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 351–400 of 1262 papers

TitleStatusHype
Non-Stationary Contextual Bandit Learning via Neural Predictive Ensemble Sampling—0
Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing—0
Adversarial Attacks on Combinatorial Multi-Armed BanditsCode0
Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation—0
Improved Algorithms for Adversarial Bandits with Unbounded Losses—0
Adversarial Contextual Bandits Go Kernelized—0
Discrete Choice Multi-Armed Bandits—0
Bayesian Design Principles for Frequentist Sequential LearningCode0
Follow-ups Also Matter: Improving Contextual Bandits via Post-serving Contexts—0
Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience—0
A Convex Framework for Confounding Robust InferenceCode0
Task Selection and Assignment for Multi-modal Multi-task Dialogue Act Classification with Non-stationary Multi-armed Bandits—0
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits—0
Doubly High-Dimensional Contextual Bandits: An Interpretable Model for Joint Assortment-Pricing—0
The Best Arm Evades: Near-optimal Multi-pass Streaming Lower Bounds for Pure Exploration in Multi-armed Bandits—0
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits—0
Concentrated Differential Privacy for Bandits—0
Pure Exploration under Mediators' Feedback—0
Stochastic Graph Bandit Learning with Side-Observations—0
Learning How to Price Charging in Electric Ride-Hailing Markets—0
Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity ConstraintsCode0
On Universally Optimal Algorithms for A/B Testing—0
Clustered Linear Contextual Bandits with Knapsacks—0
Graph Neural Bandits—0
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit ApproachCode0
AdaptEx: A Self-Service Contextual Bandit Platform—0
Cooperative Multi-agent Bandits: Distributed Algorithms with Optimal Individual Regret and Constant Communication Costs—0
Transfer Learning with Partially Observable Offline Data via Causal Bounds—0
Online Matching: A Real-time Bandit System for Large-scale RecommendationsCode0
Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems—0
Contextual Bandits and Imitation Learning via Preference-Based Active Queries—0
Preferences Evolve And So Should Your Bandits: Bandits with Evolving States for Online Platforms—0
Decentralized Smart Charging of Large-Scale EVs using Adaptive Multi-Agent Multi-Armed Bandits—0
VITS : Variational Inference Thompson Sampling for contextual banditsCode0
On Interpolating Experts and Multi-Armed Bandits—0
Adaptive Linear Estimating EquationsCode0
Tracking Most Significant Shifts in Nonparametric Contextual Bandits—0
SHAP@k:Efficient and Probably Approximately Correct (PAC) Identification of Top-k Features—0
BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits—0
Meta-Learning Adversarial Bandit Algorithms—0
Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization—0
Thompson sampling for improved exploration in GFlowNets—0
Kernel ε-Greedy for Multi-Armed Bandits with Covariates—0
Pure exploration in multi-armed bandits with low rank structure using oblivious sampler—0
You Can Trade Your Experience in Distributed Multi-Agent Multi-Armed Bandits—0
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning—0
Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits—0
Multi-Fidelity Multi-Armed Bandits Revisited—0
Budgeted Multi-Armed Bandits with Asymmetric Confidence IntervalsCode0
Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity—0
Show:102550
← PrevPage 8 of 26Next →

Benchmark Results

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