[AI Seminar] AI Lunch -- Travis Dick -- May 2

[Adams Wei Yu]

Dear faculty and students,

We look forward to seeing you Next Tuesday, May 2, at noon in NSH 3305 for
AI lunch. To learn more about the seminar and lunch, please visit the AI
Lunch webpage.

On Tuesday, Travis Dick <http://www.cs.cmu.edu/~tdick/> will give a talk :

Title: Data Driven Resource Allocation for Distributed Learning

Abstract:
The goal of distributed machine learning is to build useful models from
more data than can be processed by a single machine. In this talk I will
present a new data-dependent approach for partitioning large datasets onto
multiple machines motivated by the fact that similar data points often
belong to the same or similar classes, and more generally, classification
rules of high accuracy tend to be "locally simple but globally complex"
(Vapnik and Bottou, 1993). We present an in-depth analysis of our approach,
provide new algorithms with provable worst-case guarantees, analysis
proving existing scalable heuristics perform well in natural non worst-case
conditions, and techniques for extending the partitioning of a small sample
to the entire dataset. We overcome novel technical challenges to satisfy
important conditions for accurate distributed learning, including fault
tolerance and balancedness. We empirically compare our approach with
baselines based on random partitioning, balanced partition trees, and
locality sensitive hashing, showing that we achieve significantly higher
accuracy on both synthetic and real world image and advertising datasets.
We also demonstrate that our technique strongly scales with the available
computing power.

This is joint work with Mu Li, Krishna Pillutla, Colin White, Nina Balcan,
and Alex Smola. In Partial Fulfillment of the Speaking Requirement.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/ai-seminar-announce/attachments/20170428/199e2441/attachment.html>