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July 2020

Joint Webinar – Become a Master in Data Science

July 16, 2020 @ 12:00 pm - 1:00 pm

Presented by UTEC / IDSS


Join us on Thursday, July 16th at 12pm UYT (11am EDT) to learn more about this blended learning Master's program offered by Uruguay's Technological University (UTEC), with the academic support of IDSS.   Registration for this webinar is strongly encouraged. To register, please email ds@datascience.edu.uy   Learn more about the educational partnership between UTEC and IDSS.

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Aporta Advanced Program in Data Science & Global Skills webinar

July 15, 2020 @ 2:30 pm - 3:30 pm

Presented by Aporta / IDSS

Learn more about Aporta's Advanced Program in Data Science & Global Skills at this webinar! IDSS supports Aporta's learners in Peru through the MITx MicroMasters Program in Statistics and Data Science​.

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May 2020

The Ethical Algorithm

May 19, 2020 @ 4:00 pm - 5:00 pm

Michael Kearns (University of Pennsylvania)


https://youtu.be/IATv0m5U5z8 Title: The Ethical Algorithm Abstract: Many recent mainstream media articles and popular books have raised alarms over anti-social algorithmic behavior, especially regarding machine learning and artificial intelligence. The concerns include leaks of sensitive personal data by predictive models, algorithmic discrimination as a side-effect of machine learning, and inscrutable decisions made by complex models. While standard and legitimate responses to these phenomena include calls for stronger and better laws and regulations, researchers in machine learning, statistics and related areas are…

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Data Science and Big Data Analytics: Making Data-Driven Decisions

May 4, 2020


Developed by 11 MIT faculty members at IDSS, this seven-week course is specially designed for data scientists, business analysts, engineers and technical managers looking to learn strategies to harness data. Offered by MIT xPRO. Course begins May 4, 2020.

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Naive Feature Selection: Sparsity in Naive Bayes

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

Alexandre d'Aspremont (ENS, CNRS)


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

How to Trap a Gradient Flow

April 24, 2020 @ 11:00 am - 12:00 pm

Sébastien Bubeck (Microsoft Research)


Abstract: In 1993, Stephen A. Vavasis proved that in any finite dimension, there exists a faster method than gradient descent to find stationary points of smooth non-convex functions. In dimension 2 he proved that 1/eps gradient queries are enough, and that 1/sqrt(eps) queries are necessary. We close this gap by providing an algorithm based on a new local-to-global phenomenon for smooth non-convex functions. Some higher dimensional results will also be discussed. I will also present an extension of the 1/sqrt(eps)…

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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)


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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[POSTPONED] The Blessings of Multiple Causes

April 13, 2020 @ 4:00 pm - 5:00 pm

David Blei (Columbia University)


*Please note: this event has been POSTPONED until Fall 2020* See MIT’s COVID-19 policies for more details.   Title: The Blessings of Multiple Causes Abstract: Causal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods require that we observe all confounders, variables that affect both the causal variables and the outcome variables. But whether we have observed all confounders is a famously untestable assumption. We describe the deconfounder, a way to do causal…

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Matrix Concentration for Products

April 10, 2020 @ 11:00 am - 12:00 pm

Jonathan Niles-Weed (NYU)


Abstract: We develop nonasymptotic concentration bounds for products of independent random matrices. Such products arise in the study of stochastic algorithms, linear dynamical systems, and random walks on groups. Our bounds exactly match those available for scalar random variables and continue the program, initiated by Ahlswede-Winter and Tropp, of extending familiar concentration bounds to the noncommutative setting. Our proof technique relies on geometric properties of the Schatten trace class. Joint work with D. Huang, J. A. Tropp, and R. Ward.…

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[POSTPONED] Guido Imbens – The Applied Econometrics Professor and Professor of Economics, Graduate School of Business, Stanford University

April 7, 2020 @ 4:00 pm - 5:00 pm


IDSS will host Prof. Guido Imbens as part of the Distinguished Speaker Seminar series. Prof. Guido Imbens’ primary field of interest is Econometrics. Research topics in which he is interested include: causality, program evaluation, identification, Bayesian methods, semi-parametric methods, instrumental variables.

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