Quantum computers are expected to surpass the computational capabilities of classical
computers and have a transformative impact on numerous industry sectors. We present a …

Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

We consider the problem of jointly estimating expectation values of many Pauli observables,
a crucial subroutine in variational quantum algorithms. Starting with randomized …

Early detection is critical for effective management of Alzheimer's disease (AD) and
screening for mild cognitive impairment (MCI) is common practice. Among several deep …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

The Densest Subgraph Problem requires us to find, in a given graph, a subset of vertices
whose induced subgraph maximizes a measure of density. The problem has received a …

The problem of privacy-preserving data analysis has a long history spanning multiple
disciplines. As electronic data about individuals becomes increasingly detailed, and as …

This monograph portrays optimization as a process. In many practical applications the
environment is so complex that it is infeasible to lay out a comprehensive theoretical model …

Owing to their connection with generative adversarial networks (GANs), saddle-point
problems have recently attracted considerable interest in machine learning and beyond. By …

In this work, we aim to design a data marketplace; a robust real-time matching mechanism to
efficiently buy and sell training data for Machine Learning tasks. While the monetization of …