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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 601–625 of 1262 papers

TitleStatusHype
Instance-optimal PAC Algorithms for Contextual Bandits—0
Concentrated Differential Privacy for Bandits—0
Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward—0
BanditRank: Learning to Rank Using Contextual Bandits—0
A conversion theorem and minimax optimality for continuum contextual bandits—0
Learning to Use Learners' Advice—0
Contextual Information-Directed Sampling—0
Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain—0
Contextual Linear Bandits with Delay as Payoff—0
Is Offline Decision Making Possible with Only Few Samples? Reliable Decisions in Data-Starved Bandits via Trust Region Enhancement—0
Is Prior-Free Black-Box Non-Stationary Reinforcement Learning Feasible?—0
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon—0
Joint Representation Training in Sequential Tasks with Shared Structure—0
Contextual Multi-Armed Bandits for Causal Marketing—0
Kernel-based Multi-Task Contextual Bandits in Cellular Network Configuration—0
Bandits Don’t Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits—0
Kernel ε-Greedy for Multi-Armed Bandits with Covariates—0
Kernel Methods for Cooperative Multi-Agent Contextual Bandits—0
KL-regularization Itself is Differentially Private in Bandits and RLHF—0
Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits—0
Contextual Pandora's Box—0
Lagrangian Index Policy for Restless Bandits with Average Reward—0
Confidence-Budget Matching for Sequential Budgeted Learning—0
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning—0
From Bandits to Experts: A Tale of Domination and Independence—0
Show:102550
← PrevPage 25 of 51Next →

Benchmark Results

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