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Wednesday, August 22, 2018Uniform rates of the GlivenkoCantelli convergence and their use in approximating Bayesian inferencesSeminar: Probability Seminar  August 22  34 p.m.  1011 Evans Hall Eugenio Regazzini, Universita degli Studi di Pavia, Italy This talk deals with suitable quantifications in approximating a probability measure by an “empirical” random probability measure \hat p_n, depending on the first n terms of a sequence \{\xi_i\}_{i\ge1} Rerandomization and Regression AdjustmentSeminar: Neyman Seminar  August 22  45 p.m.  1011 Evans Hall Peng Ding, UC Berkeley Randomization is a basis for the statistical inference of treatment effects without assumptions on the outcome generating process. Appropriately using covariates further yields more precise estimators in randomized experiments. In his seminal work Design of Experiments, R. A. Fisher suggested blocking on discrete covariates in the design stage and conducting the analysis of covariance (ANCOVA) in... More > Tuesday, August 28, 2018Seminar 217, Risk Management: Is motor insurance ratemaking going to change with telematics and semiautonomous vehicles?Seminar: Risk Seminar  August 28  11 a.m.12:30 p.m.  1011 Evans Hall Speaker: Montserrat Guillen, University of Barcelona Consortium for Data Analytics in Risk Many automobile insurance companies offer the possibility to monitor driving habits and distance driven by means of telematics devices installed in the vehicles. This provides a novel source of data that can be analysed to calculate personalised tariffs. For instance, drivers who accumulate a lot of miles should be charged more for their insurance coverage than those who make little use of their... More > Wednesday, August 29, 2018Spectrum of random nonselfadjoint operatorsSeminar: Probability Seminar  August 29  34 p.m.  1011 Evans Hall Martin Vogel, UC Berkeley The spectrum of nonselfadjoint operators can be highly unstable even under very small perturbations. This phenomenon is referred to as "pseudospectral effect". Likelihood Ratio Test for Stochastic Block Models with Bounded DegreesSeminar: Neyman Seminar  August 29  45 p.m.  1011 Evans Hall Yang Feng, Columbia University A fundamental problem in network data analysis is to test whether a network contains statistical significant communities. We study this problem in the stochastic block model context by testing H0: ErdosRenyi model vs. H1: stochastic block model. This problem serves as the foundation for many other problems including the testingbased methods for determining the number of communities and... More > Thursday, August 30, 2018Stochastic Gradient Descent: Strong convergence guarantees  without parameter tuningSeminar: Neyman Seminar: Special Seminar  August 30  45 p.m.  60 Evans Hall Rachel Ward, UT Austin Department of Statistics, Department of Mathematics Stochastic Gradient Descent is the basic optimization algorithm behind powerful deep learning architectures which are becoming increasingly omnipresent in society. However, existing theoretical guarantees of convergence rely on knowing certain properties of the optimization problem such as maximal curvature and noise level which are not known a priori in practice. Thus, in practice, hyper... More > 

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