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November 2017

Generative Models and Compressed Sensing

Alex Dimakis (University of Texas at Austin)

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

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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December 2017

Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time

Susan Murphy (Harvard)

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

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 adherence…

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Stochastics and Statistics Seminar

Alex Bloemendal (Broad Institute)

December 8, 2017 @ 11:00 am - 12:00 pm

Biography: Alex Bloemendal is a computational scientist at the Broad Institute of MIT and Harvard and at the Analytic and Translational Genetics Unit of Massachusetts General Hospital. As a member of Broad institute member Ben Neale’s lab, Bloemendal leads a group in developing new methods to analyze genetic data, harnessing its unprecedented scope and scale to discover the genetic causes of disease. He also co-founded and directs the Models, Inference & Algorithms initiative at the Broad, bridging computational biology, mathematical…

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February 2018

Connections between structured estimation and weak submodularity

Sahand Negahban (Yale University)

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

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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Variable selection using presence-only data with applications to biochemistry

Garvesh Raskutti (University of Wisconsin)

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

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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User-friendly guarantees for the Langevin Monte Carlo

Arnak Dalalyan (ENSAE-CREST)

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

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

Nathan Srebro-Bartom (TTI-Chicago)

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

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 of…

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March 2018

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

Alexandra Carpentier (Otto von Guericke Universitaet)

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

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

Afonso Bandeira (NYU)

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

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;…

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When Inference is tractable

David Sontag (MIT)

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

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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