Modern machine learning models are often trained on examples with noisy labels that hurt
performance and are hard to identify. In this paper, we provide an empirical study showing …

This paper studies robust nonparametric regression, in which an adversarial attacker can
modify the values of up to q samples from a training dataset of size N. Our initial solution is …

For a regression problem with a binary label response, we examine the problem of
constructing confidence intervals for the label probability conditional on the features. In a …

We analyze the K-armed bandit problem where the reward for each arm is a noisy
realization based on an observed context under mild nonparametric assumptions. We attain …

In the evolving landscape of digital commerce, adaptive dynamic pricing strategies are
essential for gaining a competitive edge. This paper introduces novel {\em doubly …

Compositional data arise in many real-life applications and versatile methods for properly
analyzing this type of data in the regression context are needed. When parametric …

We introduce a flexible framework that produces high-quality almost-exact matches for
causal inference. Most prior work in matching uses ad-hoc distance metrics, often leading to …

We consider classification in the presence of class-dependent asymmetric label noise with
unknown noise probabilities. In this setting, identifiability conditions are known, but …

Current guidelines for treatment decision making largely rely on data from randomized
controlled trials (RCTs) studying average treatment effects. They may be inadequate to …

Markov decision process (MDP) is an important model of sequential decision making
problems. Existing theoretical analysis focus primarily on finite state spaces. For continuous …