conftrace_

contextual bandit

381 papers

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Also known as

CB

Co-occurring keywords

regret bound (1926) online learning (1777) multi-armed bandit (1098) thompson sampling (237) upper confidence bound (197) online algorithm (445) off-policy evaluation (144) linear bandit (124) reinforcement learning (4352) recommendation system (294)

Papers

Bandits with partially observable confounded data UAI 2021
Off-Policy Confidence Sequences ICML 2021
Sublinear Optimal Policy Value Estimation in Contextual Bandits AISTATS 2020
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting JMLR 2020
Stochastic Linear Contextual Bandits with Diverse Contexts AISTATS 2020
Empirical Likelihood for Contextual Bandits NIPS 2020
Information Directed Sampling for Linear Partial Monitoring COLT 2020
Differentially-Private Federated Linear Bandits NIPS 2020
Efficient Contextual Bandits with Continuous Actions NIPS 2020
Rationally Inattentive Inverse Reinforcement Learning Explains YouTube Commenting Behavior JMLR 2020
Dialog State Tracking with Reinforced Data Augmentation AAAI 2020
Diversified Interactive Recommendation with Implicit Feedback AAAI 2020
Locally Differentially Private (Contextual) Bandits Learning NIPS 2020
Latent Bandits Revisited NIPS 2020
Gaussian Gated Linear Networks NIPS 2020
Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits UAI 2020
Contextual Combinatorial Volatile Multi-armed Bandit with Adaptive Discretization AISTATS 2020
Balanced Off-Policy Evaluation in General Action Spaces AISTATS 2020
Doubly robust off-policy evaluation with shrinkage ICML 2020
Linear bandits with Stochastic Delayed Feedback ICML 2020
Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes COLT 2020
Open Problem: Model Selection for Contextual Bandits COLT 2020
Model Selection in Contextual Stochastic Bandit Problems NIPS 2020
Root-n-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank COLT 2020
Taking a hint: How to leverage loss predictors in contextual bandits? COLT 2020