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Doctoral Thesis
Full name
Antonio André Monteiro Manoel
Knowledge Area
Date of Defense
São Paulo, 2015
Vicente, Renato (President)
Alfonso, Nestor Felipe Caticha
Cozman, Fabio Gagliardi
Fontanari, Jose Fernando
Varella, Marcio Teixeira do Nascimento
Title in English
Statistical physics for compressed sensing and information hiding
Keywords in English
Bayesian inference
compressed sensing
Abstract in English
This thesis is divided into two parts. In the first part, we show how problems of statistical inference and combinatorial optimization may be approached within a unified framework that employs tools from fields as diverse as machine learning, statistical physics and information theory, allowing us to i) design algorithms to solve the problems, ii) analyze the performance of these algorithms both empirically and analytically, and iii) to compare the results obtained with the optimal achievable ones. In the second part, we use this framework to study two specific problems, one of inference (compressed sensing) and the other of optimization (information hiding). In both cases, we review current approaches, identify their flaws, and propose new schemes to address these flaws, building on the use of message-passing algorithms, variational inference techniques, and spin glass models from statistical physics.
Title in Portuguese
Física Estatística para Compressão e Ocultação de Dados
Keywords in Portuguese
compressed sensing
inferência Bayesiana
Abstract in Portuguese
Esta tese está dividida em duas partes. Na primeira delas, mostramos como problemas de inferência estatística e de otimização combinatória podem ser abordados sob um framework unificado que usa ferramentas de áreas tão diversas quanto o aprendizado de máquina, a física estatística e a teoria de informação, permitindo que i) projetemos algoritmos para resolver os problemas, ii) analisemos a performance destes algoritmos tanto empiricamente como analiticamente, e iii) comparemos os resultados obtidos com os limites teóricos. Na segunda parte, este framework é usado no estudo de dois problemas específicos, um de inferência (compressed sensing) e outro de otimização (ocultação de dados). Em ambos os casos, revisamos abordagens recentes, identificamos suas falhas, e propomos novos esquemas que visam corrigir estas falhas, baseando-nos sobretudo em algoritmos de troca de mensagens, técnicas de inferência variacional, e modelos de vidro de spin da física estatística.
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