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.
LMC-TS uses MCMC for efficient posterior sampling in contextual bandits.
problem Efficiency of Thompson sampling for high-dimensional contextual bandits.
method Langevin Monte Carlo for direct posterior sampling.
result LMC-TS achieves sublinear regret bound for linear contextual bandits.
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.
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. Paper proposes efficient algorithms for bandit problems with costly sampling.
problem Maximizing expectation function over a finite set with high sampling cost.
method Proposes naive and adaptive stochastic bandit algorithms for PAC solution.
result Adaptive algorithm outperforms naive in terms of number of samples.
BIS uses bandits to efficiently sample from expensive-to-evaluate densities.
problem Sampling from computationally expensive target densities.
method Sequential selection through multi-armed bandits, optimizing sample set directly.
result BIS achieves accurate sampling with fewer evaluations than adaptive methods.
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. AdamCB optimizes neural network training by adaptively selecting samples.
problem Inefficient convergence due to unequal influence of different data samples.
method Integrates combinatorial bandit techniques into Adam to adaptively select samples.
result AdamCB achieves faster convergence and better performance than existing methods.
Proposes sampling from reverse diffusion posteriors for contextual bandits.
problem Complex distributions in contextual bandits.
method Approximate posterior sampling with a diffusion model prior using Laplace approximation.
result Empirically consistent and efficient approximations for contextual bandits.
Predictive sampling improves on Thompson sampling for non-stationary bandit environments.
problem Thompson sampling fails in non-stationary bandit environments.
method Proposes predictive sampling, which deprioritizes actions based on information loss rate.
result Predictive sampling outperforms Thompson sampling in all tested non-stationary environments.
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.
New Thompson Sampling for partially observed context bandits reduces regret logarithmically with time.
problem Improving Thompson Sampling for partially observed context bandits.
method Proposed a Thompson Sampling algorithm for partially observable contextual multi-armed bandits with theoretical performance guarantees.
result Regret scales logarithmically with time and the number of arms, and linearly with the dimension.
We consider the problem of best arm identification in a variant of multi-armed bandits called linked bandits. In a single interaction with linked bandits, multiple arms are played sequentially until one of them receives a positive reward. Since each interaction provides feedback about more than one arm, the sample comp…
Unified framework for ensemble sampling in nonlinear contextual bandits with provable regret bounds.
problem Efficient exploration in nonlinear contextual bandits with unknown feature dimensions.
method Developed GLM-ES and Neural-ES for generalized linear and neural contextual bandits, respectively, using maximum likelihood estimation on randomly perturbed data.
result Unified high-probability frequentist regret bounds for GLM-ES and Neural-ES, matching state-of-the-art results.
Improved statistical efficiency of Thompson Sampling for combinatorial semi-bandits.
problem Efficiency of policies in stochastic combinatorial multi-armed bandits with semi-bandit feedback.
method Analysis of Combinatorial Thompson Sampling (CTS) using Beta and Gaussian priors for mutually independent and multivariate sub-Gaussian outcomes.
result CTS provides an efficient policy with optimal asymptotic regret for both mutually independent and multivariate sub-Gaussian outcomes.
Thompson sampling used for linear bandits with normal-gamma priors.
problem Optimizing decisions in uncertain environments with linear dependencies and unknown parameters.
method Bayesian Thompson sampling with multivariate normal-gamma priors.
result Derivation of a Bayesian regret bound for the approach.
Thompson Sampling shows polynomial regret for combinatorial semi-bandits with subgaussian rewards.
problem Finding optimal solutions in combinatorial semi-bandits with suboptimal sampling.
method Proposes Thompson Sampling with polynomial regret for linear combinatorial semi-bandits.
result Demonstrates 'mismatched sampling paradox' where knowing distributions can lead to worse performance.
Improved Thompson Sampling reduces regret in contextual bandits and reinforcement learning.
problem Thompson Sampling's exploration is insufficient in some contexts.
method Developed Feel-Good Thompson Sampling to address exploration issues.
result Feel-Good Thompson Sampling reduces regret compared to standard Thompson Sampling.
Improved Thompson Sampling for high-dimensional sparse bandits.
problem Stochastic linear contextual bandits with high-dimensional features.
method Thompson Sampling with spike-and-slab priors and variational inference.
result Nearly optimal upper bound on expected cumulative regret.
MetaTS learns to explore better by meta-learning prior from bandit instances.
problem Efficient exploration in bandit problems.
method MetaThompson Sampling (MetaTS) that meta-learns the prior from bandit instances.
result MetaTS quickly adapts to unknown prior and achieves better exploration.
New algorithm for contextual dueling bandits achieves nearly optimal regret.
problem Contextual dueling bandits with feedback on preferred options.
method Proposes FGTS.CDB, a Thompson sampling algorithm for linear contextual dueling bandits.
result Achieves nearly minimax-optimal regret of i l d e O ( d T ) ilde{\mathcal{O}}(d\sqrt T) i l d e O ( d T ) . In this paper, we propose a Double Thompson Sampling (D-TS) algorithm for dueling bandit problems. As indicated by its name, D-TS selects both the first and the second candidates according to Thompson Sampling. Specifically, D-TS maintains a posterior distribution for the preference matrix, and chooses the pair of arms…
A new bandit algorithm for web page item display.
problem Optimizing item display at specific positions on a web page.
method Position-based multiple-play bandits with Thompson sampling.
result Delivers better recommendations with less prior information.
Paper improves Thompson Sampling for linear contextual bandits.
problem Empirical Thompson Sampling does not achieve optimal regret bounds.
method Develops a novel estimator with adaptive data augmentation and coupling.
result Achieves nearly minimax optimal performance.
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.
New method reduces ensemble size for linear bandits, achieving near optimal regret.
problem Achieving near optimal regret in linear bandits with limited ensemble size.
method Ensemble sampling with a size of order d log T d \log T d log T for a d d d -dimensional stochastic linear bandit. result Regret is at most ( d log T ) 5 / 2 T (d \log T)^{5/2} \sqrt{T} ( d log T ) 5/2 T , improving over linear scaling with T T T . 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.
ERTS uses Thompson sampling for Gaussian entropic risk bandits, achieving regret bounds.
problem Risk in decision making complicates reward maximization in MAB problems.
method ERTS (Entropic Risk Thompson Sampling) using Thompson sampling with an entropic risk measure.
result Regret bounds for ERTS under entropic risk measure provided.
Optimizes algorithms for non-concave bandit problems.
problem Optimizing algorithms for non-concave bandit problems.
method Unified zeroth-order optimization paradigm.
result Minimax-optimal algorithms in the dimension for low-rank generalized linear bandit problems.
VITS improves Thompson Sampling for contextual bandits with efficient approximate inference.
problem Intractable posterior sampling in traditional Thompson Sampling for contextual bandits.
method VITS uses Gaussian Variational Inference for efficient posterior approximations.
result VITS achieves sub-linear regret bound similar to traditional TS.
Thompson Sampling is a well established approach to bandit and reinforcement learning problems. However its use in continuum armed bandit problems has received relatively little attention. We provide the first bounds on the regret of Thompson Sampling for continuum armed bandits under weak conditions on the function cl…
Study improves off-policy evaluation from non-i.i.d. bandit samples.
problem Improving off-policy evaluation from non-independent bandit samples.
method Constructing an estimator from a standardized martingale difference sequence.
result Proposed estimator performs better than existing methods.
Randomized exploration in linear bandits achieves optimal regret bounds.
problem Optimizing exploration in high-dimensional linear bandit problems.
method Analysis of Thompson sampling without forced optimism.
result Randomized exploration algorithms achieve an O ( d n log ( n ) ) O(d\sqrt{n} \log(n)) O ( d n log ( n )) regret bound in smooth, strongly convex action spaces. Efficiently handles contextual bandits with diffusion models.
problem Challenges in online decision-making with contextual bandits.
method Leverage pre-trained diffusion models as priors to capture action dependencies.
result Developed an algorithm for efficient posterior approximation.
FGTSVA improves Thompson Sampling for contextual bandits with optimal variance-aware regret.
problem Optimizing regret bounds for Thompson Sampling in contextual bandits.
method Developed FGTSVA, a variance-aware Thompson Sampling algorithm for contextual bandits with a new decoupling coefficient.
result Achieved optimal regret bound of i l d e O ( d c ⋅ log ∣ F ∣ ∑ t = 1 T σ t 2 + d c ) ilde{O}(\sqrt{\mathrm{dc}\cdot\log|\mathcal{F}|\sum_{t=1}^Tσ_t^2}+\mathrm{dc}) i l d e O ( dc ⋅ log ∣ F ∣ ∑ t = 1 T σ t 2 + dc ) . Algorithm identifies Copeland winners in dueling bandits with ternary feedback.
problem Identifying Copeland winners in dueling bandits with indifferences.
method Proposed POCOWISTA algorithm with a sample complexity close to lower bound.
result Algorithm shows excellent performance, even for conventional dueling bandits.
Neural Thompson Sampling uses deep neural networks for contextual bandit problems.
problem Solving contextual multi-armed bandit problems.
method Adapts deep neural networks for exploration and exploitation in a novel posterior distribution.
result Guaranteed cumulative regret of O ( T 1 / 2 ) \mathcal{O}(T^{1/2}) O ( T 1/2 ) for bounded reward functions. The paper tightens the regret rate for linear bandit problems.
problem Bayesian regret in linear bandit problems.
method Information-theoretic framework and chaining argument.
result Established a new bound with a tight rate of O ( d T ) O(d\sqrt{T}) O ( d T ) . Optimizes matching in weighted graphs with semi-bandit sampling.
problem Finding optimal pairings in weighted graphs with sequential sampling.
method Leverages rank-1 assumption on adjacency matrix to reduce sample complexity and regret.
result Achieves linear dependency in the number of vertices for sample complexity and regret.
Top-k Combinatorial Bandits generalize multi-armed bandits, where at each round any subset of k k k out of n n n arms may be chosen and the sum of the rewards is gained. We address the full-bandit feedback, in which the agent observes only the sum of rewards, in contrast to the semi-bandit feedback, in which the agent obse…
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.
Proposes Genetic Thompson Sampling for multi-armed bandits, improving performance in nonstationary settings.
problem Improving sequential decision making tasks of online learning agents using multi-armed bandits.
method Integrates genetic principles into Thompson Sampling for multi-armed bandits.
result Significantly outperforms baselines in nonstationary settings.
We consider a novel formulation of the multi-armed bandit model, which we call the contextual bandit with restricted context, where only a limited number of features can be accessed by the learner at every iteration. This novel formulation is motivated by different online problems arising in clinical trials, recommende…
In this paper we propose a multi-armed bandit inspired, pool based active learning algorithm for the problem of binary classification. By carefully constructing an analogy between active learning and multi-armed bandits, we utilize ideas such as lower confidence bounds, and self-concordant regularization from the multi…
Thompson sampling provides a solution to bandit problems in which new observations are allocated to arms with the posterior probability that an arm is optimal. While sometimes easy to implement and asymptotically optimal, Thompson sampling can be computationally demanding in large scale bandit problems, and its perform…
Thompson Sampling is at most twice as bad as any other policy in Bayesian bandit models.
problem Optimizing selection of the best arm in Bayesian bandit models with independent latent processes.
method Thompson Sampling approach applied to models with independent latent arm processes.
result Thompson Sampling makes at most twice the expected number of mistakes compared to any other policy.
DPPS uses DP priors for Bayesian non-parametric multi-arm bandits.
problem Optimizing multi-arm bandit environments with prior beliefs.
method Bayesian non-parametric algorithm based on Dirichlet Process priors.
result DPPS provides principled incorporation of prior beliefs and is optimal in Bayesian regret setup.
Bayesian optimization improved for biased data.
problem Adversarial bias in observations, especially hidden confounders.
method Reduction to dueling bandits, information-directed sampling (IDS).
result First efficient kernelized algorithm with regret guarantees.