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 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; no registration is necessary. 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, Novmeber 11th 2014, 13h00 - 14h00

João Xavier (ISR)

Distributed detection over random networks

Anfiteatro do Complexo Interdisciplinar

Instituto Superior Técnico - Alameda


A team of agents collaborate to distinguish between two states of nature. Agents receive private measurements and exchange messages with neighbors to collectively solve the detection problem. We consider the challenging scenario of communication networks with time-variant random topologies thereby embracing several models of link erasure, packet drops and gossip-like randomized protocols.

We suggest a consensus+innovations distributed detector and characterize its performance. We show that an interesting phase-transition phenomenon emerges: if the random connectivity model is fast enough for the given hypothesis test, each agent is asymptotically equivalent to a (virtual) central node that sees all network measurements instantaneously. Moreover, the threshold for the equivalence depends on the hypothesis' distributions at stake, and not only on their Chernoff distance as in classical centralized detection.

Our proofs draw from large-deviations theory and convex analysis, and introduce novel results in random matrix theory.


Bio: João Xavier is with the Electrical and Computer Engineering (ECE) Department at Instituto Superior Técnico (IST) and the Instituto de Sistemas e Robótica (ISR).
His current research focus on distributed estimation, detection and optimization for large-scale networks.