Machine Learning: Higher, Faster, Stronger
Ohad Shamir, Microsoft Research New England
Computer Science Small Auditorium (Room 105)
Tuesday, February 21, 2012, 4:30 PM - 5:30 PM
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 on large-scale data.
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. His work 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 scholarship.