adversarial multi-armed bandit with arbitrary feedback delays.
method SFD-INF combines convex combination trick and doubling/skipping technique.
result Achieves adaptive regret bounds for non-negative and general scale-free losses.
New MAB model for online caching costs.
problem Learning costs of cached items online.
method Synchronization bandits, MirrorSync algorithm.
result Adversarial regret of O ( T 2 / 3 ) O(T^{2/3}) O ( T 2/3 ) for MirrorSync. Paper studies attacks on bandit algorithms and shows how attackers can manipulate data to hijack behavior.
problem Potential attacks on bandit algorithms can cause catastrophic loss in real-world applications.
method Proposes a framework of offline and online attacks on bandit algorithms using convex optimization and adaptive strategies.
result Attackers can force bandit algorithms to pull target arms with high probability by manipulating data.
Banker-OMD improves online learning with delayed feedback.
problem Handling delayed feedback in online learning.
method Generalized Online Mirror Descent (OMD) framework.
result Achieves nearly-optimal performance in three bandit scenarios.
New findings on universal learning in contextual bandits with adversarial rewards.
problem Learning in contextual bandits with time-varying, adversarial rewards.
method Characterization of learnable processes and necessary/sufficient conditions for universal learning.
result Optimistic universal learning for contextual bandits with adversarial rewards is impossible in general.
Algorithm tackles adaptive discretization in adversarial Lipschitz bandits for dynamic pricing and auctions.
problem Adaptive discretization in adversarial Lipschitz bandits.
method Adversarial Zooming algorithm for adaptive discretization.
result First algorithm for adversarial Lipschitz bandits with instance-dependent regret bounds.
New dynamic allocation methods for multi-armed bandit models.
problem Dynamic allocation problems in multi-armed bandit models.
method New types of dynamic allocation problems and proofs for Gittins index decomposition.
result New proofs for Gittins index decomposition and related results.