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March 2018

When Inference is Tractable

March 16 @ 11:00 am - 12:00 pm

David Sontag (MIT)

MIT Building E18, Room 304

Abstract: A key capability of artificial intelligence will be the ability to reason about abstract concepts and draw inferences. Where data is limited, probabilistic inference in graphical models provides a powerful framework for performing such reasoning, and can even be used as modules within deep architectures. But, when is probabilistic inference computationally tractable? I will present recent theoretical results that substantially broaden the class of provably tractable models by exploiting model stability (Lang, Sontag, Vijayaraghavan, AI Stats ’18), structure in…

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Statistical estimation under group actions: The Sample Complexity of Multi-Reference Alignment

March 9 @ 11:00 am - 12:00 pm

Afonso Bandeira (NYU)

MIT Building E18, Room 304

Abstract: Many problems in signal/image processing, and computer vision amount to estimating a signal, image, or tri-dimensional structure/scene from corrupted measurements. A particularly challenging form of measurement corruption are latent transformations of the underlying signal to be recovered. Many such transformations can be described as a group acting on the object to be recovered. Examples include the Simulatenous Localization and Mapping (SLaM) problem in Robotics and Computer Vision, where pictures of a scene are obtained from different positions andorientations; Cryo-Electron…

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One and two sided composite-composite tests in Gaussian mixture models

March 2 @ 11:00 am - 12:00 pm

Alexandra Carpentier (Otto von Guericke Universitaet)

MIT Building E18, Room 304

Abstract: Finding an efficient test for a testing problem is often linked to the problem of estimating a given function of the data. When this function is not smooth, it is necessary to approximate it cleverly in order to build good tests. In this talk, we will discuss two specific testing problems in Gaussian mixtures models. In both, the aim is to test the proportion of null means. The aforementioned link between sharp approximation rates of non-smooth objects and minimax testing…

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February 2018

Optimization’s Implicit Gift to Learning: Understanding Optimization Bias as a Key to Generalization

February 23 @ 11:00 am - 12:00 pm

Nathan Srebro-Bartom (TTI-Chicago)

MIT Building E18, Room 304

Abstract: It is becoming increasingly clear that implicit regularization afforded by the optimization algorithms play a central role in machine learning, and especially so when using large, deep, neural networks. We have a good understanding of the implicit regularization afforded by stochastic approximation algorithms, such as SGD, and as I will review, we understand and can characterize the implicit bias of different algorithms, and can design algorithms with specific biases. But in this talk I will focus on implicit biases…

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User-friendly guarantees for the Langevin Monte Carlo

February 16 @ 11:00 am - 12:00 pm

Arnak Dalalyan (ENSAE-Crest)

MIT Building E18, Room 304

Abstract: In this talk, I will revisit the recently established theoretical guarantees for the convergence of the Langevin Monte Carlo algorithm of sampling from a smooth and (strongly) log-concave density. I will discuss the existing results when the accuracy of sampling is measured in the Wasserstein distance and provide further insights on relations between, on the one hand, the Langevin Monte Carlo for sampling and, on the other hand, the gradient descent for optimization. I will also present non-asymptotic guarantees for the accuracy…

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Variable selection using presence-only data with applications to biochemistry

February 9 @ 11:00 am - 12:00 pm

Garvesh Raskutti (University of Wisconsin)

MIT Building E18, Room 304

Abstract: In a number of problems, we are presented with positive and unlabelled data, referred to as presence-only responses. The application I present today involves studying the relationship between protein sequence and function and presence-only data arises since for many experiments it is impossible to obtain a large set of negative (non-functional) sequences. Furthermore, if the number of variables is large and the goal is variable selection (as in this case), a number of statistical and computational challenges arise due…

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Connections between structured estimation and weak submodularity

February 2 @ 11:00 am - 12:00 pm

Sahand Negahban (Yale University)

MIT Building E18, Room 304

Abstract:  Many modern statistical estimation problems rely on imposing additional structure in order to reduce the statistical complexity and provide interpretability. Unfortunately, these structures often are combinatorial in nature and result in computationally challenging problems. In parallel, the combinatorial optimization community has placed significant effort in developing algorithms that can approximately solve such optimization problems in a computationally efficient manner. The focus of this talk is to expand upon ideas that arise in combinatorial optimization and connect those algorithms and…

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December 2017

Stochastics and Statistics Seminar: Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time

December 1, 2017 @ 11:00 am - 12:00 pm

Susan Murphy (Harvard)

MIT Building E18, Room 304

Abstract:  A formidable challenge in designing sequential treatments is to  determine when and in which context it is best to deliver treatments.  Consider treatment for individuals struggling with chronic health conditions.  Operationally designing the sequential treatments involves the construction of decision rules that input current context of an individual and output a recommended treatment.   That is, the treatment is adapted to the individual’s context; the context may include  current health status, current level of social support and current level of…

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November 2017

Stochastics and Statistics Seminar: Generative Models and Compressed Sensing

November 17, 2017 @ 11:00 am - 12:00 pm

Alex Dimakis (University of Texas at Austuin)

MIT Building E18, Room 304

Abstract:  The goal of compressed sensing is to estimate a vector from an under-determined system of noisy linear measurements, by making use of prior knowledge in the relevant domain. For most results in the literature, the structure is represented by sparsity in a well-chosen basis. We show how to achieve guarantees similar to standard compressed sensing but without employing sparsity at all. Instead, we assume that the unknown vectors lie near the range of a generative model, e.g. a GAN…

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Statistics, Computation and Learning with Graph Neural Networks

November 3, 2017 @ 11:00 am - 12:00 pm

Joan Bruna Estrach (NYU)

Abstract: Deep Learning, thanks mostly to Convolutional architectures, has recently transformed computer vision and speech recognition. Their ability to encode geometric stability priors, while offering enough expressive power, is at the core of their success. In such settings, geometric stability is expressed in terms of local deformations, and it is enforced thanks to localized convolutional operators that separate the estimation into scales. Many problems across applied sciences, from particle physics to recommender systems, are formulated in terms of signals defined…

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