Date and Time

Tuesday, April 2, 2013 - 4:30pm to 5:30pm

Location

Computer Science Small Auditorium (Room 105)

Type

CS Department Colloquium Series

Speaker

Host

Robert Schapire

Machine Learning is often viewed through the lens of statistics, where
one tries to model or fit a set of data under stochastic conditions;
for example, it is typical to assume one's observations were sampled
IID. However, dating back to results of Blackwell and Hannan from the
1950s we know how to construct learning and decision strategies that
possess robust guarantees even under adversarial conditions. Within
this setting the goal of the learner is to "minimize regret" against
any sequence of inputs. In this talk we lay out the framework, discuss
some recent results, and we finish by exploring a couple of surprising
applications and connections, including: (a) market making in
combinatorial prediction markets, (b) routing with limited feedback,
and (c) hedging derivative securities (e.g. European option contracts)
in the worst-case, with a connection to the classical Black-Scholes
option-pricing model.

Jake received his undergraduate degree in Mathematics from MIT in 2002 and a Master's degree in Computer Science from TTI-C in 2006. He recently finished a PhD in Computer Science at UC Berkeley, advised by Peter Bartlett, and he is now the Simons Postdoctoral Fellow at University of Pennsylvania. Jake has a particular focus on the intersection between machine learning, games and markets.