Detecting and repairing dirty data is one of the perennial challenges in data analytics, and
failure to do so can result in inaccurate analytics and unreliable decisions. Over the past few …

Deep neural networks excel in supervised learning tasks but are constrained by the need for
extensive labeled data. Self-supervised learning emerges as a promising alternative …

Aggregated predictors are obtained by making a set of basic predictors vote according to
some weights, that is, to some probability distribution. Randomized predictors are obtained …

Temporal difference learning (TD) is a simple iterative algorithm used to estimate the value
function corresponding to a given policy in a Markov decision process. Although TD is one of …

Prediction systems are successfully deployed in applications ranging from disease
diagnosis, to predicting credit worthiness, to image recognition. Even when the overall …

We derive upper bounds on the generalization error of a learning algorithm in terms of the
mutual information between its input and output. The bounds provide an information …

We study the use of randomized value functions to guide deep exploration in reinforcement
learning. This offers an elegant means for synthesizing statistically and computationally …

Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the
fairness of such data-driven insights remains a concern in high-stakes fields. Despite …

We provide an information-theoretic framework for studying the generalization properties of
machine learning algorithms. Our framework ties together existing approaches, including …

This paper considers the optimal adaptive allocation of measurement effort for identifying the
best among a finite set of options or designs. An experimenter sequentially chooses designs …