[Ml-stat-talks] Fwd: [talks] Colloquium Speaker Sham Kakade Monday, Dec 1, 4:30pm

Barbara Engelhardt bee at CS.Princeton.EDU
Mon Dec 1 09:24:13 EST 2014

Computer Science Colloquium Speaker
Sham Kakade, Microsoft Research, New England
Monday, December 1, 4:30pm
Computer Science 105

Some Algorithmic Challenges in Statistics

Machine learning is seeing tremendous progress in its impact on society.
Along with this progress comes an increasing role for both scalable
algorithms and theoretical foundations; the hope being that the such
progress can facilitate further breakthroughs on core AI problems. This
will talk will survey recent progress and future challenges at the
intersection of computer science and statistics, with a focus on three

How can we learn the interactions between observed variables, where there
exist certain latent (or hidden) causes which help to explain the
correlations in the observed data. Such settings where latent variable
models have seen successes include document (or topic) modeling, hidden
Markov models (say for modeling time series of acoustic signals), and
discovering communities of individuals in social networks.

The second is that of stochastic optimization. Many problems that arise in
science and engineering are those in which we only have a stochastic
approximation to the underlying problem at hand (e.g. linear regression or
other problems where our objective function is a sample average). Such
problems highlight some of the challenges we face at the interface of
computer science and statistics: should we use a highly (numerically)
accurate algorithm (with costly time and space requirements) or a crude
stochastic approximation scheme like stochastic gradient descent (which is
light on memory and simple to implement, yet has a poor convergence rate)?
Finally, I will provide a brief discussion with regards to future
challenges inspired by the impressive successes of deep learning.

A recurring theme is that algorithmic advances can provide new practical
techniques for statistical estimation.

Sham Kakade is a principal research scientist scientist at Microsoft
Research, New England. His research focus is on designing scalable and
efficient algorithms for machine learning and artificial intelligence; he
has worked (and has continued interests) in areas such as statistics,
optimization, probability theory, algorithms, economics, and neuroscience.
Previously, Dr. Kakade was an associate professor at the Department of
Statistics, Wharton, University of Pennsylvania (from 2010-2012) and was an
assistant professor at the Toyota Technological Institute at Chicago.
Before this, he did a postdoc in the Computer and Information Science
department at the University of Pennsylvania under the supervision of
Michael Kearns. Dr. Kakade completed his PhD at the Gatsby Unit where his
advisor was Peter Dayan. Before Gatsby, Dr. Kakade was an undergraduate at
Caltech where he did his BS in physics.
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