Abstract The Extreme Learning Machine is a single-hidden-layer feedforward learning
algorithm, which has been successfully applied in regression and classification problems in …

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

We propose the first global accelerated gradient method for Riemannian manifolds. Toward
establishing our results, we revisit Nesterov's estimate sequence technique and develop a …

In this paper, we provide a unified analysis of temporal difference learning algorithms with
linear function approximators by exploiting their connections to Markov jump linear systems …

We consider the problem of analysing and designing gradient-based discrete-time
optimisation algorithms for a class of unconstrained optimisation problems having strongly …

Stochastic Approximation (SA) is a classical algorithm that has had since the early days a
huge impact on signal processing, and nowadays on machine learning, due to the necessity …

Stochastic gradient descent (SGD) algorithms, with constant momentum and its variants
such as Adam, are the optimization methods of choice for training deep neural networks …

We study the problem of minimizing a strongly convex, smooth function when we have noisy
estimates of its gradient. We propose a novel multistage accelerated algorithm that is …

We initiate a formal study of reproducibility in optimization. We define a quantitative measure
of reproducibility of optimization procedures in the face of noisy or error-prone operations …

We consider strongly-convex-strongly-concave saddle point problems assuming we have
access to unbiased stochastic estimates of the gradients. We propose a stochastic …