Hi everybody,
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: AA3195 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).