Tengyuan Liang Assistant Professor of Econometrics and Statistics University of Chicago, Booth School of Business
Title: On Adaptivity, Generalization, and Interpolation Motivated from Neural Networks
Abstract: In the absence of explicit regularization, neural network learning and kernel learning have the potential to fit the training data perfectly. It has been observed empirically, however, that such interpolated solutions can still generalize well on test data. We isolate a phenomenon of implicit regularization for minimum-norm...
Stability-driven deep model interpretation and provably fast MCMC sampling
Data science is transforming many traditional ways in which we approach scientific problems. While the abundance of data and algorithms generate a lot of excitement in statistical modeling, serious concerns about how to reliably and efficiently extract scientific knowledge from data and models are being raised.
In this talk, I will address particular reliability and efficiency issues that arise from my PhD study on a neuroscience project. Understanding how primates process...
Title: A modern maximum-likelihood approach for high-dimensional logistic regression
Abstract: Logistic regression is arguably the most widely used and studied non-linear model in statistics. Classical maximum-likelihood theory based statistical inference is ubiquitous in this context. This theory hinges on well-known fundamental results: (1) the maximum-likelihood-estimate (MLE) is asymptotically unbiased and normally distributed, (2) its variability can be quantified via the inverse Fisher information, and (3) the...