Although we have witnessed great success of pre-trained models in natural language
processing (NLP) and computer vision (CV), limited progress has been made for general …

Decentralized stochastic optimization methods have gained a lot of attention recently, mainly
because of their cheap per iteration cost, data locality, and their communication-efficiency. In …

Huge scale machine learning problems are nowadays tackled by distributed optimization
algorithms, ie algorithms that leverage the compute power of many devices for training. The …

Mini-batch stochastic gradient descent (SGD) is state of the art in large scale distributed
training. The scheme can reach a linear speedup with respect to the number of workers, but …

In this monograph, I introduce the basic concepts of Online Learning through a modern view
of Online Convex Optimization. Here, online learning refers to the framework of regret …

Mini-batch stochastic gradient methods (SGD) are state of the art for distributed training of
deep neural networks. Drastic increases in the mini-batch sizes have lead to key efficiency …

Many real-world analytics problems involve two significant challenges: prediction and
optimization. Because of the typically complex nature of each challenge, the standard …

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 …

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

Motivated by federated learning, we consider the hub-and-spoke model of distributed
optimization in which a central authority coordinates the computation of a solution among …