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Stochastics and Statistics Seminar Series Marco Mondelli, Institute of Science and Technology Austria

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IDSS Distinguished Seminars Jianqing Fan, Princeton University

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Stochastics and Statistics Seminar Julia Palacios, Stanford University

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Stochastics and Statistics Seminar Jaouad Mourtada, ENSAE Paris

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Stochastics and Statistics Seminar Series

Inference in High Dimensions for (Mixed) Generalized Linear Models: the Linear, the Spectral and the Approximate

Marco Mondelli, Institute of Science and Technology Austria
E18-304

Abstract: In a generalized linear model (GLM), the goal is to estimate a d-dimensional signal x from an n-dimensional observation of the form f(Ax, w), where A is a design matrix and w is a noise vector. Well-known examples of GLMs include linear regression, phase retrieval, 1-bit compressed sensing, and logistic regression. We focus on the high-dimensional setting in which both the number of measurements n and the signal dimension d diverge, with their ratio tending to a fixed constant.…

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IDSS Distinguished Seminars

Structural Deep Learning in Financial Asset Pricing

Jianqing Fan, Princeton University
E18-304

Abstract: We develop new financial economics theory guided structural nonparametric methods for estimating conditional asset pricing models using deep neural networks, by employing time-varying conditional information on alphas and betas carried by firm-specific characteristics. Contrary to many applications of neural networks in economics, we can open the “black box” of machine learning predictions by incorporating financial economics theory into the learning, and provide an economic interpretation of the successful predictions obtained from neural networks,  by decomposing the neural predictors as…

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Stochastics and Statistics Seminar

Distance-based summaries and modeling of evolutionary trees

Julia Palacios, Stanford University
E18-304

Abstract:  Phylogenetic trees are mathematical objects of great importance used to model hierarchical data and evolutionary relationships with applications in many fields including evolutionary biology and genetic epidemiology. Bayesian phylogenetic inference usually explore the posterior distribution of trees via Markov Chain Monte Carlo methods, however assessing uncertainty and summarizing distributions remains challenging for these types of structures. In this talk I will first introduce a distance metric on the space of unlabeled ranked tree shapes and genealogies. I will then…

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Stochastics and Statistics Seminar

Coding convex bodies under Gaussian noise, and the Wills functional

Jaouad Mourtada, ENSAE Paris
E18-304

Abstract: We consider the problem of sequential probability assignment in the Gaussian setting, where one aims to predict (or equivalently compress) a sequence of real-valued observations almost as well as the best Gaussian distribution with mean constrained to a general domain. First, in the case of a convex constraint set K, we express the hardness of the prediction problem (the minimax regret) in terms of the intrinsic volumes of K. We then establish a comparison inequality for the minimax regret…

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