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 group 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 Thursday (previously on 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!

Thursday, June 6th 2019, 13h00 - 14h00

Inês Almeida (ISR)

DJAM - Distributed Jacobi Asynchronous Method for Learning Personalized Models


Instituto Superior Técnico - Alameda


With the widespread of data collection agent networks, distributed optimization and learning methods become preferable over centralized solutions. Typically, distributed machine learning problems are solved by having the network’s agents aim for a common (or consensus) model. In certain applications, however, each agent may be interested in meeting a personal goal which may differ from the consensus solution. This problem is referred to as (asynchronous) distributed learning of personalized models: Each agent reaches a compromise between agreeing with its neighbours and minimizing its personal loss function. We present a Jacobi-like distributed algorithm which converges with probability one to the centralized solution, provided the personal loss functions are strongly convex. We then evidence that our algorithm’s performance is comparable to or better than that of distributed ADMM in a number of applications. These very experiments suggest that our Jacobi method converges linearly to the centralized solution.


Bio: Inês is currently doing a PhD on distributed optimization at IST/ISR. Before that, she worked for three years as a data scientist on a number of companies; her work focused mostly on credit scoring, mobile data analytics, and model explainability. She completed her Master degree in Physics at IST in 2013.

Eventbrite - DJAM - Distributed Jacobi Asynchronous Method for Learning Personalized Models