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Reimagining Digitized Newspapers with Machine Learning

Reimagining Digitized Newspapers with Machine Learning
Fri, May 15, 2020, 11:30 am

The 16 million digitized historic newspaper pages within Chronicling America, a joint initiative by the Library of Congress and the NEH, represent an incredibly rich resource for a wide range of users. Historians, journalists, genealogists, students, and members of the American public explore...

Speaker(s):
Ben Lee
Library of Congress Innovator-in-Residence
Newspaper Navigator: Reimagining Digitized Newspapers with Machine Learning

Running and Analyzing Large-scale Psychology Experiments

Running and Analyzing Large-scale Psychology Experiments
Fri, Mar 6, 2020, 12:00 pm

Psychology has traditionally been a laboratory discipline, focused on small-scale experiments conducted in person. However, recent technological innovations have made it possible to collect far more data from far more people than ever before.

Speaker(s):
Tom Griffiths
Princeton University

Preference Modeling with Context-Dependent Salient Features

Preference Modeling with Context-Dependent Salient Features
Mon, Feb 24, 2020, 4:00 pm

This talk considers the preference modeling problem and addresses the fact that pairwise comparison data often reflects irrational choice, e.g. intransitivity. Our key observation is that two items compared in isolation from other items may be compared based on only a salient subset of features.

Speaker(s):
LAURA BALZANO
UNIVERSITY OF MICHIGAN
Preference Modeling with Context-Dependent Salient Features

Massively Parallel Evolutionary Computation for Empowering Electoral Reform

Massively Parallel Evolutionary Computation for Empowering Electoral Reform
Fri, Feb 21, 2020, 12:15 pm
Important insights into redistricting can be gained by formulating and analyzing the problem with a Markov Chain Monte Carlo framework that utilizes optimization heuristics to inform transition proposals.
Speaker(s):
Wendy Tam Cho
UIUC, Politics/Computer Science

Wearable Brain-Machine Interface Architectures for Neurocognitive Stress

Wearable Brain-Machine Interface Architectures for Neurocognitive Stress
Mon, Feb 10, 2020, 4:30 pm
The human body responds to neurocognitive stress in multiple ways through its autonomic nervous system. Changes in skin conductance measurements indicate sudomotor nerve activity, and could be used in inferring the underlying autonomic nervous system stimulation. We model skin conductance measurements using a state-space model with sparse...
Speaker(s):
Rose Faghih
University of Houston

Scalable Semidefinite Programming

Scalable Semidefinite Programming
Mon, Feb 10, 2020, 4:00 pm
Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking potential for data science applications. This talk develops new provably correct algorithms for solving large SDP problems by economizing on both the storage and the arithmetic costs. We present two methods: one based on sketching, and the other on...
Speaker(s):
Madeleine Udell
Cornell University

Targeted Machine Learning for Causal Inference

Targeted Machine Learning for Causal Inference
Fri, Feb 7, 2020, 12:00 pm
We review targeted minimum loss estimation (TMLE), which provides a general template for the construction of asymptotically efficient plug-in estimators of a target estimand for infinite dimensional models.
Speaker(s):
Mark van der Laan
UC Berkeley, Biostatistics/Statistics

Recent Advances in Non-Convex Distributed Optimization and Learning

Recent Advances in Non-Convex Distributed Optimization and Learning
Mon, Nov 18, 2019, 4:30 pm
We consider a class of distributed non-convex optimization problems, in which a number of agents are connected by a communication network, and they collectively optimize a sum of (possibly non-convex and non-smooth) local objective functions. This type of problem has gained some recent popularities, especially in the application of distributed...
Speaker(s):
Mingyi Hong
University of Minnesota

Can learning theory resist deep learning?

Can learning theory resist deep learning?
Fri, Nov 15, 2019, 12:30 pm
Machine learning algorithms are ubiquitous in most scientific, industrial and personal domains, with many successful applications. As a scientific field, machine learning has always been characterized by the constant exchanges between theory and practice, with a stream of algorithms that exhibit both good empirical performance on real-world...
Speaker(s):
Francis Bach
INRIA

Convergence Rates of Stochastic Algorithms in Nonsmooth Nonconvex Optimization

Convergence Rates of Stochastic Algorithms in Nonsmooth Nonconvex Optimization
Thu, Nov 14, 2019, 4:30 pm
Stochastic iterative methods lie at the core of large-scale optimization and its modern applications to data science. Though such algorithms are routinely and successfully used in practice on highly irregular problems (e.g. deep neural networks), few performance guarantees are available outside of smooth or convex settings. In this talk, I will...
Speaker(s):
Dmitriy Drusvyatskiy
University of Washington
Convergence Rates of Stochastic Algorithms in Nonsmooth Nonconvex Optimization

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