The MIT Statistics and Data Science Center hosts guest lecturers from around the world in this weekly seminar.

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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 from Kalman filter, to efficiently solve these inverse problems. First, I will explain some basics about Variational Inference under general metric tensors. In particular, under the…

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Geometric EDA for Random Objects

Paromita Dubey (University of Southern California)
E18-304

Abstract: In this talk I will propose new tools for the exploratory data analysis of data objects taking values in a general separable metric space. First, I will introduce depth profiles, where the depth profile of a point ω in the metric space refers to the distribution of the distances between ω and the data objects. I will describe how depth profiles can be harnessed to define transport ranks, which capture the centrality of each element in the metric space…

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