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OPT2013: Optimization for Machine Learning
Suvrit Sra · Alekh Agarwal

Mon Dec 09 07:30 AM -- 06:30 PM (PST) @ Harrah's Tahoe C
Event URL: https://sites.google.com/site/mloptstat/opt-2013 »

Dear NIPS Workshop Chairs,

We propose to organize the workshop

OPT2013: Optimization for Machine Learning.

As the sixth in its series, OPT 2013 stands on significant precedent established by OPT 2008--OPT 2012 which were all very well-received NIPS workshops.

The previous OPT workshops enjoyed packed (to overpacked) attendance, and this enthusiastic reception underscores the strong interest, relevance, and importance enjoyed by optimization in the ML community.

This interest has grown remarkably strongly every year, no wonder, since optimization lies at the heart of most ML algorithms. Although classical textbook algorithms might sometimes suffice, the majority of ML problems require tailored methods based on a deeper understanding of learning task. Indeed, ML applications and researchers are driving some of the most cutting-edge developments in optimization today. This intimate relation of optimization with ML is the key motivation for our workshop, which aims to foster discussion, discovery, and dissemination of the state-of-the-art in optimization as relevant to machine learning.

Author Information

Suvrit Sra (MIT)

Suvrit Sra is a faculty member within the EECS department at MIT, where he is also a core faculty member of IDSS, LIDS, MIT-ML Group, as well as the statistics and data science center. His research spans topics in optimization, matrix theory, differential geometry, and probability theory, which he connects with machine learning --- a key focus of his research is on the theme "Optimization for Machine Learning” (http://opt-ml.org)

Alekh Agarwal (Microsoft Research)

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