Nonconvex constrained stochastic optimization has emerged in many important application
areas. Subject to general functional constraints, it minimizes the sum of an expectation …

Constrained Markov games offer a formal mathematical framework for modeling multi-agent
reinforcement learning problems where the behavior of the agents is subject to constraints …

We prove the first convergence guarantees for a subgradient method minimizing a generic
Lipschitz function over generic Lipschitz inequality constraints. No smoothness or convexity …

We consider a non-convex constrained optimization problem, where the objective function is
weakly convex and the constraint function is either convex or weakly convex. To solve this …

We consider (stochastic) subgradient methods for strongly convex but potentially nonsmooth
non-Lipschitz optimization. We provide new equivalent dual descriptions (in the style of dual …

As machine learning being used increasingly in making high-stakes decisions, an arising
challenge is to avoid unfair AI systems that lead to discriminatory decisions for protected …

As the complexity of learning tasks surges, modern machine learning encounters a new
constrained learning paradigm characterized by more intricate and data-driven function …

This dissertation investigates primal-dual optimization methods for solving nonconvex
problems in both stochastic and deterministic settings. The proposed methods address …

The recent studies and innovations in the topic of machine learning (ML) demonstrates the
capabilities of ML to analyze data, make predictions, and so on. To facilitate decisions …

In recent years, artificial intelligence (AI) and machine learning (ML) technologies have been
used in high-stakes decision making systems like lending decision, employment screening …