Computer Science Colloquium
Time+Place : Tuesday 20/12/2011 14:30 room 337-8 Taub  Bld.
Speaker    : Ohad Shamir
Affiliation: Microsoft Research New England
Host       : Johann Makowsky
Title      : Machine Learning: Higher, Faster, Stronger
Abstract   :
Over the past decade, machine learning has emerged as a major and highly
influential discipline of computer science and engineering. As the scope and
variety of its applications increase, it faces novel and increasingly
challenging settings, which go beyond classical learning frameworks. In this
talk, I will present two recent works which fall under this category. The
first work introduces a new model of sequential decision making with partial
information. The model interpolates between two well-known online learning
settings ("experts" and multi-armed bandits), and trades-off between the
information obtained per round and the total number of rounds required to
reach the same performance. The second work discusses the problem of
parallelizing gradient-based learning algorithms, which is increasingly
important for web-scale applications, but is highly non-trivial as these
algorithms are inherently sequential. We show how this can be done using a
generic and simple protocol, prove its theoretical optimality, and
substantiate its performance experimentally.
Short Bio:
Ohad Shamir is a postdoctoral researcher at Microsoft Research New England.
He joined Microsoft in 2010 after receiving a Ph.D. in computer science from
the Hebrew university, advised by Prof. Naftali Tishby. His research focuses
on machine learning, with emphasis on novel algorithms which combine
practical applicability and theoretical insight. Ohad's research was
recognized by several awards, such as the Hebrew University's Schlomiuk
Ph.D. thesis prize, the COLT 2010 best paper award, and the Wolf foundation
Refreshments served from 14:15 on,
 	Lecture starts at 14:30
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