[Lisa_teatalk] Reminder: Talk tomorrow, 2:30pm in room AA1207

Jörg Bornschein bornj at iro.umontreal.ca
Thu Dec 17 19:03:26 EST 2015


Just a friendly reminder and a correction:

The talk is in room AA1207 , not in 3195!


This Friday we will have Simon Lacoste-Julien, who is a researcher at INRIA
in the SIERRA project team which is part of the Computer Science Department
of École Normale Supérieure in Paris give a talk about Frank-Wolfe
Optimization for Structured Machine Learning.


Looking forward to see many of you there,

  j


--
Title: Frank-Wolfe Optimization for Structured Machine Learning
Who: Simon Lacoste-Julien
Where: AA1207
When: Friday, 18th December, 2:30pm

Abstract:
The Frank-Wolfe (FW) optimization algorithm has lately re-gained
popularity thanks in particular to its ability to nicely handle the
structured constraints appearing in machine learning applications.
However, its convergence rate is known to be slow (sublinear) when the
solution lies at the boundary. In the first part of the talk, I will
present some less well-known variants of the FW algorithm for which we
proved their global linear convergence rate recently for the first
time, highlighting at the same time an interesting geometric notion of
"condition number" for the constraint set appearing in the constant.
In the second part of the talk, I will present an application of these
variants for approximate marginal inference in a Markov random field,
by optimizing the TRW variational objective over the marginal
polytope. The proposed algorithm, called "barrier FW" due to its
similarities with barrier methods in optimization, is the first
provably convergent algorithm of the TRW objective over the marginal
polytope, and gives more accurate marginals than previous methods in
our experiments. If time permits, I will also present how FW can be
used to obtain adaptive quadrature rules and be used in particular in
a particle filter to obtain better accuracy than the usual random
sampling.

This is joint work with David Sontag (NYU), Rahul Krishnan (NYU),
Martin Jaggi (ETH), Fredrik Lindsten (U of Cambridge) and Francis Bach
(INRIA).
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