Gatsby Computational Neuroscience Unit


Hanna Wallach


Wednesday 7th September 2016

Time: 4.00pm


Ground Floor Seminar Room

25 Howland Street, London, W1T 4JG


Bayesian Poisson Tensor Decomposition for International Relations


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.