Séminaire ICI : Pablo Piantanida
Titre du séminaire et orateur
From Information Theory to Machine Learning and Statistics.
Pablo Piantanida, L2S / Université Paris-Sud.
Date et lieu
Jeudi 30 juin 2016, 11h.
ENSEA, salle 384.
In the first part of this talk, we investigate the problem of distributed biclustering of memoryless sources. This scenario consists of a set of distributed stationary memoryless sources where the goal is to find rate-limited representations such that the mutual information between two selected subsets of descriptions (each of them generated by distinct encoder functions) is maximized. This formulation is fundamentally different from conventional information-theoretic problems since here redundancy among descriptions should actually be maximally preserved. Furthermore, necessary and sufficient conditions for the special case of two arbitrarily correlated Rademacher random variables and Boolean encoders are derived. Interestingly, these results positively resolve long-standing open conjecture.
In the second part of the talk, we study the problem of collaborative distributed hypothesis testing. Two statisticians are required to declare the correct probability measure of two jointly distributed memoryless processes out of two possible probability measures. The marginal samples given are assumed to be available at different locations and the statisticians are allowed to exchange limited amount of data over multiple rounds of interactions. A new achievable error exponent is derived based on the use of non-asymptotic binning, improving the quality of communicated descriptions. Optimal achievable error exponents for the special cases of testing against independence and zero-rate communication (data exchanges grow sub-exponentially with n) are characterized. Application examples to binary symmetric sources are provided as well.
Joint work with Georg Pichler (TU Wien, Austria), Prof. Gerald Matz (TU Wien, Austria), Gil Katz (CentraleSupélec, France) and Prof. Merouane Debbah (Huawei Technologies Co., France)
Pablo Piantanida received both B.Sc. in Electrical Engineering and Mathematics degrees from the University of Buenos Aires (Argentina) in 2003, and the Ph.D. from Université Paris-Sud (Orsay, France) in 2007. Since October 2007 he has joined the Laboratoire des Signaux et Systèmes (L2S), at CentraleSupélec together with CNRS (UMR 8506) and Université Paris-Sud, as an Associate Professor of Network Information Theory. He is an IEEE Senior Member, and coordinator of the Information Theory and its Applications group(ITA) at L2S and General Co-Chair of the 2019 IEEE International Symposium on Information Theory (ISIT). His research interests lie broadly in information theory and its interactions with other fields, including multi-terminal information theory, Shannon theory, machine learning, statistical inference, cooperative communications, communication mechanisms for security and privacy, and distributed representation learning.