I will survey our efforts at the Computational Story Lab to measure and study a wide array of social and cultural phenomena using “lexical meters” — online, interactive instruments that use social media and other texts to quantify population dynamics of human behavior. These include happiness, public health, obesity rates, and depression. I will explain how lexical meters work and how we have used them to uncover natural language encodings of positivity biases across cultures, universal emotional arcs of stories, links between social media posts and health, measures of fame and ultra-fame, and time compression for news. I will offer some thoughts on how fully developing a post-disciplinary, collaborative science of human stories is vital in our efforts to understand the evolution, stability, and fracturing of social systems.
(Georgia Institute of Technology, 2017-09-22)
Mahoney, Michael
A major challenge for large-scale machine learning, and one that will
only increase in importance as we develop models that are more and
more domain-informed, involves going beyond high-variance first-order
optimization methods to more robust second order methods.
Here, we consider the problem of minimizing the sum of a large
number of functions over a convex constraint set, a problem that
arises in many data analysis, machine learning, and more traditional
scientific computing applications, as well as non-convex variants of
these basic methods. While this is of interest in many situations, it
has received attention recently due to challenges associated with
training so-called deep neural networks. We establish improved
bounds for algorithms that incorporate sub-sampling as a way to
improve computational efficiency, while maintaining the original
convergence properties of these algorithms. These methods exploit
recent results from Randomized Linear Algebra on approximate
matrix multiplication. Within the context of second order
optimization methods, they provide quantitative convergence results
for variants of Newton's methods, where the Hessian and/or the
gradient is uniformly or non-uniformly sub-sampled, under much
weaker assumptions than prior work.
(Georgia Institute of Technology, 2017-09-15)
Medvedev, Paul
As genome sequencing technologies continue to facilitate the generation of large datasets, developing
scalable algorithms has come to the forefront as a crucial step in analyzing these datasets. In this talk,
I will discuss several recent advances, with a focus on the problem of reconstructing a genome from a
set of reads (genome assembly). I will describe low-memory and scalable algorithms for automatic
parameter selection and de Bruijn graph compaction, recently implemented in two tools: KmerGenie
and bcalm. I will also present recent advances in the theoretical foundations of genome assemblers.