Section: Bilateral Contracts and Grants with Industry
Bilateral Contracts with Industry
Deezer , 2013-2014
Participants : Jérémie Mary, Philippe Preux, Romaric Gaudel.
A research project has started on June 2013 in collaboration with the Deezer company. The goal is to build a system which automatically recommends music to users. That goal is an extension of the bandit setting to the Collaborative Filtering problem.
Nuukik , 2013-2014
Participant : Jérémie Mary.
Nuukik is a start-up from Hub Innovation in Lille. It proposes a recommender systems for e-commerce based on matrix factorization. We worked with them specifically on the cold start problem (i.e when you have absolutely no data on a product or a customer). This led to promising result and allowed us to close the gap between bandits and matrix factorization. This work led to a patent submission in december 2013.
TBS , 2012-2013
Participants : Jérémie Mary, Philippe Preux.
A research project has started in September 2012 in collaboration with the TBS company. The goal is to understand and predict the audience of news related websites. These websites tend to present an ergodic frequentation with respect to a context. The main goal is to separate the effect of the context (big events, elections, ...) and the impact of the policies of the news websites. This work is based on data originating from major French media websites and also involves research of tendencies on the web (as Google Trends and Google Flu do). Used algorithms mix methods from time series prediction (ARIMA and MARSS models) and machine learning methods (L1 penalization, SVM).
Squoring Technologies , 2011-2014
Participants : Boris Baldassari, Philippe Preux.
Boris Baldassari has been hired by Squoring Technologies (Toulouse) as a PhD student in May 2011. He works on the use of machine learning to improve the quality of the software development process. During his first year as a PhD student, Boris investigated the existing norms and measures of quality of software development process. He also dedicated some time to gather some relevant datasets, which are made of either the sequence of source code releases over a multi-years period, or all the versions stored on an svn repository (svn or alike). Information from mailing-lists (bugs, support, ...) may also be part of these datasets. Tools in machine learning capable of dealing with this sort of data have also been investigated. Goals that may be reached in this endeavor have also been precised.
INTEL Corp. , 2013 - 2014
Participants : Philippe Preux, Michal Valko, Rémi Munos, Adrien Hoarau.
This is a research project on Algorithmic Determination of IoT Edge Analytics Requirements. We are attempting to solve the problem of how to automatically predict the system requirements for edge node analytics in the Internet of Things (IoT). We envision that a flexible extensible system of edge analytics can be created for IoT management; however, edge nodes can be very different in terms of the systems requirements around: processing capability, wireless communication, security/cryptography, guaranteed responsiveness, guaranteed quality of service and on-board memory requirements. One of the challenges of managing a heterogeneous Internet of Things is determining the systems requirements at each edge node in the network.
We suggest exploiting opportunity of being able to automatically customize large scale IoT systems that could comprise heterogeneous edge nodes and allow a flexible and scalable component and firmware SoC systems to be matched to the individual need of enterprise/ government level IoT customers. We propose using large scale sequential decision learning algorithms, particularly contextual bandit modeling to automatically determine the systems requirements for edge analytics. These algorithms have an adaptive property that allows for the addition of new nodes and the re-evaluation of existing nodes under dynamic and potentially adversarial conditions.