Martin Jaggi Thesis

Martin Jaggi Thesis-26
During February 25-March 1, I am attending the workshop Numerical Algorithms in Nonsmooth Optimization.

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As of today, Dmitry Kovalev is visiting Moscow - he will stay there for two weeks and will give two research talks while there (one in Boris Polyak's group and another at MIPT).

Zheng Qu (The University of Hong Kong) is visiting me at KAUST this week.

My SEGA slides are here (click on the image to get the pdf file): As of today, Konstantin Mishchenko is visiting Martin Jaggi's Machine Learning and Optimization Laboratory at EPFL. Update (March 17): Konstantin is back at KAUST now.

"Stochastic three points method for unconstrained smooth minimization" - joint work with El Houcine Bergou and Eduard Gorbunov.

The city of Edinburgh has been an amazing place to live, and the School of Mathematics and the University of Edinburgh have been an excellent academic home to me.

I will miss my many colleagues and friends; I hope to be returning often for visits!

Before joining EPFL, I worked as a lab associate at Disney Research.

EPSRC Fellow in Mathematical Sciences Turing Fellow, The Alan Turing Institute School of Mathematics, University of Edinburgh 6317 James Clerk Maxwell Building Peter Guthrie Tait Road, Edinburgh, EH9 3FD e-mail: 1st dot last at ed dot ac dot uk phone: ( 44) 131 6505-049 After having spent 9.5 years at the University of Edinburgh (the last two of which I was on leave), I have decided to move on and pursue new opportunities: I will continue my academic career at KAUST.

Abstract: In this paper we present a convergence rate analysis of inexact variants of several randomized iterative methods.

Among the methods studied are: stochastic gradient descent, stochastic Newton, stochastic proximal point and stochastic subspace ascent.

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