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 801–850 of 1262 papers

TitleStatusHype
Meta-Learning surrogate models for sequential decision making—0
Meta-Prompt Optimization for LLM-Based Sequential Decision Making—0
Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models—0
Meta-Thompson Sampling—0
Metric-Free Individual Fairness with Cooperative Contextual Bandits—0
Minimax Off-Policy Evaluation for Multi-Armed Bandits—0
Minimax-optimal trust-aware multi-armed bandits—0
Minimax Policy for Heavy-tailed Bandits—0
Mitigating Bias in Adaptive Data Gathering via Differential Privacy—0
Modeling Attrition in Recommender Systems with Departing Bandits—0
Modeling Human Decision-making in Generalized Gaussian Multi-armed Bandits—0
Modelling Cournot Games as Multi-agent Multi-armed Bandits—0
Model selection for behavioral learning data and applications to contextual bandits—0
Model Selection for Generic Contextual Bandits—0
Model Selection in Contextual Stochastic Bandit Problems—0
Model Selection in Reinforcement Learning with General Function Approximations—0
Modified Meta-Thompson Sampling for Linear Bandits and Its Bayes Regret Analysis—0
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning—0
More Robust Doubly Robust Off-policy Evaluation—0
Mortal Multi-Armed Bandits—0
Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities—0
Multi-Agent Multi-Armed Bandits with Limited Communication—0
Multi-agent Multi-armed Bandit with Fully Heavy-tailed Dynamics—0
Multi-Agent Stochastic Bandits Robust to Adversarial Corruptions—0
Multi-armed Bandit Learning for TDMA Transmission Slot Scheduling and Defragmentation for Improved Bandwidth Usage—0
Multi-Armed Bandits and Quantum Channel Oracles—0
Multi-armed Bandits: Competing with Optimal Sequences—0
Multi-Armed Bandits for Correlated Markovian Environments with Smoothed Reward Feedback—0
Multi-Armed Bandits for Intelligent Tutoring Systems—0
Multi-armed Bandits for Link Configuration in Millimeter-wave Networks—0
Multi-Armed Bandits for Minesweeper: Profiting from Exploration-Exploitation Synergy—0
Multi-Armed Bandits in Metric Spaces—0
Multi-Armed Bandits Meet Large Language Models—0
Multi-armed bandits on implicit metric spaces—0
Multi-Armed Bandits on Partially Revealed Unit Interval Graphs—0
Multi-Armed Bandits with Abstention—0
Multi-armed Bandits with Application to 5G Small Cells—0
Multi-Armed Bandits with Bounded Arm-Memory: Near-Optimal Guarantees for Best-Arm Identification and Regret Minimization—0
Multi-Armed Bandits with Censored Consumption of Resources—0
Multi-armed Bandits with Compensation—0
Multi-armed Bandits with Cost Subsidy—0
Multi-Armed Bandits with Dependent Arms—0
Multi-Armed Bandits with Generalized Temporally-Partitioned Rewards—0
Multi-Armed Bandits with Interference—0
Multi-Armed Bandits with Local Differential Privacy—0
Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards—0
Multi-Armed Bandits with Metric Movement Costs—0
Multi-Armed Bandits with Self-Information Rewards—0
Multi-Fidelity Multi-Armed Bandits Revisited—0
Multilinguality in LLM-Designed Reward Functions for Restless Bandits: Effects on Task Performance and Fairness—0
Show:102550
← PrevPage 17 of 26Next →

Benchmark Results

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