[NY-NLP+ML] Talk Friday: John Langford: Steps Towards Efficient Parallel Learning

[Joseph Turian]

Jon Langford is a prolific researcher, with a strong theoretical bent,
but also focused on problems that are very practical.
Namely, how do we scale machine learning to a web-scale? And how do we
scale it horizontally, not just vertically?

He is one of the authors of Vowpal Wabbit, one of the fastest ML
packages (if not the fastest) freely available today.

> No sign up, you should just be able to show up and sit in.

---------- Forwarded message ----------
From: Paul Dix <pa...@pauldix.net>
Date: Mon, May 10, 2010 at 3:02 PM
Subject: [NYC-Machine-Learning] Talk Friday: John Langford: Steps
Towards Efficient Parallel Learning
To: NYC-Machine-Le...@meetup.com


Thought everyone would be interested in this from the Nycmlpeople list:

From: Claire Monteleoni
We are pleased to be hosting John Langford this Friday, May 14th, in our
CCLS-Yahoo! Distinguished Lecture Series. We hope you can make it to his
talk. Please forward to others who may be interested.
-Claire

Who: John Langford, Yahoo! Research

When: Friday May 14th, 11am, Interschool Lab, 750 CEPSR, Schapiro Building

Talk Title: Steps Towards Efficient Parallel Learning

Abstract:
Online learning approaches are often the most efficient
methods for learning on a dataset. For really big datasets however,
they are not efficient enough, simply because the bandwidth requirements
are too great. How can we effectively apply and adapt our efficient
online learning algorithms to parallel environments? I will describe
what we've learned so far for both multicore and multinode
parallelization.

Speaker Bio:

John Langford is a computer scientist, working as a senior researcher at
Yahoo! Research. He studied Physics and Computer Science at the
California Institute of Technology, earning a double bachelor's degree
in 1997, and received his Ph.D. from Carnegie Mellon University in
2002. Previously, he was affiliated with the Toyota Technological
Institute and IBM's Watson Research Center. He is also the author of
the popular Machine Learning weblog, hunch.net.




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