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We present a new perspective on the celebrated Sinkhorn algorithm by showing that is a special case of incremental/stochastic mirror …

We revisit the local Stochastic Gradient Descent (local SGD) method and prove new convergence rates. We close the gap in the theory by …

We provide the first convergence analysis of local gradient descent for minimizing the average of smooth and convex but otherwise …

When forecasting time series with a hierarchical structure, the existing state of the art is to forecast each time series …

MISO, also known as Finito, was one of the first stochastic variance reduced methods discovered, yet its popularity is fairly low. Its …

In this paper, we consider distributed algorithms for solving the empirical risk minimization problem under the master/worker …

We consider the problem of minimizing the sum of three convex functions: i) a smooth function $f$ in the form of an expectation or a …

We consider a new extension of the extragradient method that is motivated by approximating implicit updates. Since in a recent work …

We consider distributed optimization where the objective function is spread among different devices, each sending incremental model …

Many popular distributed optimization methods for training machine learning models fit the following template: a local gradient …

Recent Posts

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My new paper (for the first time I wrote a single authored work!) is now available online, see the publication section. It turned out …

As I’ve done some research in the field of minmax optimization and deep learning, I was invited to be a reviewer for this year …

We just uploaded two papers on federated learning to arxiv. The links are above on my website (“Recent publications”).

I received free NeurIPS registration for providing high quality reviews. It is awarded to the top 400 reviewers, and some people call …

This year I am also serving as a reviewer for the AAAI conference, which will take place in February in New-York. See the official …

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