Calibrated Fairness in Bandits

We study fairness within the stochastic, \emph{multi-armed bandit} (MAB) decision making framework. We adapt the fairness framework of "treating similar individuals similarly" to this setting. Here, an `individual' corresponds to an arm and two arms are `similar' if they have a similar quality distribution. First, we adopt a {\em smoothness constraint} that if two arms have a similar quality distribution then the probability of selecting each arm should be similar. In addition, we define the {\em fairness regret}, which corresponds to the degree to which an algorithm is not calibrated, where perfect calibration requires that the probability of selecting an arm is equal to the probability with which the arm has the best quality realization. We show that a variation on Thompson sampling satisfies smooth fairness for total variation distance, and give an $\tilde{O}((kT)^{2/3})$ bound on fairness regret. This complements prior work, which protects an on-average better arm from being less favored. We also explain how to extend our algorithm to the dueling bandit setting.Comment: To be presented at the FAT-ML'17 worksho

Efficient methods for near-optimal sequential decision making under uncertainty

This chapter discusses decision making under uncertainty. More specifically, it offers an overview of efficient Bayesian and distribution-free algorithms for making near-optimal sequential decisions under uncertainty about the environment. Due to the uncertainty, such algorithms must not only learn from their interaction with the environment but also perform as well as possible while learning is taking place. © 2010 Springer-Verlag Berlin Heidelberg

DUCT: An upper confidence bound approach to distributed constraint optimization problems

The Upper Confidence Bounds (UCB) algorithm is a well-known near-optimal strategy for the stochastic multi-armed bandit problem. Its extensions to trees, such as the Upper Confidence Tree (UCT) algorithm, have resulted in good solutions to the problem of Go. This paper introduces DUCT, a distributed algorithm inspired by UCT, for solving Distributed Constraint Optimization Problems (DCOP). Bounds on the solution quality are provided, and experiments show that, compared to existing DCOP approaches, DUCT is able to solve very large problems much more efficiently, or to find significantly higher quality solutions. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved

The Reinforcement Learning Competition 2014

Reinforcement learning is one of the most general problems in artificial intelligence. It has been used to model problems in automated experiment design, control, economics, game playing, scheduling and telecommunications. The aim of the reinforcement learning competition is to encourage the development of very general learning agents for arbitrary reinforcement learning problems and to provide a test-bed for the unbiased evaluation of algorithms

The Contribution of Three-Dimensional Power Doppler Imaging in the Preoperative Assessment of Breast Tumors: A Preliminary Report

Purpose. The aim of this study was to determine the value of 3D and 3D Power Doppler sonography in the detection of tumor malignancy in breast lesions and to find new diagnostic criteria for differential diagnosis. Methods. One hundred and twenty five women with clinically or mammographically suspicious findings were referred for 3D Power Doppler ultrasound prior to surgery. Histological diagnosis was conducted after surgery and compared with ultrasound findings. Sonographic criteria used for breast cancer diagnosis were based on a system that included morphological characteristics and criteria of the vascular pattern of a breast mass by Power Doppler imaging. Results. Seventy-two lesions were histopathologically diagnosed as benign and 53 tumors as malignant. Three-dimensional ultrasound identified 49 out of 53 histologically confirmed breast cancers resulting in a sensitivity of 92.4% and a specificity of 86.1% in diagnosing breast malignancy (PPV: 0.83, NPV:0.94). Conclusions. 3D ultrasonography is a valuable tool in identifying preoperatively the possibility of a tumor to be malignant