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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.am...@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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