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

[Adams Wei Yu]

A gentle reminder that the talk will be tomorrow (Tuesday).

On Fri, Apr 28, 2017 at 6:16 PM, Adams Wei Yu <weiyu at cs.cmu.edu> wrote:

> 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.
>
>
>
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