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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 76–100 of 1262 papers

TitleStatusHype
Reinforcement Learning for Machine Learning Model Deployment: Evaluating Multi-Armed Bandits in ML Ops Environments—0
MultiScale Contextual Bandits for Long Term Objectives—0
Sparse Additive Contextual Bandits: A Nonparametric Approach for Online Decision-making with High-dimensional Covariates—0
NeuroSep-CP-LCB: A Deep Learning-based Contextual Multi-armed Bandit Algorithm with Uncertainty Quantification for Early Sepsis PredictionCode0
Sparse Nonparametric Contextual Bandits—0
A New Benchmark for Online Learning with Budget-Balancing Constraints—0
Variance-Dependent Regret Lower Bounds for Contextual Bandits—0
Bi-Criteria Optimization for Combinatorial Bandits: Sublinear Regret and Constraint Violation under Bandit Feedback—0
Locally Private Nonparametric Contextual Multi-armed BanditsCode0
Multiplayer Information Asymmetric Contextual Bandits—0
Graph-Dependent Regret Bounds in Multi-Armed Bandits with Interference—0
Cost-Aware Optimal Pairwise Pure Exploration—0
Greedy Algorithm for Structured Bandits: A Sharp Characterization of Asymptotic Success / Failure—0
Tight Gap-Dependent Memory-Regret Trade-Off for Single-Pass Streaming Stochastic Multi-Armed Bandits—0
Evolution of Information in Interactive Decision Making: A Case Study for Multi-Armed Bandits—0
Towards Understanding the Benefit of Multitask Representation Learning in Decision Process—0
Semi-Parametric Batched Global Multi-Armed Bandits with Covariates—0
Functional multi-armed bandit and the best function identification problems—0
Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models—0
Transfer Learning in Latent Contextual Bandits with Covariate Shift Through Causal TransportabilityCode0
Heterogeneous Multi-Agent Bandits with Parsimonious Hints—0
Multi-agent Multi-armed Bandits with Minimum Reward Guarantee FairnessCode0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
Continuous K-Max Bandits—0
Efficient and Optimal Policy Gradient Algorithm for Corrupted Multi-armed Bandits—0
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Benchmark Results

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