Loading Events

Past Events

Events Search and Views Navigation

Event Views Navigation

March 2020

Does Revolution Work? Evidence from Nepal

Rohini Pande (Yale University)

March 3 @ 4:00 pm - 5:00 pm

The last half century has seen the adoption of democratic institutions in much of the developing world. However, the conditions under which de jure democratization leads to the representation of historically disadvantaged groups remains debated as do the implications of descriptive representation for policy inclusion. Using detailed administrative and survey data from Nepal, we examine political selection in a new democracy, the implications for policy inclusion and the role of conflict in affecting political transformation. I situate these findings in the context…

Find out more »

Women in Data Science (WiDS) – Cambridge, MA

March 6 @ 8:00 am - 5:00 pm
Microsoft NERD Center

This one-day technical conference brings together local academic leaders,  industrial professionals and students to hear about the latest data science-related research in a number of domains, to learn how leading-edge companies are leveraging data science for success, and to connect with potential mentors, collaborators, and others in the field.

Find out more »

April 2020

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

April 7 @ 8:00 am - 5:00 pm

*Please note: this event has been POSTPONED until Fall 2020* See MIT’s COVID-19 policies for more details.   About the author: 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. Guido Imbens does research in econometrics and statistics. His research focuses on developing methods for drawing causal inferences in observational studies, using matching, instrumental variables, and regression discontinuity designs. Guido Imbens is Professor…

Find out more »

Matrix Concentration for Products

Jonathan Niles-Weed (New York University)

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

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.…

Find out more »

[POSTPONED] The Blessings of Multiple Causes

David Blei (Columbia University)

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

  *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…

Find out more »

On Using Graph Distances to Estimate Euclidean and Related Distances

Ery Arias-Castro (University of California, San Diego)

April 17 @ 11:00 am - 12:00 pm

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…

Find out more »

How to Trap a Gradient Flow

Sébastien Bubeck (Microsoft Research)

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

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)…

Find out more »

May 2020

Naive Feature Selection: Sparsity in Naive Bayes

Alexandre d'Aspremont (ENS, CNRS)

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

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…

Find out more »

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

May 4

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.

Find out more »

The Ethical Algorithm

Michael Kearns (University of Pennsylvania)

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

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 also…

Find out more »
+ Export Events