A Cost-Sensitive Decision Tree Learning Algorithm Based on a Multi-Armed Bandit Framework

Cost-sensitive decision tree learning using a multi-armed bandit framework

Stream-based Online Active Learning in a Contextual Multi-Armed Bandit Framework

We study the stream-based online active learning in a contextual multi-armed bandit framework. In this framework, the reward depends on both the arm and the context. In a stream-based active learning setting, obtaining the ground truth of the reward is costly, and the conventional contextual multi-armed bandit algorithm fails to achieve a sublinear regret due to this cost. Hence, the algorithm ...

Asymptotically Optimal Multi-Armed Bandit Policies under a Cost Constraint

We develop asymptotically optimal policies for the multi armed bandit (MAB), problem, under a cost constraint. This model is applicable in situations where each sample (or activation) from a population (bandit) incurs a known bandit dependent cost. Successive samples from each population are iid random variables with unknown distribution. The objective is to have a feasible policy for deciding ...

MULTI–ARMED BANDIT FOR PRICING Multi–Armed Bandit for Pricing

This paper is about the study of Multi–Armed Bandit (MAB) approaches for pricing applications, where a seller needs to identify the selling price for a particular kind of item that maximizes her/his profit without knowing the buyer demand. We propose modifications to the popular Upper Confidence Bound (UCB) bandit algorithm exploiting two peculiarities of pricing applications: 1) as the selling...

Transfer Learning in Multi-Armed Bandit: A Causal Approach

We leverage causal inference tools to support a principled and more robust transfer of knowledge in reinforcement learning (RL) settings. In particular, we tackle the problem of transferring knowledge across bandit agents in settings where causal effects cannot be identified by Pearl’s do-calculus nor standard off-policy learning techniques. Our new identification strategy combines two steps – ...