Seminars


The Priberam Machine Learning Lunch Seminars are a series of informal meetings which occur every two weeks at Instituto Superior Técnico, in Lisbon. It works as a discussion forum involving different research groups, from IST and elsewhere. Its participants are interested in areas such as (but not limited to): statistical machine learning, signal processing, pattern recognition, computer vision, natural language processing, computational biology, neural networks, control systems, reinforcement learning, or anything related (even if vaguely) with machine learning.

The seminars last for about one hour (including time for discussion and questions) and revolve around the general topic of Machine Learning. The speaker is a volunteer who decides the topic of his/her presentation. Past seminars have included presentations about state-of-the-art research, surveys and tutorials, practicing a conference talk, presenting a challenging problem and asking for help, and illustrating an interesting application of Machine Learning such as a prototype or finished product.

Presenters can have any background: undergrads, graduate students, academic researchers, company staff, etc. Anyone is welcome both to attend the seminar as well as to present it. Ocasionally we will have invited speakers. See below for a list of all seminars, including the speakers, titles and abstracts.

Note: The seminars are held at lunch-time, and include delicious free food.

Feel free to join our mailing list, where seminar topics are announced beforehand. You may also visit the mailing list webpage. Anyone can attend the seminars. If you would like to present something, please send us an email.

The seminars are usually held every other Tuesday, from 1 PM to 2 PM, at the IST campus in Alameda. This sometimes changes due to availability of the speakers, so check regularly!

Tuesday, May 22nd 2018, 13h00 - 14h00

João Xavier (ISR)

Two optimisation ideas for locating a target

PA2

Instituto Superior Técnico - Alameda

Abstract:

We want to guess the position of a target, based only on how weakly or strongly its emitted wireless signal is received at a few places. Assuming usual probabilistic properties of wireless channels, we proceed to frame this problem as a maximum likelihood (ML) estimation problem. The ML estimate, however, is deceptively hard to compute, for it lies in the bowels of a difficult nonconvex minimisation problem. With dim hopes of quickly computing the global minimiser, we honourably retreat to a realistic goal - computing a local minimiser.
We suggest two paths. The first path was opened by a sift through the toolbox of convex trickery that revealed two inequalities fitting our problem structure; combining those inequalities allows to compute a local minimiser by solving a sequence of easy convex problems. For the second path, we reformulate the nonconvex optimisation problem in such a way that it begs to be solved by a randomised algorithm that moves along by solving proximal operators of nonconvex functions. Both paths perform surprisingly well in practice, although we don’t have a clue on how to prove it theoretically - just as in deep learning.

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Bio: João Xavier is a professor at the Department of Electrical Engineering, Instituto Superior Técnico, University of Lisbon, and a researcher at the Instituto de Sistemas e Robótica (ISR), Lisbon. He delights in being continually surprised by unexpected links between statistical signal processing, probability theory and stochastic control.

Eventbrite - Two optimisation ideas for locating a target