3 accepted papers
Fair regression methods typically rely on squared error loss, making them fragile under heavy tailed noise. We propose a general framework for robust regression under demographic parity (DP) that applies to a wide class of M-estimators, including Cauchy, Huber, least absolute deviation, quantile, an…
For ensuring the safety of users by protecting the privacy, the traditional privacy-preserving bandit algorithm aiming to maximize the mean reward has been widely studied in scenarios such as online ride-hailing, advertising recommendations, and personalized healthcare. However, classical bandit le…
Stochastic gradient descent algorithm (SGD) has been popular in various fields of artificial intelligence as well as a prototype of online learning algorithms. This article proposes a novel and general framework of one-sided testing for streaming data based on SGD, which determines whether the u…