OPT2014: Optimization for Machine Learning
Zaid Harchaoui · Suvrit Sra · Alekh Agarwal · Martin Jaggi · Miro Dudik · Aaditya Ramdas · Jean B Lasserre · Yoshua Bengio · Amir Beck

Fri Dec 12th 08:30 AM -- 06:30 PM @ Level 5; room 513 e, f
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Optimization lies at the heart of many machine learning algorithms and enjoys great interest in our community. Indeed, this intimate relation of optimization with ML is the key motivation for the OPT series of workshops. We aim to foster discussion, discovery, and dissemination of the state-of-the-art in optimization relevant to ML.

This year, as the seventh in its series, the workshop's special topic will be the challenges in non-convex optimization, with contributions spanning both the challenges (hardness results) and the opportunities (modeling flexibility) of non-convex optimization. Irrespective of the special topic, the workshop will again warmly welcome contributed talks and posters on all topics in optimization for machine learning.

The confirmed invited speakers for this year are:

* Amir Beck (Technion, Israel)
* Jean Bernard Lasserre (CNRS, France)
* Yoshua Bengio (University of Montreal, Canada)

Author Information

Zaid Harchaoui (University of Washington)
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” (

Alekh Agarwal (Microsoft Research)
Martin Jaggi (ETH Zurich)
Miro Dudik (Microsoft Research)
Aaditya Ramdas (Carnegie Mellon University)
Jean B Lasserre (
Yoshua Bengio (University of Montreal)

Yoshua Bengio (PhD'1991 in Computer Science, McGill University). After two post-doctoral years, one at MIT with Michael Jordan and one at AT&T Bell Laboratories with Yann LeCun, he became professor at the department of computer science and operations research at Université de Montréal. Author of two books (a third is in preparation) and more than 200 publications, he is among the most cited Canadian computer scientists and is or has been associate editor of the top journals in machine learning and neural networks. Since '2000 he holds a Canada Research Chair in Statistical Learning Algorithms, since '2006 an NSERC Chair, since '2005 his is a Senior Fellow of the Canadian Institute for Advanced Research and since 2014 he co-directs its program focused on deep learning. He is on the board of the NIPS foundation and has been program chair and general chair for NIPS. He has co-organized the Learning Workshop for 14 years and co-created the International Conference on Learning Representations. His interests are centered around a quest for AI through machine learning, and include fundamental questions on deep learning, representation learning, the geometry of generalization in high-dimensional spaces, manifold learning and biologically inspired learning algorithms.

Amir Beck (Technion - Israel Institute of Technology)

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