Data Science Seminar

Le 22 juin 2017. Séminaire entièrement en anglais.
The LTCI Data Science Seminar is a joint research seminar between the DIG team and the S2A teams. It focuses on machine learning and data science topics.
June 22, 2017

The seminar takes place from 2PM to 4PM in Amphi Saphir, and consists of the two following talks:

Talk 1 - Pascal Bianchi (Télécom Paris) - Distributed optimization on graphs using operator splitting methods

You can download the slides of this talk.

Abstract: Consider a network of N agents (computing units) having private objective functions and seeking to find a minimum of the aggregate objective. The aim is to design iterative algorithms where, at a each iteration, an agent updates a local estimate of the minimizer based on the sole knowledge of its private function and the information received from its neighbors. In this talk, i will first provide an overview of standard distributed optimization methods. Then, i will explain how recent and generic results in stochastic optimization can be used in order to design asynchronous and adaptive distributed optimization algorithms.

Talk 2 - Maximilien Danisch (UPMC) - Towards real-world graph algorithmics

You can download the slides of this talk.

Abstract: Real-world graphs (a.k.a. complex networks) are ubiquitous: the web, Facebook, brain networks, protein interaction networks, etc. Studying these graphs and solving computational problems on them (say maximum clique, partitioning or dense subgraph) has applications in many fields. I will first show that the structure of some real-world graphs is special. In particular, they are not adversarial and some difficult problems (say NP-hard problems) can be solved on some huge real-world graphs (say 2G edges or more). I will then present two works along the lines of “understanding and leveraging the structure of real-world graphs in order to build better algorithms”: (i) Enumerating all k-cliques and (ii) Computing the density-friendly graph decomposition. The latter one has been published in WWW2017.