AN EXPONENTIAL TWO-ARMED BANDIT PROBLEM WITH ONE ARM KNOWN UNDER BATCH SAMPLING

Multi-armed bandit problem with known trend

We consider a variant of the multi-armed bandit model, which we call multi-armed bandit problem with known trend, where the gambler knows the shape of the reward function of each arm but not its distribution. This new problem is motivated by different on-line problems like active learning, music and interface recommendation applications, where when an arm is sampled by the model the received re...

Woodroofe ’ S One - Armed Bandit Problem Revisited

We consider the one-armed bandit problem of Woodroofe [J. Amer. Statist. Assoc. 74 (1979) 799–806], which involves sequential sampling from two populations: one whose characteristics are known, and one which depends on an unknown parameter and incorporates a covariate. The goal is to maximize cumulative expected reward. We study this problem in a minimax setting, and develop rate-optimal police...

A two armed bandit type problem

Scalable Discrete Sampling as a Multi-Armed Bandit Problem

Drawing a sample from a discrete distribution is one of the building components for Monte Carlo methods. Like other sampling algorithms, discrete sampling also suffers from high computational burden in large-scale inference problems. We study the problem of sampling a discrete random variable with a high degree of dependency that is typical in large-scale Bayesian inference and graphical models...

The two-armed-bandit problem with time-invariant finite memory

Absfracf-This paper solves the classical two-armed-bandit problem under the finite-memory constraint descr ibed below. Given are probability densit ies p0 and p,, and two experiments A and B. It is not known which density is associated with which experiment. Thus the experimental outcome Y of experiment A is as likely to be distributed according to p0 as it is to be distributed according to p,....