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February 2018

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
E18-304

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 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
E18-304

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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Stochastics and Statistics Seminar

Afonso Bandeira (NYU)

March 9 @ 11:00 am - 12:00 pm
E18-304

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

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Stochastics and Statistics Seminar

David Sontag (MIT)

March 16 @ 11:00 am - 12:00 pm
E18-304

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

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Statistical theory for deep neural networks with ReLU activation function

Johannes Schmidt-Hieber (Leiden)

March 23 @ 11:00 am - 12:00 pm
E18-304

Abstract: The universal approximation theorem states that neural networks are capable of approximating any continuous function up to a small error that depends on the size of the network. The expressive power of a network does, however, not guarantee that deep networks perform well on data. For that, control of the statistical estimation risk is needed. In the talk, we derive statistical theory for fitting deep neural networks to data generated from the multivariate nonparametric regression model. It is shown…

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April 2018

Stochastics and Statistics Seminar

Jianqing Fan (Princeton University)

April 6 @ 11:00 am - 12:00 pm
E18-304

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

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Stochastics and Statistics Seminar

Subhabrata Sen (Microsoft)

April 13 @ 11:00 am - 12:00 pm
E18-304

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

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Stochastics and Statistics Seminar

Genevera Allen (Rice)

April 27 @ 11:00 am - 12:00 pm
E18-304

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

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May 2018

Stochastics and Statistics Seminar

Ohad Shamir (Weizman)

May 4 @ 11:00 am - 12:00 pm
E18-304

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

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Stochastics and Statistics Seminar

Adel Javanmard (USC)

May 11 @ 11:00 am - 12:00 pm
E18-304

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

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