Generalization bounds are one of the main tools available for explaining the performance of learning algorithms. At the same time, most bounds in the literature are loose to an extent that raises the question as to whether these bounds actually have any explanatory power in the nonasymptotic regime of actual machine learning practice. I'll report on progress towards developing bounds and techniques---both statistical and computational---aimed at closing the gap between empirical performance and theoretical understanding.

Bio: Daniel Roy is an Assistant Professor in the Department of Statistical Sciences and, by courtesy, Computer Science at the University of Toronto, and a founding faculty member of the Vector Institute for Artificial Intelligence. Daniel is a recent recipient of an Ontario Early Researcher Award and Google Faculty Research Award. Before joining U of T, Daniel held a Newton International Fellowship from the Royal Academy of Engineering and a Research Fellowship at Emmanuel College, University of Cambridge. Daniel earned his S.B., M.Eng., and Ph.D. from the Massachusetts Institute of Technology: his dissertation on probabilistic programming won an MIT EECS Sprowls Dissertation Award. Daniel's group works on foundations of machine learning and statistics.