Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …

It is currently known how to characterize functions that neural networks can learn with SGD
for two extremal parametrizations: neural networks in the linear regime, and neural networks …

We consider a distributed empirical risk minimization (ERM) optimization problem with
communication efficiency and privacy requirements, motivated by the federated learning …

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 …

We propose a tuning-free dynamic SGD step size formula, which we call Distance over
Gradients (DoG). The DoG step sizes depend on simple empirical quantities (distance from …

We propose and analyze algorithms for distributionally robust optimization of convex losses
with conditional value at risk (CVaR) and $\chi^ 2$ divergence uncertainty sets. We prove …

Seismic velocity is one of the most important parameters used in seismic exploration.
Accurate velocity models are the key prerequisites for reverse time migration and other high …

We propose a general yet simple theorem describing the convergence of SGD under the
arbitrary sampling paradigm. Our theorem describes the convergence of an infinite array of …

Abstract The Randomized Response (RR) algorithm is a classical technique to improve
robustness in survey aggregation, and has been widely adopted in applications with …