[Ml-stat-talks] talk by Jake Abernethy, tue, 4/2, 4:30pm, CS105

Robert Schapire schapire at CS.Princeton.EDU
Fri Mar 29 14:18:16 EDT 2013

*Learning in an Adversarial World, with Connections to Pricing, Hedging, 
and Routing*
*Jacob Duncan Abernethy <http://www.seas.upenn.edu/%7Ejaber/>*, 
University of Pennsylvania <http://www.upenn.edu/>
Tuesday, April 2, 2013, 4:30pm
Computer Science 105

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.
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