[seminarios-mlpb] Re: [seminarios-mlpb] Tuesday, December 14th, 13:00, Sala PA2: Mário Figueiredo on "Network Inference from Co-occurences"
- From: Paulo Gomes <pauloxgomes@xxxxxxxxx>
- To: seminarios-mlpb@xxxxxxxxxxxxx
- Date: Mon, 13 Dec 2010 19:10:51 +0000
Olá,
sou bolseiro do INESC-ID e gostaria de assistir à apresentação.
Saudações Académicas
Paulo Gomes
2010/12/7 Andre Martins <afm+@xxxxxxxxxx <afm%2B@xxxxxxxxxx>>
> TALK ANNOUNCEMENT:
> ***********************************************************************
>
> Priberam Machine Learning Lunch Seminar
> Speaker: Mário Figueiredo (IT)
> Venue: IST Alameda, Sala PA2 (Edifício de Pós-Graduação)
> Date: Tuesday, December 14th, 2010
> Time: 13:00
> Lunch will be provided
>
> Title: Network Inference from Co-occurences
>
> Abstract:
>
> Inferring network structures is a central problem arising in numerous
> fields of science and technology, including communication systems, biology,
> sociology, and neuroscience. Unfortunately, it is often difficult, or
> impossible, to obtain data that directly reveal the underlying network
> structure, and so one must infer a network from incomplete data. In this
> talk, we will look at the problem of inferring network structure from
> ``co-occurrence" observations.
> These observations identify which network components (e.g., switches and
> routers, in a communications network, or genes, in a gene regulatory
> network) co-occur in a path, but do not indicate the order in which they
> occur in that path. Without order information, the number of networks that
> are consistent with the data grows exponentially with the size of the
> network (i.e., the number of nodes). Yet, the basic engineering/evolutionary
> principles underlying most
> networks strongly suggest that not all networks consistent with the
> observations are equally likely. In particular, nodes that often co-occur
> are probably closer than nodes that rarely co-occur. This rationale suggests
> modeling co-occurrence observations as independent realizations of a
> Markovian random walk on the network, subjected to a random permutation to
> account for the lack of order information. Treating permutations as missing
> data, allows deriving
> an expectation-–maximization (EM) algorithm for estimating the random walk
> parameters. The model and EM algorithm significantly simplify the problem,
> but the computational complexity of the reconstruction process does grow
> exponentially in the length of each transmission path. For networks with
> long paths, the exact E-step may be computationally intractable. We thus
> propose a polynomial-time Monte Carlo EM algorithm based on importance
> sampling and derive conditions that
> ensure convergence of the algorithm with high probability. Finally, we
> report simulations and experiments with Internet measurements and inference
> of biological networks that demonstrate the promise of this approach.
>
> The work reported in this talk was done in collaboration with Prof.
> Michael Rabbat (McGill University, Canada) and Prof. Robert D. Nowak
> (University of Wisconsin, USA).
>
> --
>
> Bio: Mário A. T. Figueiredo received EE, MSc, PhD, and "Agregado"
> degrees in electrical and computer engineering, all from Instituto Superior
> Técnico (IST), Technical University of Lisbon (TULisbon), Portugal, in 1985,
> 1990, 1994, and 2004, respectively. Since 1994, he has been with the faculty
> of the Department of Electrical and Computer
> Engineering, IST. He is also area and group coordinator at Instituto de
> Telecomunicações, a private not-for-profit research institution. Mário
> Figueiredo spent sabbatical leaves at the Department of Computer Science
> and Engineering, Michigan State University, and the Department
> of Electrical and Computer Engineering, University of Wisconsin-Madison,
> in 1998 and 2005, respectively. He is a Fellow of the IEEE and of the IAPR
> (International Association for Pattern Recognition) and a member of the
> Image, Video, and Multidimensional Signal Processing Technical Committee of
> the IEEE.
>
> Mário Figueiredo's scientific interests include image processing and
> analysis, pattern recognition, statistical learning, and optimization. He
> received the 1995 Portuguese IBM Scientific Prize and the 2008
> UTL/Santander-Totta Scientific Prize. He is/was associate editor of the
> following journals: IEEE Transactions on Image Processing, IEEE Transactions
> on Pattern Analysis and Machine Intelligence (IEEE-TPAMI), IEEE Transactions
> on Mobile Computing, Pattern Recognition Letters, and Signal Processing. He
> is/was guest co-editor of special issues of the IEEE-TPAMI, the IEEE
> Transactions on Signal Processing, and the IEEE Journal of Selected Topics
> in Signal Processing. Mário Figueiredo was a co-chair of the 2001 and 2003
> Workshops on Energy Minimization Methods in Computer Vision and Pattern
> Recognition, and has served as a member of
> organizing/program/technical/scientific committees of many
> international conferences, including ICIP, ICASSP, CVPR, ICML,
> ICPR, NIPS, IGARSS, MLSP, IJCNN, and others.
>
>
>
>
>
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