Like their inhabitants, countries interact with one another:
they consult, negotiate, trade, threaten, and fight. These
interactions are seldom uncoordinated. Rather, they are connected by a fabric
of overlapping communities, such as security coalitions,
treaties, trade cartels, and military alliances. A single country can belong
to multiple communities, reflecting its many overlapping
identities, and can engage in both within- and between-community interactions, depending on the capacity in which it is acting. In this
talk, I will introduce two tensor decomposition models for modeling
interaction events of the form "country i took action a toward
country j at time t." The first model (Bayesian Poisson CP decomposition)
discovers coherent threads of events, characterized by sender
countries, receiver countries, action types, and time steps; the second
model (Bayesian Poisson Tucker decomposition) discovers latent country--community memberships, including the number of
latent communities, as well as directed community--community
interaction networks that are specific to "topics" of similar
action types. I will demonstrate that these models infer interpretable latent
structures that conform to and inform our knowledge of international
relations. Many existing models for discrete data (such as networks and
text) are special cases of these models, including infinite relational
models, stochastic block models, and latent Dirichlet allocation. As
a result, Bayesian Poisson tensor decomposition is a general framework
for analyzing and understanding discrete data sets in the social
sciences.
Hanna Wallach is a Senior Researcher at Microsoft Research New York
City and an Adjunct Associate Professor in the College of Information
and Computer Sciences at the University of Massachusetts Amherst. She
is also a member of UMass's Computational Social Science
Institute. Hanna develops machine learning methods for analyzing the
structure, content, and dynamics of social processes. Her work is
inherently interdisciplinary: she collaborates with political
scientists, sociologists, and journalists to understand how
organizations work by analyzing publicly available interaction data,
such as email networks, document collections, press releases, meeting
transcripts, and news articles. To complement this agenda, she also
studies issues of fairness, accountability, and transparency as they
relate to machine learning. Hanna's research has had broad impact in
machine learning, natural language processing, and computational
social science. In 2010, her work on infinite belief networks won the
best paper award at the Artificial Intelligence and Statistics
conference; in 2014, she was named one of Glamour magazine's "35 Women
Under 35 Who Are Changing the Tech Industry"; in 2015, she was elected
to the International Machine Learning Society's Board of Trustees; and
in 2016, she was named co-winner of the 2016 Borg Early Career
Award. She is the recipient of several National Science Foundation
grants, an Intelligence Advanced Research Projects Activity grant, and
a grant from the Office of Juvenile Justice and Delinquency
Prevention. Hanna is committed to increasing diversity and has worked
for over a decade to address the underrepresentation of women in
computing. She co-founded two projects---the first of their kind---to
increase women's involvement in free and open source software
development: Debian Women and the GNOME Women's Summer Outreach
Program. She also co-founded the annual Women in Machine Learning
Workshop, which is now in its eleventh year. Hanna holds a BA in
computer science from the University of Cambridge, an MSc in cognitive
science and machine learning from the University of Edinburgh, and a
PhD in machine learning from the University of Cambridge.
