SOTAVerified

Multi-Armed Bandits

Multi-armed bandits refer to a task where a fixed amount of resources must be allocated between competing resources that maximizes expected gain. Typically these problems involve an exploration/exploitation trade-off.

( Image credit: Microsoft Research )

Papers

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Showing 451–500 of 1262 papers

TitleStatusHype
Bandit Social Learning: Exploration under Myopic Behavior—0
Adversarial Rewards in Universal Learning for Contextual Bandits—0
Piecewise-Stationary Multi-Objective Multi-Armed Bandit with Application to Joint Communications and SensingCode0
Leveraging User-Triggered Supervision in Contextual Bandits—0
On Private and Robust Bandits—0
Multiplier Bootstrap-based Exploration—0
Randomized Greedy Learning for Non-monotone Stochastic Submodular Maximization Under Full-bandit Feedback—0
Stochastic Contextual Bandits with Long Horizon Rewards—0
Quantum contextual bandits and recommender systems for quantum data—0
Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedback—0
Adversarial Attacks on Adversarial Bandits—0
A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback—0
Contextual Causal Bayesian Optimisation—0
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits—0
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning—0
Quantum Heavy-tailed Bandits—0
Multi-Armed Bandits and Quantum Channel Oracles—0
Multi-armed Bandit Learning for TDMA Transmission Slot Scheduling and Defragmentation for Improved Bandwidth Usage—0
Best Arm Identification in Stochastic Bandits: Beyond β-optimality—0
Local Differential Privacy for Sequential Decision Making in a Changing Environment—0
Contextual Bandits and Optimistically Universal Learning—0
Online Statistical Inference for Contextual Bandits via Stochastic Gradient Descent—0
On the Complexity of Representation Learning in Contextual Linear Bandits—0
Faster Maximum Inner Product Search in High Dimensions—0
MABSplit: Faster Forest Training Using Multi-Armed BanditsCode0
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes—0
Networked Restless Bandits with Positive ExternalitiesCode0
Stochastic Rising BanditsCode0
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm ConfigurationCode0
On Regret-optimal Cooperative Nonstochastic Multi-armed Bandits—0
Incorporating Multi-armed Bandit with Local Search for MaxSATCode0
Constrained Pure Exploration Multi-Armed Bandits with a Fixed Budget—0
Contextual Decision-Making with Knapsacks Beyond the Worst Case—0
Transfer Learning for Contextual Multi-armed Bandits—0
Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning—0
Causal Bandits: Online Decision-Making in Endogenous Settings—0
Bandit Algorithms for Prophet Inequality and Pandora's Box—0
On Penalization in Stochastic Multi-armed Bandits—0
Latent Bottlenecked Attentive Neural ProcessesCode0
Multi-Player Bandits Robust to Adversarial Collisions—0
Hypothesis Transfer in Bandits by Weighted Models—0
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression—0
Generalizing distribution of partial rewards for multi-armed bandits with temporally-partitioned rewards—0
Thompson Sampling for High-Dimensional Sparse Linear Contextual BanditsCode0
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO AlgorithmCode0
Adaptive Data Depth via Multi-Armed BanditsCode0
Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms—0
Revisiting Simple Regret: Fast Rates for Returning a Good Arm—0
Robust Contextual Linear Bandits—0
PAC-Bayesian Offline Contextual Bandits With Guarantees—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NeuralLinear FullPosterior-MRCumulative regret1.92—Unverified
2Linear FullPosterior-MRCumulative regret1.82—Unverified