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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 1001–1050 of 1262 papers

TitleStatusHype
Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewardsCode0
Model selection for contextual banditsCode0
Equipping Experts/Bandits with Long-term Memory—0
Rarely-switching linear bandits: optimization of causal effects for the real world—0
Multi-Objective Generalized Linear Bandits—0
Distribution-dependent and Time-uniform Bounds for Piecewise i.i.d Bandits—0
Differential Privacy for Multi-armed Bandits: What Is It and What Is Its Cost?—0
Regret Bounds for Thompson Sampling in Episodic Restless Bandit ProblemsCode0
Top-k Combinatorial Bandits with Full-Bandit Feedback—0
Are sample means in multi-armed bandits positively or negatively biased?—0
Achieving Fairness in Stochastic Multi-armed Bandit Problem—0
OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits—0
Data Poisoning Attacks on Stochastic Bandits—0
Lessons from Contextual Bandit Learning in a Customer Support Bot—0
Tight Regret Bounds for Infinite-armed Linear Contextual Bandits—0
Meta-learners' learning dynamics are unlike learners'—0
Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without—0
Constrained Restless Bandits for Dynamic Scheduling in Cyber-Physical Systems—0
Introduction to Multi-Armed BanditsCode0
Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication—0
Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Bandits—0
Batched Multi-armed Bandits ProblemCode0
A Survey on Practical Applications of Multi-Armed and Contextual Bandits—0
Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits—0
Meta-Learning surrogate models for sequential decision making—0
Contextual Bandits with Random Projection—0
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox OptimizationCode0
Perturbed-History Exploration in Stochastic Multi-Armed Bandits—0
Better Algorithms for Stochastic Bandits with Adversarial Corruptions—0
AdaLinUCB: Opportunistic Learning for Contextual Bandits—0
Equal Opportunity in Online Classification with Partial FeedbackCode0
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting—0
Randomized Allocation with Nonparametric Estimation for Contextual Multi-Armed Bandits with Delayed Rewards—0
A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free—0
On the bias, risk and consistency of sample means in multi-armed bandits—0
Target Tracking for Contextual Bandits: Application to Demand Side Management—0
Almost Boltzmann Exploration—0
Deep Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood Matching—0
The Assistive Multi-Armed BanditCode0
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits—0
Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory—0
Parallel Contextual Bandits in Wireless Handover Optimization—0
Imitation-Regularized Offline Learning—0
Multiplayer Multi-armed Bandits for Optimal Assignment in Heterogeneous NetworksCode1
Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions—0
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit FeedbackCode0
Multi-player Multi-armed Bandits for Stable Allocation in Heterogeneous Ad-Hoc Networks—0
Human-AI Learning Performance in Multi-Armed Bandits—0
Generalizable Meta-Heuristic based on Temporal Estimation of Rewards for Large Scale Blackbox Optimization—0
Balanced Linear Contextual Bandits—0
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Benchmark Results

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