The schedule is still incomplete Program Highlights »
Fri Dec 9th 08:00 AM -- 06:30 PM @ Area 5 + 6
Nonconvex Optimization for Machine Learning: Theory and Practice
Hossein Mobahi · Anima Anandkumar · Percy S Liang · Stefanie Jegelka · Anna E Choromanska

Workshop Home Page

A large body of machine learning problems require solving nonconvex optimization. This includes deep learning, Bayesian inference, clustering, and so on. The objective functions in all these instances are highly non-convex, and it is an open question if there are provable, polynomial time algorithms for these problems under realistic assumptions.

A diverse set of approaches have been devised to solve nonconvex problems in a variety of approaches. They range from simple local search approaches such as gradient descent and alternating minimization to more involved frameworks such as simulated annealing, continuation method, convex hierarchies, Bayesian optimization, branch and bound, and so on. Moreover, for solving special class of nonconvex problems there are efficient methods such as quasi convex optimization, star convex optimization, submodular optimization, and matrix/tensor decomposition.

There has been a burst of recent research activity in all these areas. This workshop brings researchers from these vastly different domains and hopes to create a dialogue among them. In addition to the theoretical frameworks, the workshop will also feature practitioners, especially in the area of deep learning who are developing new methodologies for training large scale neural networks. The result will be a cross fertilization of ideas from diverse areas and schools of thought.

08:15 AM Opening Remarks
08:30 AM Learning To Optimize
Nando de Freitas
09:00 AM Morning Poster Spotlight
09:30 AM Morning Poster Session
10:30 AM Coffee Break
11:00 AM The moment-LP and moment-SOS approaches in optimization and some related applications
Jean B Lasserre
11:30 AM Non-convexity in the error landscape and the expressive capacity of deep neural networks
Surya Ganguli
12:00 PM Leveraging Structure in Bayesian Optimization
Ryan P Adams
12:30 PM Lunch Break
01:30 PM Submodular Optimization and Nonconvexity
Stefanie Jegelka
02:00 PM Submodular Functions: from Discrete to Continuous Domains
Francis Bach
02:30 PM Taming non-convexity via geometry
Suvrit Sra
03:00 PM Break
03:30 PM Discussion Panel
04:30 PM Afternoon Poster Spotlight
05:00 PM Afternoon Poster Session