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Past Events › Stochastics and Statistics Seminar Series

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

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November 2020

Valid hypothesis testing after hierarchical clustering

November 6, 2020 @ 11:00 am - 12:00 pm

Daniela Witten (University of Washington)

online

Abstract:  As datasets continue to grow in size, in many settings the focus of data collection has shifted away from testing pre-specified hypotheses, and towards hypothesis generation. Researchers are often interested in performing an exploratory data analysis in order to generate hypotheses, and then testing those hypotheses on the same data; I will refer to this as ‘double dipping’. Unfortunately, double dipping can lead to highly-inflated Type 1 errors. In this talk, I will consider the special case of hierarchical…

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October 2020

Statistical Aspects of Wasserstein Distributionally Robust Optimization Estimators

October 23, 2020 @ 11:00 am - 12:00 pm

Jose Blanchet (Stanford University)

online

Abstract: Wasserstein-based distributional robust optimization problems are formulated as min-max games in which a statistician chooses a parameter to minimize an expected loss against an adversary (say nature) which wishes to maximize the loss by choosing an appropriate probability model within a certain non-parametric class. Recently, these formulations have been studied in the context in which the non-parametric class chosen by nature is defined as a Wasserstein-distance neighborhood around the empirical measure. It turns out that by appropriately choosing the…

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Data driven variational models for solving inverse problems

October 16, 2020 @ 11:00 am - 12:00 pm

Carola-Bibiane Schönlieb (University of Cambridge )

online

Abstract:  In this talk we discuss the idea of data- driven regularisers for inverse imaging problems. We are in particular interested in the combination of mathematical models and purely data-driven approaches, getting the best from both worlds. In this context we will make a journey from “shallow” learning for computing optimal parameters for variational regularisation models by bilevel optimization to the investigation of different approaches that use deep neural networks for solving inverse imaging problems. Bio: Carola-Bibiane Schönlieb is Professor of…

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On Estimating the Mean of a Random Vector

October 9, 2020 @ 11:00 am - 12:00 pm

Gábor Lugosi (Pompeu Fabra University )

online

Abstract: One of the most basic problems in statistics is the estimation of the mean of a random vector, based on independent observations. This problem has received renewed attention in the last few years, both from statistical and computational points of view. In this talk we review some recent results on the statistical performance of mean estimators that allow heavy tails and adversarial contamination in the data. The basic punchline is that one can construct estimators that, under minimal conditions,…

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Bayesian inverse problems, Gaussian processes, and partial differential equations

October 2, 2020 @ 11:00 am - 12:00 pm

Richard Nickl (University of Cambridge)

online

Abstract: The Bayesian approach to inverse problems has become very popular in the last decade after seminal work by Andrew Stuart (2010) and collaborators. Particularly in non-linear applications with PDEs and when using Gaussian process priors, this can leverage powerful MCMC methodology to tackle difficult high-dimensional and non-convex inference problems. Little is known in terms of rigorous performance guarantees for such algorithms. After laying out the main ideas behind Bayesian inversion, we will discuss recent progress providing both statistical and…

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September 2020

Separating Estimation from Decision Making in Contextual Bandits

September 25, 2020 @ 11:00 am - 12:00 pm

Dylan Foster (MIT)

online

Abstract: The contextual bandit is a sequential decision making problem in which a learner repeatedly selects an action (e.g., a news article to display) in response to a context (e.g., a user’s profile) and receives a reward, but only for the action they selected. Beyond the classic explore-exploit tradeoff, a fundamental challenge in contextual bandits is to develop algorithms that can leverage flexible function approximation to model similarity between contexts, yet have computational requirements comparable to classical supervised learning tasks…

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Causal Inference and Overparameterized Autoencoders in the Light of Drug Repurposing for SARS-CoV-2

September 18, 2020 @ 11:00 am - 12:00 pm

Caroline Uhler (MIT)

online

Abstract:  Massive data collection holds the promise of a better understanding of complex phenomena and ultimately, of better decisions. An exciting opportunity in this regard stems from the growing availability of perturbation / intervention data (drugs, knockouts, overexpression, etc.) in biology. In order to obtain mechanistic insights from such data, a major challenge is the development of a framework that integrates observational and interventional data and allows predicting the effect of yet unseen interventions or transporting the effect of interventions…

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Stein’s method for multivariate continuous distributions and applications

September 11, 2020 @ 11:00 am - 12:00 pm

Gesine Reinert (University of Oxford)

online

Abstract: Stein’s method is a key method for assessing distributional distance, mainly for one-dimensional distributions. In this talk we provide a general approach to Stein’s method for multivariate continuous distributions. Among the applications we consider is the Wasserstein distance between two continuous probability distributions under the assumption of existence of a Poincare constant. This is joint work with Guillaume Mijoule (INRIA Paris) and Yvik Swan (Liege). – Bio: Gesine Reinert is a Research Professor of the Department of Statistics and…

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May 2020

Naive Feature Selection: Sparsity in Naive Bayes

May 1, 2020 @ 11:00 am - 12:00 pm

Alexandre d'Aspremont (ENS, CNRS)

online

Abstract: Due to its linear complexity, naive Bayes classification remains an attractive supervised learning method, especially in very large-scale settings. We propose a sparse version of naive Bayes, which can be used for feature selection. This leads to a combinatorial maximum-likelihood problem, for which we provide an exact solution in the case of binary data, or a bound in the multinomial case. We prove that our bound becomes tight as the marginal contribution of additional features decreases. Both binary and…

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April 2020

On Using Graph Distances to Estimate Euclidean and Related Distances

April 17, 2020 @ 11:00 am - 12:00 pm

Ery Arias-Castro (University of California, San Diego)

online

Abstract: Graph distances have proven quite useful in machine learning/statistics, particularly in the estimation of Euclidean or geodesic distances. The talk will include a partial review of the literature, and then present more recent developments on the estimation of curvature-constrained distances on a surface, as well as on the estimation of Euclidean distances based on an unweighted and noisy neighborhood graph. – About the Speaker: Ery Arias-Castro received his Ph.D. in Statistics from Stanford University in 2004. He then took…

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