[Elsnet-list] PhD position- Learning with non-stationary data, Grenoble, France

Massih-Reza Amini Massih-Reza.Amini at imag.fr
Wed Feb 19 13:25:49 CET 2014


Our apologies if you receive multiple copies.

Learning with non-stationary data - application to collaborative 
filtering and link prediction between name entities in knowledge bases 
like freebase


Subject:
The continuous production of tremendous amount of data upsets the 
traditional view in science and information technology, particularly in 
machine learning (ML). These data evolve generally over time and, do not 
follow the fundamental hypothesis of stationarity upon which the 
learning theory is based. This is for example the case in collaborative 
filtering where the goal is to generate personalized recommendations for 
each user. Recommender systems filter out a potentially huge set of 
items, and extract a subset of N items that best matches user's needs 
with respect to other users preferences (observed) over existing items 
and who may have the same tastes than the latter. In this case, user 
preferences generally evolve over time ; as the perception of different 
items as well as their popularity are completely time dependent.

Learning in a non stationary environment, or learning concept drift, has 
found much attention in the ML community in recent years. Though 
learning algorithms in such environnements have been formerly proposed, 
they were studied by making restrictive assumptions like, the partial 
availability of old data being generated with the past probability 
distribution, the impossibility of having new classes; and they have not 
been tested on non stationary applications.

The thesis aims at studying a new framework for this kind of learning 
and developing algorithms able to learn from large volumes of 
non-stationary data that come from real-life applications. We are 
particularly interested in learning problems such as collaborative 
filtering and link prediction in knowledge bases. Other related works, 
like zero-shot learning and transfer learning, are under investigation 
and the successful candidate will come to interact with other PhD and 
post-doc students working on these subjects.


Profile:
For this position, we are looking for highly motivated people, with a 
passion to work in machine learning and the skills to develop algorithms 
for prediction in real-life applications. We are looking for an 
inquisitive mind with the curiosity to use a new and challenging 
technology that requires a rethinking visual processing to achieve a 
high payoff in terms of speed and efficiency. The applicant must have a 
Master of Science in Computer Science, Statistics, or related fields, 
possibly with background in reinforcement learning, bandits, or 
optimization. The working language in the lab is English, a good written 
and oral communication skills are required.

Application:
The application should include a brief description of research interests 
and past experience, a CV, degrees and grades, a copy of Master thesis 
(or a draft thereof), motivation letter (short but pertinent to this 
call), relevant publications, and other relevant documents. Candidates 
are encouraged to provide letter(s) of recommendation and contact 
information to reference persons. Please send your application in one 
single pdf to Massih-Reza.Amini at imag.fr.

Duration: 3 years (a full time position)
Starting date: September, 2014
Supervisors: Massih-Reza Amini (AMA, LIG) & Zaid Harchaoui (LEAR, INRIA)

Working Environment:
The PhD candidate will work at AMA team (http://ama.liglab.fr/) of the 
LIG lab and LEAR team (http://lear.inrialpes.fr/) of INRIA Rhone-Alpes 
at Grenoble. LIG (http://www.liglab.fr) and INRIA Rhône Alpes 
(http://www.inria.fr/) are leading institutions in Computer Science in 
France. Grenoble is the capital of the Alps in France, with excellent 
train connection to Geneva (2h), Paris (3h) and Turin (4h). AMA team is 
a dynamic group working in Machine Learning and connected scientific 
domains over 20 researchers (including PhD students) and that covers 
several aspects of machine learning from theory to applications, 
including statistical learning, data-mining, and cognitive science. LEAR 
team is a well-known computer science laboratory which main focus is 
learning based approaches to visual object recognition and scene 
interpretation, particularly for object category detection, image 
retrieval, video indexing and the analysis of humans and their movements.

Benefits:

Duration: 36 months – starting date of the contract : October 2014, 15th
Salary after taxes: around 1597,11€,
Possibility of French courses
Help for housing
Participation for public transport
Scientific Resident card and help for husband/wife visa


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