New algorithm for duelling bandits with weak regret in adversarial settings.
problem Improving performance in duelling bandits with weak regret.
method Developed an algorithm for duelling bandits in adversarial environments, considering the Borda winner.
result Algorithm provides theoretical guarantees in both utility-based and unrestricted settings.
Efficient bandit exploration for various distributions without distribution-specific tuning.
problem Optimizing exploration in multi-armed bandit models for different distributions.
method Sub-sampling Duelling Algorithms (SDA) with Random Block sampling for efficient exploration.
result Achieves asymptotically optimal regret for Bernoulli, Gaussian, and Poisson distributions.
Improved Thompson Sampling algorithms for bandits with tighter regret bounds.
problem Efficient and adaptive algorithms for stochastic bandits with bounded rewards.
method Proposed two parameterized Thompson Sampling-based algorithms: TS-MA-α and TS-TD-α.
result Achieved O(Kln^(α+1)(T)/Δ) regret bound, improving scalability and resource allocation.
RL agent learns to place limit orders for trading signals in financial markets.
problem Training an RL agent to execute trading signals in limit order book markets.
method Deep Duelling Double Q-learning with APEX architecture, using synthetic alpha signals.
result RL agent outperforms heuristic trading strategies in inventory management and order placing.
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.
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.
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.
Paper solves stochastic contextual linear bandits using linear bandit algorithms.
problem Stochastic contextual linear bandits with unknown context distribution.
method Establishes a reduction framework to convert to linear bandit problems.
result Achieves nearly optimal regret bound of O ( d T log T ) O(d\sqrt{T\log T}) O ( d T log T ) . Paper tackles LDP bandits learning with improved results and sub-linear regret.
problem Contextual bandits learning with LDP privacy constraints.
method Simple black-box reduction frameworks for context-free bandits, extended to GLB.
result First result for BCO with multi-point feedback under LDP, sub-linear regret for GLB.
Algorithm identifies best arm in linked bandits with reduced feedback.
problem Best arm identification in linked bandits with reduced feedback.
method Combines uniform sampling with regular bandit algorithm.
result Almost matching upper and lower bounds on sample complexity.
Graph-Triggered Bandits unify rested and restless bandits with graph-defined arm interactions.
problem Modeling sequential decision-making problems with evolving arm rewards.
method Graph-Triggered Bandits (GTBs) framework that generalizes rested and restless bandits using a graph.
result Rested and restless bandits are special cases of GTBs for suitable graphs.
New definition resolves ambiguity in non-stationary bandit classification.
problem Ambiguity in classifying non-stationary bandits using existing definitions.
method Introducing a formal definition that resolves ambiguity and provides a unified approach.
result Unified approach applicable to both Bayesian and frequentist formulations, resolves classification issues.
Unified approach for non-stationary and clustered bandits.
problem Solving non-stationary and clustered bandits with overlapping solutions.
method Test of homogeneity for seamless integration of non-stationary and clustered bandits.
result Unified solution framework for change detection and cluster identification.
A framework for auto-tuning hyper-parameters in contextual bandit algorithms.
problem Auto-tuning hyper-parameters in real-time for contextual bandit algorithms.
method Proposes a Syndicated Bandits framework to learn multiple hyper-parameters dynamically.
result Achieves optimal regret bounds under certain scenarios and handles multiple contextual bandit algorithms.
New algorithm learns optimal exploration parameters for contextual bandits.
problem Learning optimal exploration in contextual bandits.
method Proposes two algorithms that learn optimal exploration parameters online based on context and reward.
result Demonstrates improved performance in learning optimal exploration compared to traditional methods.
Investigates sequential problems on graph structures and large action spaces.
problem Sequential decision-making on graph structures and large action spaces.
method Spectral bandits, side observations, influence maximization, kernel bandits, polymatroid bandits, function optimization, infinitely many-arms bandits.
result Contributions to graph and structured bandits.
A new method for dueling bandits improves performance.
problem Improving decision-making in dueling bandits.
method Sup-KLUCB method for K-armed dueling bandits, converting Copeland dueling bandits into standard MAB problems.
result Sup-KLUCB outperforms state-of-the-art methods in Copeland dueling bandits.
Study on indexability of restless multi-armed bandits and rollout policy performance.
problem Maximizing discounted rewards in finite state restless multi-armed bandit problems.
method Decouple the problem into single-armed restless bandits, analyze using value iteration, and compare with Whittle index policy.
result Demonstrates conditions for indexability and compares performance of index policy and rollout policy.
New insights into multi-armed bandits with budget constraints.
problem Multi-armed bandits with supply/budget constraints.
method Characterization of logarithmic regret rates, simple regret, and reduction to other bandit problems.
result Full characterization of logarithmic, instance-dependent regret rates for BwK.
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 ε ε -Thresholding Bandit Problem (TBP). result Asymptotically optimal algorithm for TBP.
A meta-UCB method combines stochastic bandit algorithms.
problem Combining multiple stochastic bandit algorithms efficiently.
method Meta-UCB procedure solving an N-armed bandit problem.
result Final regret depends only on the best base algorithm's regret.
New method for contextual bandits with corrupted context.
problem Contextual bandits with corrupted context in online settings.
method Combining contextual bandit and multi-armed bandit approaches.
result Improved learning from all iterations, including corrupted ones.
A new algorithm for top-k combinatorial bandits with full-bandit feedback.
problem Optimizing decisions among subsets of arms with only aggregate rewards observed.
method CSAR algorithm using Hadamard matrices for estimating individual arm rewards.
result Proved a tight lower bound on sample complexity for k = O ( 1 ) k=O(1) k = O ( 1 ) . A new framework for structured bandits using influence diagrams and variational Thompson sampling.
problem Complex statistical dependencies in structured bandit problems.
method Influence diagram framework, variational Thompson sampling, tracking structured posterior distribution.
result Empirically evaluated algorithms perform as well as or better than existing baselines.
Smooth Contextual Bandits bridge two previously studied extremes of non-differentiable and parametric-response bandits.
problem Nonparametric contextual bandits with Hölder smoothness.
method Developed a novel algorithm that optimally balances between non-differentiable and parametric-response bandits.
result Proved the algorithm achieves rate-optimal regret for all smoothness settings.
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.
First robust bandit algorithm for contextual bandits with sub-linear regret.
problem Vulnerability of linear contextual bandit algorithms to adversarial attacks.
method Proposes a robust bandit algorithm for stochastic linear contextual bandits under fully adaptive and omniscient attacks.
result Sub-linear regret under various attacks without requiring attack information.
Unified framework for high-dimensional bandit problems with low-dimensional structures.
problem Stochastic high-dimensional bandit problems with low-dimensional structures.
method Proposed a simple unified algorithm and a general analysis framework for the regret upper bound.
result Unified algorithm achieves comparable regret bounds in various high-dimensional bandit problems.
Adapts multi-armed bandits to contextual bandits using logistic regression.
problem Online decision-making with contextual information and binary rewards.
method Adapts multi-armed bandits policies to contextual bandits using logistic regression and bootstrapping.
result Adaptive-Greedy algorithm shows better performance than upper confidence bound and Thompson sampling.
Survey and compare PAC-Bayes bounds for bandit problems.
problem Designing and evaluating bandit algorithms with strong performance guarantees.
method PAC-Bayes bounds applied to bandit problems.
result PAC-Bayes bounds useful for offline bandit algorithms, but loose for online algorithms.
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.
New algorithms reduce regret for convex bandits with small comparator norms.
problem Optimizing in bandit convex optimization with varying comparator norms.
method Developed algorithms using techniques from full-information setting and new gradient estimators.
result Regret bounds are small when comparator norm is small.
Robustly combines supervised and bandit feedback for contextual bandits.
problem Learning from mixed supervised and bandit data with potentially misaligned costs.
method Developed no-regret algorithms robust to misaligned cost distributions.
result Our approach is feasible and helpful in practice, as shown by empirical evaluations.
Paper tackles domain adaptation for contextual bandits with sub-linear regret.
problem Adapting contextual bandit algorithms across domains with distribution shift.
method Learn a bandit model for the target domain using feedback from the source domain.
result Sub-linear regret bound maintained across domains.
Conversational UCB accelerates bandit learning with user feedback.
problem Slow learning speed in traditional contextual bandit algorithms.
method Generalized contextual bandit to conversational contextual bandit, leveraging both behavioral and conversational feedbacks.
result ConUCB achieves a smaller regret upper bound, indicating faster learning speed.
New model tackles causal bandits with dependent variables.
problem Understanding reward-maximizing interventions in causal networks with dependent variables.
method Introduces hierarchical causal bandit model with a contextual variable capturing interactions among variables.
result Derives nearly matching regret bounds for binary context in causal bandits with dependent arms.
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.
The paper tackles lifelong learning in multi-armed bandits, aiming to minimize average regret over multiple tasks.
problem Minimizing average regret in multi-armed bandits over multiple tasks.
method Confidence interval tuning of UCB algorithms and greedy algorithms applied to a bandit over bandit approach.
result Empirical improvement over previous work in the mortal bandit problem.
New algorithm for partially observable contexts in finance.
problem Decision making based on partially observable, correlated market information.
method EMKF-Bandit algorithm integrating system identification, filtering, and bandit algorithms.
result Sub-linear regret under conditions on filtering.
New meta-learning approach for bandit policies that achieve high average reward.
problem Designing bandit policies that balance between worst-case and Bayesian assumptions.
method Differentiable parameterized policies optimized using policy gradients.
result Proposed algorithm achieves low regret and is practical for various bandit problems.
A new algorithm reduces regret in cooperative multi-agent bandits with heavy-tailed data.
problem Cooperative multi-agent bandits with heavy-tailed data.
method MP-UCB algorithm incorporating robust estimation with message-passing protocol.
result Optimal regret bounds for MP-UCB in various settings.
Paper addresses DP in bandits, focusing on zCDP and providing private algorithms.
problem Privacy concerns in recommender systems using user-sensitive data.
method Formalizes and compares different DP adaptations to bandits, proposes private algorithms for various bandit settings.
result Private algorithms ensure negligible privacy costs compared to non-private regret.
Modified Meta-TS for linear contextual bandits reduces regret.
problem Optimizing decision-making in dynamic environments with context vectors.
method Meta-TSLB algorithm for linear contextual bandits, analyzing Bayes regret.
result Derives an O ( ( m + log ( m ) ) n log ( n ) ) O((m+\log(m))\sqrt{n\log(n)}) O (( m + log ( m )) n log ( n ) ) bound on Bayes regret. Study online multiclass classification under bandit feedback, extending previous results.
problem Online multiclass classification with bandit feedback, focusing on label space unboundedness.
method Extend Daniely and Helbertal's results, show necessity and sufficiency of Bandit Littlestone dimension for learnability.
result Sequential uniform convergence is necessary but not sufficient for bandit online learnability.
Thompson Sampling bounds for contextual bandits with sub-Gaussian rewards.
problem Improving the performance of Thompson Sampling in contextual bandits with sub-Gaussian rewards.
method Proved comprehensive bounds on Thompson Sampling expected cumulative regret based on mutual information and lifted information ratio for sub-Gaussian rewards.
result Explicit regret bounds for various contextual bandit scenarios.
Develops first optimal algorithm for logistic bandits.
problem Pure exploration in logistic bandits.
method Logistic track-and-stop (Log-TS) algorithm.
result Asymptotically matches lower bound for expected sample complexity.
Bayesian bandit algorithms with approximate inference improve regret bounds in stochastic linear bandits.
problem Theoretical justification for Bayesian bandit algorithms with approximate inference in stochastic linear bandits.
method Proposed a theoretical framework to analyze approximate inference impact and conducted frequentist regret analysis on LinTS and LinBUCB.
result LinTS and LinBUCB preserve their original regret upper bounds with larger constant terms in approximate inference settings.
OSOM solves multi-armed and linear contextual bandits efficiently.
problem Simultaneously optimal algorithm for multi-armed and linear contextual bandits.
method Design of a single computationally efficient algorithm that adapts to both regimes.
result Simultaneously optimal regret rates in both simple multi-armed and linear contextual bandits.