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 1026–1050 of 1262 papers

TitleStatusHype
Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits—0
Meta-Learning surrogate models for sequential decision making—0
Contextual Bandits with Random Projection—0
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox OptimizationCode0
Perturbed-History Exploration in Stochastic Multi-Armed Bandits—0
Better Algorithms for Stochastic Bandits with Adversarial Corruptions—0
AdaLinUCB: Opportunistic Learning for Contextual Bandits—0
Equal Opportunity in Online Classification with Partial FeedbackCode0
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting—0
A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free—0
Randomized Allocation with Nonparametric Estimation for Contextual Multi-Armed Bandits with Delayed Rewards—0
On the bias, risk and consistency of sample means in multi-armed bandits—0
Target Tracking for Contextual Bandits: Application to Demand Side Management—0
Almost Boltzmann Exploration—0
The Assistive Multi-Armed BanditCode0
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits—0
Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory—0
Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching—0
Parallel Contextual Bandits in Wireless Handover Optimization—0
Imitation-Regularized Offline Learning—0
Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions—0
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit FeedbackCode0
Multi-player Multi-armed Bandits for Stable Allocation in Heterogeneous Ad-Hoc Networks—0
Human-AI Learning Performance in Multi-Armed Bandits—0
Generalizable Meta-Heuristic based on Temporal Estimation of Rewards for Large Scale Blackbox Optimization—0
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Benchmark Results

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