Multi-Armed Bandits
133 papers with code • 1 benchmarks • 1 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Multi-Armed Bandits
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Libraries
Use these libraries to find Multi-Armed Bandits models and implementations
Duolingo Bandit Notifications
Most implemented papers
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
tensorflow/models
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ICLR 2018
At the same time, advances in approximate Bayesian methods have made posterior approximation for flexible neural network models practical.
On-line Adaptative Curriculum Learning for GANs
We argue that less expressive discriminators are smoother and have a general coarse grained view of the modes map, which enforces the generator to cover a wide portion of the data distribution support.
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
backgom2357/Recommender_system_via_deep_RL
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The DRR framework treats recommendation as a sequential decision making procedure and adopts an "Actor-Critic" reinforcement learning scheme to model the interactions between the users and recommender systems, which can consider both the dynamic adaptation and long-term rewards.
Locally Differentially Private (Contextual) Bandits Learning
huang-research-group/LDPbandit2020
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NeurIPS 2020
We study locally differentially private (LDP) bandits learning in this paper.
Multi-Armed Bandits in Metric Spaces
In this work we study a very general setting for the multi-armed bandit problem in which the strategies form a metric space, and the payoff function satisfies a Lipschitz condition with respect to the metric.
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits
facebookresearch/ReAgent
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ICML 2017
We study the off-policy evaluation problem---estimating the value of a target policy using data collected by another policy---under the contextual bandit model.
Semiparametric Contextual Bandits
This paper studies semiparametric contextual bandits, a generalization of the linear stochastic bandit problem where the reward for an action is modeled as a linear function of known action features confounded by an non-linear action-independent term.
Correlated Multi-armed Bandits with a Latent Random Source
As a result, there are regimes where our algorithm achieves a $\mathcal{O}(1)$ regret as opposed to the typical logarithmic regret scaling of multi-armed bandit algorithms.
Adapting multi-armed bandits policies to contextual bandits scenarios
This work explores adaptations of successful multi-armed bandits policies to the online contextual bandits scenario with binary rewards using binary classification algorithms such as logistic regression as black-box oracles.
Bayesian Optimisation over Multiple Continuous and Categorical Inputs
Efficient optimisation of black-box problems that comprise both continuous and categorical inputs is important, yet poses significant challenges.
