Early next week we will have *François Laviolette* from *Université Laval* giving a talk on *Monday March 26* at *3pm* in room *Claire-McNicoll (CM) Z240*.
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Learn how to improve the lower bound on your knowledge! Michael
*TITLE *PAC-Bayesian Analysis and Its Applications
*KEYWORDS* ML Theory, Statistics, DL Theoretic Bounds
*ABSTRACT*PAC-Bayesian analysis is a basic and very general tool for data-dependent analysis of the generalization performance of learning algorithms. By now, it has been applied in diverse areas, such as supervised learning, unsupervised learning, and reinforcement learning, leading to state-of-the-art algorithms and generalization bounds. PAC-Bayesian analysis, in a sense, takes the best out of Bayesian methods and the more frequentist approach related to PAC learning and puts it together: (1) it provides an easy way to exploit prior knowledge (like Bayesian methods); (2) it provides strict and explicit generalization guarantees (like VC theory); and (3) it is data-dependent and provides an easy and strict way of exploiting benign conditions (like Rademacher complexities). In addition, PAC-Bayesian bounds directly lead to efficient learning algorithms.
We will start with a general introduction to PAC-Bayesian analysis, which should be accessible to people with prior knowledge of machine learning. Then, we will survey multiple forms of PAC-Bayesian bounds and their numerous applications in different fields (including deep learning). Some of these applications will be explained in more details, while others will be surveyed at a high level.
*BIO* Professor François Laviolette is the founding director of the Big Data Research Centre (BDRC) of Université Laval, which regroups more than 47 researchers from five faculties working on different fundamental and applied aspects of data science. He has been a full professor in the Department of Computer Science and Software Engineering of Université Laval since 2002. Prof. Laviolette's main research involves Machine Learning and Big Data Analytics. He is a world-class leader in PAC-Bayesian theory, a branch of learning theory that gives rise to a better understanding of existing machine learning algorithms, and also makes it possible to design new ones. Professor Laviolette has also developed a strong expertise in bio-informatics. In that area, he worked on interpretable machine learning algorithms that were able to uncover valuable knowledge for new medical applications. Professor Laviolette is the lead researcher of the GRAAL laboratory of Laval University, whose mission is to conduct research on Machine Learning fundamentals and to develop innovative artificial intelligence applications. Over the past six years, Prof. Laviolette has supervised and co-supervised more than 36 graduate students members of the GRAAL.