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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 651–700 of 1262 papers

TitleStatusHype
Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric—0
Nonparametric Stochastic Contextual Bandits—0
Non-Stationary Contextual Bandit Learning via Neural Predictive Ensemble Sampling—0
Adversarial Rewards in Universal Learning for Contextual Bandits—0
Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset—0
Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach—0
Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback—0
Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without—0
No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization—0
No-Regret Learning for Fair Multi-Agent Social Welfare Optimization—0
Observation-Augmented Contextual Multi-Armed Bandits for Robotic Search and Exploration—0
Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search—0
Offline Clustering of Linear Bandits: Unlocking the Power of Clusters in Data-Limited Environments—0
Offline Contextual Bandits for Wireless Network Optimization—0
Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation—0
Offline Learning for Combinatorial Multi-armed Bandits—0
Offline Oracle-Efficient Learning for Contextual MDPs via Layerwise Exploration-Exploitation Tradeoff—0
Off-policy estimation with adaptively collected data: the power of online learning—0
Off-Policy Evaluation for Large Action Spaces via Policy Convolution—0
Off-Policy Risk Assessment in Contextual Bandits—0
Off-Policy Risk Assessment in Markov Decision Processes—0
On Best-Arm Identification with a Fixed Budget in Non-Parametric Multi-Armed Bandits—0
On conditional versus marginal bias in multi-armed bandits—0
On Differentially Private Federated Linear Contextual Bandits—0
On Finding the Largest Mean Among Many—0
On Interpolating Experts and Multi-Armed Bandits—0
On Kernelized Multi-armed Bandits—0
On Kernelized Multi-Armed Bandits with Constraints—0
On Lai's Upper Confidence Bound in Multi-Armed Bandits—0
On Learning to Rank Long Sequences with Contextual Bandits—0
Online Algorithm for Unsupervised Sequential Selection with Contextual Information—0
Online Allocation and Pricing: Constant Regret via Bellman Inequalities—0
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination—0
Online and Scalable Model Selection with Multi-Armed Bandits—0
Online certification of preference-based fairness for personalized recommender systems—0
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits—0
Generalizable Meta-Heuristic based on Temporal Estimation of Rewards for Large Scale Blackbox Optimization—0
Online Fair Division with Contextual Bandits—0
Online Fair Revenue Maximizing Cake Division with Non-Contiguous Pieces in Adversarial Bandits—0
Online Learning for Autonomous Management of Intent-based 6G Networks—0
Online Learning for Cooperative Multi-Player Multi-Armed Bandits—0
Online Learning in Contextual Bandits using Gated Linear Networks—0
Online learning over a finite action set with limited switching—0
Online Learning under Adversarial Corruptions—0
Online Learning via the Differential Privacy Lens—0
Online Learning with an Unknown Fairness Metric—0
Online learning with Corrupted context: Corrupted Contextual Bandits—0
Online learning with feedback graphs and switching costs—0
Online Learning with Off-Policy Feedback—0
Online Limited Memory Neural-Linear Bandits—0
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Benchmark Results

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