Loading Events
Find Events

Event Views Navigation

Past Events

Events List Navigation

March 2019

Optimization of random polynomials on the sphere in the full-RSB regime

March 22, 2019 @ 11:00 am - 12:00 pm

Eliran Subag (New York University)

E18-304

Abstract: The talk will focus on optimization on the high-dimensional sphere when the objective function is a linear combination of homogeneous polynomials with standard Gaussian coefficients. Such random processes are called spherical spin glasses in physics, and have been extensively studied since the 80s. I will describe certain geometric properties of spherical spin glasses unique to the full-RSB case, and explain how they can be used to design a polynomial time algorithm that finds points within small multiplicative error from…

Find out more »

*CANCELED* LIDS Seminar Series – Marija Ilic

March 19, 2019 @ 4:00 pm - 5:00 pm

Marija Ilic (MIT)

32-155

Title Talk: TBD Speaker: Marija Ilic Affiliation: MIT Abstract: TBD Bio: TBD ____________________________________ The LIDS Seminar Series features distinguished speakers who provide an overview of a research area, as well as exciting recent progress in that area. Intended for a broad audience, seminar topics span the areas of communications, computation, control, learning, networks, probability and statistics, optimization, and signal processing. 

Find out more »

Subvector Inference in Partially Identified Models with Many Moment Inequalities

March 15, 2019 @ 11:00 am - 12:00 pm

Alex Belloni (Duke University)

E18-304

Abstract: In this work we consider bootstrap-based inference methods for functions of the parameter vector in the presence of many moment inequalities where the number of moment inequalities, denoted by p, is possibly much larger than the sample size n. In particular this covers the case of subvector inference, such as the inference on a single component associated with a treatment/policy variable of interest. We consider a min-max of (centered and non-centered) Studentized statistics and study the properties of the…

Find out more »

Automatic Computation of Exact Worst-Case Performance for First-Order Methods

March 12, 2019 @ 4:00 pm - 5:00 pm

Julien Hendrickx (UCLouvain)

32-155

Title Talk: Automatic Computation of Exact Worst-Case Performance for First-Order Methods Speaker: Julien Hendrickx Affiliation: UCLouvain Abstract: Joint work with Adrien Taylor (INRIA) and Francois Glineur (UCLouvain). We show that the exact worst-case performances of a wide class of first-order convex optimization algorithms can be obtained as solutions to semi-definite programs, which provide both the performance bounds and functions on which these are reached.  Our formulation is based on a necessary and sufficient condition for smooth (strongly) convex interpolation, allowing…

Find out more »

Using Computer Vision to Study Society: Methods and Challenges

March 11, 2019 @ 4:00 pm - 5:00 pm

Timnit Gebru (Google)

32-G449 (KIva/Patel)

  Abstract: Targeted socio-economic policies require an accurate understanding of a country's demographic makeup. To that end, the United States spends more than 1 billion dollars a year gathering census data such as race, gender, education, occupation and unemployment rates. Compared to the traditional method of collecting surveys across many years which is costly and labor intensive, data-driven, machine learning driven approaches are cheaper and faster--with the potential ability to detect trends in close to real time. In this work,…

Find out more »

Univariate total variation denoising, trend filtering and multivariate Hardy-Krause variation denoising

March 8, 2019 @ 11:00 am - 12:00 pm

Aditya Guntuboyina (UC Berkley)

E18-304

Abstract: Total variation denoising (TVD) is a popular technique for nonparametric function estimation. I will first present a theoretical optimality result for univariate TVD for estimating piecewise constant functions. I will then present related results for various extensions of univariate TVD including adaptive risk bounds for higher-order TVD (also known as trend filtering) as well as a multivariate extension via the Hardy-Krause Variation which avoids the curse of dimensionality to some extent. I will also mention connections to shape restricted…

Find out more »

Univariate total variation denoising, trend filtering and multivariate Hardy-Krause variation denoising

March 8, 2019 @ 11:00 am - 12:00 pm

Aditya Guntuboyina (UC Berkley)

E18-304

Abstract: Total variation denoising (TVD) is a popular technique for nonparametric function estimation. I will first present a theoretical optimality result for univariate TVD for estimating piecewise constant functions. I will then present related results for various extensions of univariate TVD including adaptive risk bounds for higher-order TVD (also known as trend filtering) as well as a multivariate extension via the Hardy-Krause Variation which avoids the curse of dimensionality to some extent. I will also mention connections to shape restricted…

Find out more »

A Theory for Representation Learning via Contrastive Objectives

March 5, 2019 @ 4:00 pm - 5:00 pm

Sanjeev Arora (Princeton University)

32-155

Abstract: Representation learning seeks to represent complicated data (images, text etc.) using a vector embedding, which can then be used to solve complicated new classification tasks using simple methods like a linear classifier. Learning such embeddings is an important type of unsupervised learning (learning from unlabeled data) today. Several recent methods leverage pairs of "semantically similar" data points (eg sentences occuring next to each other in a text corpus). We call such methods contrastive learning (another term would be "like…

Find out more »

Women in Data Science (WiDS) – Cambridge, MA

March 4, 2019 @ 8:00 am - 5:00 pm

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. Watch WiDS Cambridge on YouTube.

Find out more »

Why Aren’t Network Statistics Accompanied By Uncertainty Statements?

March 1, 2019 @ 11:00 am - 12:00 pm

Eric Kolaczyk (Boston University)

E18-304

Abstract: Over 500K scientific articles have been published since 1999 with the word “network” in the title. And the vast majority of these report network summary statistics of one type or another. However, these numbers are rarely accompanied by any quantification of uncertainty. Yet any error inherent in the measurements underlying the construction of the network, or in the network construction procedure itself, necessarily must propagate to any summary statistics reported. Perhaps surprisingly, there is little in the way of…

Find out more »
+ Export Events

© MIT Institute for Data, Systems, and Society | 77 Massachusetts Avenue | Cambridge, MA 02139-4307 | 617-253-1764 | Design by Opus