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Mean-field approximations for high-dimensional Bayesian Regression

Subhabrata Sen, Harvard University
E18-304

Abstract: Variational approximations provide an attractive computational alternative to MCMC-based strategies for approximating the posterior distribution in Bayesian inference. Despite their popularity in applications, supporting theoretical guarantees are limited, particularly in high-dimensional settings. In the first part of the talk, we will study bayesian inference in the context of a linear model with product priors, and derive sufficient conditions for the correctness (to leading order) of the naive mean-field approximation. To this end, we will utilize recent advances in the…

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SDSCon 2022

MIT Media Lab Multi-Purpose Room: E14-674

SDSCon 2022 is the fourth celebration of the statistics and data science community at MIT and beyond, organized by MIT’s Statistics and Data Science Center (SDSC).

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The query complexity of certification

Li-Yang Tan, Stanford University
E18-304

Abstract: We study the problem of certification: given queries to an n-variable boolean function f with certificate complexity k and an input x, output a size-k certificate for f's value on x. This abstractly models a problem of interest in explainable machine learning, where we think of f as a blackbox model that we seek to explain the predictions of. For monotone functions, classic algorithms of Valiant and Angluin accomplish this task with n queries to f. Our main result is…

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Causal Representation Learning – A Proposal

Caroline Uhler, MIT
E18-304

Abstract: The development of CRISPR-based assays and small molecule screens holds the promise of engineering precise cell state transitions to move cells from one cell type to another or from a diseased state to a healthy state. The main bottleneck is the huge space of possible perturbations/interventions, where even with the breathtaking technological advances in single-cell biology it will never be possible to experimentally perturb all combinations of thousands of genes or compounds. This important biological problem calls for a…

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Learning with Random Features and Kernels: Sharp Asymptotics and Universality Laws

Yue M. Lu, Harvard University
E18-304

Abstract:  Many new random matrix ensembles arise in learning and modern signal processing. As shown in recent studies, the spectral properties of these matrices help answer crucial questions regarding the training and generalization performance of neural networks, and the fundamental limits of high-dimensional signal recovery. As a result, there has been growing interest in precisely understanding the spectra and other asymptotic properties of these matrices. Unlike their classical counterparts, these new random matrices are often highly structured and are the…

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Beyond Fairness: Big Data, Racial Justice & Housing

MIT Media Lab Multi-Purpose Room: E14-674

Please join us for the one-day symposium Beyond Fairness: Big Data, Racial Justice & Housing on Wednesday, April 27, 2022 from 9:00-4:30pm at the MIT Media Lab (E14-674). Organized by the ICSR Housing vertical team, this one-day symposium explores the intersection of data, algorithms and AI in relation to housing insecurity, home ownership and evictions. Please see icsr-fairhousing.mit.edu for more information.

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Is quantile regression a suitable method to understand tax incentives for charitable giving? Case study from the Canton of Geneva, Switzerland

Giedre Lideikyte Huber and Marta Pittavino, University of Geneva
E18-304

Abstract: Under the current Swiss law, taxpayers can deduct charitable donations from their individual’s taxable income subject to a 20%-ceiling. This deductible ceiling was increased at the communal and cantonal level from a previous 5%-ceiling in 2009. The goal of the reform was boosting charitable giving to non-profit entities. However, the effects of this reform, and more generally of the existing Swiss system of tax deductions for charitable giving has never been empirically studied. The aim of this work is…

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Sampling rare events in Earth and planetary science

Jonathan Weare, New York University
E18-304

Abstract: This talk will cover recent work in our group developing and applying algorithms to simulate rare events in atmospheric science and other areas. I will review a rare event simulation scheme that biases model simulations toward the rare event of interest by preferentially duplicating simulations making progress toward the event and removing others. I will describe applications of this approach to rapid intensification of tropical cyclones and instability of Mercury's orbit with an emphasis on the elements of algorithm…

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