Jacob Abernethy (University of Pennsylvania) - "Learning in an Adversarial World, with Connections to Pricing, Hedging, and Routing" - EECS Special Seminar Series


Event Speaker: 

Jacob Abernethy,

Event Location: 


Event Date/Time: 

Thursday, March 14, 2013 - 4:00pm

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. But stochastic assumptions are not always necessary: Blackwell
and Hannan in 1950s showed how to construct learning and decision
strategies that possess robust guarantees under adversarial
conditions. Within this setting the goal of the learner is generally
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 few surprising applications and connections: (a) market
making in combinatorial prediction markets, (b) routing with limited
feedback, and (c) a minimax view of option pricing, with a connection
to the classical Black-Scholes valuation 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
finished a PhD in Computer Science at UC Berkeley, advised by
Professor Peter Bartlett, and he is now the Simons Postdoctoral Fellow
at University of Pennsylvania with Professor Michael Kearns. Jake's
research focuses on the intersection between machine learning, games
and markets.