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Stochastics and Statistics Seminar Series Konstantin Tikhomirov, Georgia Institute of Technology

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Stochastics and Statistics Seminar Series Jiaoyang Huang, University of Pennsylvania

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

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Regularized modified log-Sobolev inequalities, and comparison of Markov chains

Konstantin Tikhomirov, Georgia Institute of Technology
E18-304

Abstract: In this work, we develop a comparison procedure for the Modified log-Sobolev Inequality (MLSI) constants of two reversible Markov chains on a finite state space. As an application, we provide a sharp estimate of the MLSI constant of the switch chain on the set of simple bipartite regular graphs of size n with a fixed degree d. Our…

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Efficient derivative-free Bayesian inference for large-scale inverse problems

Jiaoyang Huang, University of Pennsylvania
E18-304

Abstract: We consider Bayesian inference for large-scale inverse problems, where computational challenges arise from the need for the repeated evaluations of an expensive forward model, which is often given as a black box or is impractical to differentiate. In this talk I will propose a new derivative-free algorithm Unscented Kalman Inversion, which utilizes the ideas…

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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…

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