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

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Showing 526–550 of 1262 papers

TitleStatusHype
Generalized Risk-Aversion in Stochastic Multi-Armed Bandits—0
Generalized Thompson Sampling for Contextual Bandits—0
Generalized Translation and Scale Invariant Online Algorithm for Adversarial Multi-Armed Bandits—0
Generalizing distribution of partial rewards for multi-armed bandits with temporally-partitioned rewards—0
Genetic multi-armed bandits: a reinforcement learning approach for discrete optimization via simulation—0
GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits—0
Global Bandits—0
Global Rewards in Restless Multi-Armed Bandits—0
Gradient-free Online Learning in Continuous Games with Delayed Rewards—0
Graph Clustering Bandits for Recommendation—0
Graph-Dependent Regret Bounds in Multi-Armed Bandits with Interference—0
Practical Contextual Bandits with Feedback Graphs—0
Graph Neural Bandits—0
Greedy Algorithm almost Dominates in Smoothed Contextual Bandits—0
Greedy Algorithm for Structured Bandits: A Sharp Characterization of Asymptotic Success / Failure—0
Greedy Bandits with Sampled Context—0
Greybox fuzzing as a contextual bandits problem—0
Contextual Bandits for adapting to changing User preferences over time—0
Guaranteed Fixed-Confidence Best Arm Identification in Multi-Armed Bandits: Simple Sequential Elimination Algorithms—0
GuideBoot: Guided Bootstrap for Deep Contextual Bandits—0
Contextual Bandits for Advertising Budget Allocation—0
Hawkes Process Multi-armed Bandits for Disaster Search and Rescue—0
HD-CB: The First Exploration of Hyperdimensional Computing for Contextual Bandits Problems—0
Heterogeneous Multi-Agent Bandits with Parsimonious Hints—0
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses—0
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Benchmark Results

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