It is envisioned that 6G, unlike its predecessor 5G, will depart from connected machines and
connected people to connected intelligence. The main goal of 6G networks is to support …

We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the
generator objective with respect to an unrolled optimization of the discriminator. This allows …

This monograph covers some recent advances in a range of acceleration techniques
frequently used in convex optimization. We first use quadratic optimization problems to …

This monograph presents the main complexity theorems in convex optimization and their
corresponding algorithms. Starting from the fundamental theory of black-box optimization …

We consider a family of algorithms that successively sample and minimize simple stochastic
models of the objective function. We show that under reasonable conditions on …

This paper studies first order methods for solving smooth minimax optimization problems
$\min_x\max_y g (x, y) $ where $ g (\cdot,\cdot) $ is smooth and $ g (x,\cdot) $ is concave for …

We introduce a generic scheme for accelerating first-order optimization methods in the
sense of Nesterov, which builds upon a new analysis of the accelerated proximal point …

Trust region policy optimization (TRPO) is a popular and empirically successful policy
search algorithm in Reinforcement Learning (RL) in which a surrogate problem, that restricts …

This paper considers a class of constrained stochastic composite optimization problems
whose objective function is given by the summation of a differentiable (possibly nonconvex) …

In this paper, we propose a new accelerated stochastic first-order method called clipped-
SSTM for smooth convex stochastic optimization with heavy-tailed distributed noise in …