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.
Improved Boltzmann exploration reduces regret in stochastic MAB problems.
problem Improving regret in stochastic multi-armed bandit problems.
method A modified Boltzmann exploration with a doubling trick.
result Achieves O ( K log 1 + α T ) O(K\log^{1+α} T) O ( K log 1 + α T ) regret, improving on previous results. New insights into balancing reward and fairness in stochastic MAB.
problem Balancing reward and fairness in stochastic multi-armed bandits.
method Formulated a penalization framework and proposed a hard-threshold UCB-like algorithm.
result Asymptotic fairness, nearly optimal regret, better reward-fairness tradeoff.
Survey of MAB strategies for non-stationary reward distributions with delayed feedback.
problem Optimizing product availability in an online grocery pick-up platform with non-stationary and delayed reward feedback.
method Evaluation of ε ε ε -greedy, UCB1, Thompson Sampling, and a new adaptive technique (AG1) in MAB simulations. result AG1 outperforms traditional MAB strategies in minimizing regret for non-stationary and delayed feedback.
We extend Bayesian multi-armed bandit (MAB) algorithms beyond their original setting by making use of sequential Monte Carlo (SMC) methods. A MAB is a sequential decision making problem where the goal is to learn a policy that maximizes long term payoff, where only the reward of the executed action is observed. In the …
This paper analyzes the multi-armed bandit model using path-integral methods.
problem Understanding the stochastic dynamics and optimal strategies in multi-armed bandit problems.
method Path-integral analysis of statistical physics.
result Emergence of multimodal regret distribution with large regrets from exploitation of sub-optimal arms.
New model for multi-armed bandits with growing arms.
problem Balancing exploration and exploitation in a growing set of arms.
method Introduces Ballooning Multi-Armed Bandits (BL-MAB) and analyzes existing algorithms.
result Achieves sub-linear regret under certain conditions.
The paper studies MAB problems with LDP to balance privacy and service quality.
problem Balancing privacy and service quality in multi-armed bandit systems.
method Investigates regret minimization for MAB with LDP guarantee, proving lower bounds and proposing matching upper bounds algorithms.
result Regret upper bounds match lower bounds up to constant factors for MAB algorithms with LDP guarantee.
New algorithm ensures consistent results in constrained MAB problems.
problem Achieving consistent results in constrained MAB problems.
method Developed replicable algorithms for constrained MAB problems using the optimism principle.
result Regret and constraint violation of replicable algorithms match those of non-replicable ones.
Study on individual regret in cooperative MAB with agents communicating over a graph.
problem Individual regret in cooperative stochastic multi-armed bandits with communication constraints.
method Analyzed COOP-SE algorithm, derived individual regret bounds under various communication constraints.
result First to show an individual regret bound in cooperative stochastic MAB independent of graph diameter.
Improved UCB method for stochastic bandits using distance tuning.
problem Improving performance in stochastic bandit problems.
method Tuning confidence bounds based on bandit distance.
result Empirically shows increased performance compared to existing methods.
Paper develops robust estimators and strategies for stochastic MABs with heavy-tailed rewards.
problem Stochastic multi-armed bandits with heavy-tailed rewards.
method Proposes a novel robust estimator and perturbation-based exploration strategy.
result Develops upper and lower regret bounds for various perturbations.
New MAB problem with delayed, anonymous feedback analyzed.
problem Delayed, anonymous feedback in stochastic bandits.
method Phase-based extensions of UCB algorithm for SDCAF.
result Sub-linear regret guarantees for proposed algorithms.
Maximin UCB algorithm optimizes energy harvesting for sensor networks.
problem Optimizing energy harvesting for sensor nodes in varying environments.
method Modeling as Maximin Multi-Armed Bandits and proposing Maximin UCB algorithm.
result Maximin UCB algorithm achieves performance guarantees similar to UCB1.
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.
A decentralized policy achieves logarithmic regret for multi-agent MAB problems with communication constraints.
problem Decentralized policy for multi-agent MAB problems with option availability and communication constraints.
method Upper Confidence Bound (UCB) algorithms with non-stationary stochastic communication protocol.
result Guaranteed logarithmic regret for non-fully connected spatial graphs with communication constraints.
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.
We discuss a multiple-play multi-armed bandit (MAB) problem in which several arms are selected at each round. Recently, Thompson sampling (TS), a randomized algorithm with a Bayesian spirit, has attracted much attention for its empirically excellent performance, and it is revealed to have an optimal regret bound in the…
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…
New algorithm identifies optimal actions in large reward spaces efficiently.
problem Finding the best action from a large set of options with minimal trials.
method GenTS-Explore algorithm for real-valued combinatorial pure exploration.
result Achieves optimal sample complexity for large action sets.
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…
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 analyzes bias in sample means of multi-armed bandits.
problem Analyzing bias in sample means of multi-armed bandits.
method Decoupling three sources of bias: sampling, stopping, choosing; using optimism to capture monotonic behaviors.
result Optimistic sampling induces negative bias, while optimistic stopping and choosing induce positive bias.
Paper closes the gap in MP-MAB problems with novel adaptive communication and exploration.
problem Closing the gap between decentralized MP-MAB and natural centralized lower bound.
method BEACON: Batched Exploration with Adaptive COmmunicatioN, incorporating ADC and batched exploration.
result Proves logarithmic regret for a generalized MP-MAB problem.
New algorithms control loss and constraints in uncertain, changing environments.
problem Adapting to adversarial constraints in uncertain, changing environments.
method Developed algorithms for constrained MAB problems with optimal rates of regret and positive constraint violation.
result Achieved optimal rates of regret and positive constraint violation under varying degrees of adversariality.
New algorithms reduce regret in non-stationary bandits with increasing payoffs.
problem Non-stationary bandits with monotonically increasing payoffs.
method R-ed-UCB for rested case and R-less-UCB for restless case.
result Regret bound of O ~ ( T 2 3 ) \widetilde{\mathcal{O}}(T^{\frac{2}{3}}) O ( T 3 2 ) under certain conditions. We formulate the problem of sampling and recovering clustered graph signal as a multi-armed bandit (MAB) problem. This formulation lends naturally to learning sampling strategies using the well-known gradient MAB algorithm. In particular, the sampling strategy is represented as a probability distribution over the indiv…
The paper tackles rested bandits with non-decreasing and concave rewards, deriving lower bounds and an efficient algorithm.
problem Studying the sample complexity and optimal strategies for rested bandits with specific reward properties.
method Deriving regret lower bounds and designing an efficient algorithm R-ed-UCB with theoretical and empirical analysis.
result An efficient algorithm R-ed-UCB with a regret bound of O ~ ( T 2 3 ) \widetilde{\mathcal{O}}(T^{\frac{2}{3}}) O ( T 3 2 ) under certain conditions. New algorithms minimize regret in streaming MAB with memory constraints.
problem Minimizing regret in single-pass streaming MAB with limited memory.
method Developed two algorithms with tight regret bounds for different memory sizes.
result Established tight gap-dependent regret bounds for streaming MAB.
A new algorithm reduces regret in multi-player bandits without collision info.
problem Decentralized multi-player multi-armed bandits with no collision info.
method EC-SIC algorithm using optimal error correction coding for reward statistics.
result Regret approaches that of centralized with collision info.
A new bandit problem where experiments can be interrupted if results are not promising.
problem Interruptible multi-armed bandit problem with a threshold for cumulative reward.
method Formalized survival regret, identified key components (regret and probability of ruin), derived lower bounds and optimal policies.
result No policy can achieve sublinear survival regret, but optimal policies minimize survival regret in a Pareto sense.
In this work, we study recommendation systems modelled as contextual multi-armed bandit (MAB) problems. We propose a graph-based recommendation system that learns and exploits the geometry of the user space to create meaningful clusters in the user domain. This reduces the dimensionality of the recommendation problem w…
New analysis improves Monte Carlo Tree Search for reinforcement learning.
problem Improving Monte Carlo Tree Search for reinforcement learning with finite simulations.
method Established polynomial concentration property of regret for non-stationary MABs, leading to a new UCB with polynomial bonus term.
result MCTS with polynomial bonus term requires nearly optimal sample size for learning value functions.
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.
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.
Trust-aware MAB improves learning performance by accounting for human deviation.
problem Learning performance suffers when humans deviate from recommended policies due to lack of trust.
method Integrates a dynamic trust model into MAB framework, establishing minimax regret and proposing a two-stage trust-aware procedure.
result Proves near-optimal statistical guarantees for trust-aware MAB algorithms.
We study the contextual linear bandit problem, a version of the standard stochastic multi-armed bandit (MAB) problem where a learner sequentially selects actions to maximize a reward which depends also on a user provided per-round context. Though the context is chosen arbitrarily or adversarially, the reward is assumed…
New algorithm for shareable arms with load-dependent rewards in stochastic bandits.
problem Learning optimal play strategy with shareable finite-capacity arms in stochastic bandits.
method Developed a capacity estimator and online learning algorithm for MP-MAB with shareable arms.
result Regret upper bound matches the lower bound, validating the algorithm's performance.
New algorithm for multi-fidelity bandits reduces costs and improves regret.
problem Optimizing decisions with varying costs and accuracy in multi-fidelity bandits.
method Cost complexity bounds, algorithmic framework, elimination-based algorithm.
result New regret definition and matching upper and lower bounds for elimination-based algorithm.
New MAB algorithm for nonstationary, semiparametric reward models.
problem Nonstationary and non-linear reward models in sequential decision tasks.
method Contextual multi-armed bandit algorithm for semiparametric reward model.
result Proposed algorithm achieves a tight regret upper bound and performs well in simulations and real-world data.
Study on bandit problem with fixed number of arm types, achieving optimal regret bounds.
problem Stochastic multi-armed bandit problem with a fixed number of arm types.
method Proposes algorithms achieving optimal regret bounds for the problem.
result Achieves $\mathcal{O}\left( \log n
ight)$ instance-dependent regret and $ ilde{\mathcal{O}}\left( \sqrt{n}
ight)$ instance-independent regret.
Reduces learning regret with diverse user preferences.
problem Reducing regret in stochastic multi-armed bandit problems with diverse user preferences.
method Formulated a stochastic linear bandits model and proposed a Weighted Upper Confidence Bound (W-UCB) algorithm.
result Achieves constant regret when user preferences are sufficiently diverse.
A new batch ensemble method reduces regret in stochastic bandits.
problem Efficiently balancing exploration and exploitation in online RL.
method Proposes a batch ensemble scheme to achieve near-optimal regret.
result Proves near-optimal regret with a single parameter independent of variance.
New batched Langevin Thompson Sampling reduces communication costs for sequential decision making.
problem Efficiently learning unknown reward distributions and transition dynamics in batched settings.
method Langevin Thompson Sampling with logarithmic communication costs.
result Order-optimal regret guarantees for stochastic MABs and RL.
The Multi-Armed Bandits (MAB) framework highlights the tension between acquiring new knowledge (Exploration) and leveraging available knowledge (Exploitation). In the classical MAB problem, a decision maker must choose an arm at each time step, upon which she receives a reward. The decision maker's objective is to maxi…
Improved adaptive algorithms for identifying the best arm in MABs with fixed budget.
problem Identifying the best arm in stochastic Multi-Armed Bandits with a fixed sampling budget.
method Established a connection between Large Deviation Principles and adaptive algorithms, improving error probability bounds and devising new algorithms.
result The \sred algorithm outperforms existing algorithms in identifying the best arm.
UCB algorithm's arm-sampling behavior is revealed, leading to new insights and proofs.
problem Optimizing multi-armed bandit algorithms for worst-case scenarios.
method Analysis of UCB algorithm's arm-sampling behavior and process-level characterization.
result UCB's arm-sampling rates are asymptotically deterministic, regardless of problem complexity.
We extend the classic multi-armed bandit (MAB) model to the setting of noncompliance, where the arm pull is a mere instrument and the treatment applied may differ from it, which gives rise to the instrument-armed bandit (IAB) problem. The IAB setting is relevant whenever the experimental units are human since free will…