Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper
Query: Adaptive Machine Learning Winter 2018 Lecture 9 : Linear Bandits and Thompson Sampling
Analysis of Thompson Sampling for the multi-armed bandit problem
Context-Aware Bandits
Adaptive Monte Carlo via Bandit Allocation
Provable Optimal Algorithms for Generalized Linear Contextual Bandits
Thompson Sampling: An Asymptotically Optimal Finite Time Analysis
Multi-armed bandit algorithms and empirical evaluation
Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret
Algorithms for the multi-armed bandit problem
Optimistic Bayesian sampling in contextual-bandit problems
Finite-time Analysis of the Multiarmed Bandit Problem
Competing in the dark: an efficient algorithm for bandit linear optimization
Generalized Thompson Sampling for Sequential Decision-Making and Causal Inference
Algorithms for adversarial bandit problems with multiple plays
Contextual Bandits with Linear Payoff Functions
The Epoch-Greedy Algorithm for Contextual Multi-armed Bandits
Stochastic Dual Coordinate Descent with Bandit Sampling.
A Survey of Preference-Based Online Learning with Bandit Algorithms
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
Contextual Multi-Armed Bandits
Combinatorial Pure Exploration of Multi-Armed Bandits
Conceptual approaches for defining data, information, and knowledge
An Auto-Scaling Cloud Controller Using Fuzzy Q-Learning - Implementation in OpenStack
BOXES: a symbolic abstract domain of boxes
Finite-Time Analysis of Kernelised Contextual Bandits
Reducing Price Uncertainty through Demand Side Management.