Optimization for Machine Learning

This workshop builds on precedent established the previous OPT workshops:

(@NIPS*08): http://opt2008.kyb.tuebingen.mpg.de/
(@NIPS*09): http://opt.kyb.tuebingen.mpg.de/opt09/
(@NIPS*10): http://opt.kyb.tuebingen.mpg.de/opt10/

Both these workshops had packed (often overpacked) attendance almost throughout the day. This enthusiastic reception reflects the strong interest, relevance, and importance enjoyed by optimization in the greater ML community.

One could ask why does optimization attract such continued interest? The answer is simple but telling: optimization lies at the heart of almost every ML algorithm. For some algorithms textbook methods suffice, but the majority requires tailoring algorithmic tools from optimization; moreover, this tailoring depends on a deeper understanding of the ML requirements. In fact, ML applications and researchers are driving some of the most cutting-edge developments in optimization today. The 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, especially in the context of ML.

Workshop homepage: http://opt.kyb.tuebingen.mpg.de/index.html

Event sections

Home Cosmology meets Machine Learning From Statistical Genetics to Predictive Models in Personalized Medicine New Frontiers in Model Order Selection

Optimization for Machine Learning

Relations between machine learning problems – an approach to unify the field Bayesian Nonparametric Methods: Hope or Hype? Choice Models and Preference Learning Discrete Optimization in Machine Learning Machine Learning meets Computational Photography


Invited Talks
[syn] 50521 views, 1:09:15 Invited Talk Alternating Direction Method of MultipliersAlternating Direction Method of Multipliers Stephen P. Boyd Stephen P. Boyd 1 comment	[syn] 7775 views, 51:54 Invited Talk Lock-Free Approaches to Parallelizing Stochastic Gradient DescentLock-Free Approaches to Parallelizing Stochastic Gradient Descent Benjamin Recht Benjamin Recht
Lectures
[syn] 3970 views, 23:06 Stochastic optimization with non-i.id. noiseStochastic optimization with non-i.id. noise Alekh Agarwal Alekh Agarwal	[syn] 4413 views, 19:44 Steppest descent analysis for unregularized linear prediction with strictly convex penaltiesSteppest descent analysis for unregularized linear prediction with strictly convex ... Matus Telgarsky Matus Telgarsky	[syn] 4231 views, 19:55 Making Gradient Descent Optimal for Strongly Convex Stochastic OptimizationMaking Gradient Descent Optimal for Strongly Convex Stochastic Optimization Ohad Samir Ohad Samir	[syn] 5012 views, 20:52 Fast first-order methods for convex optimization with line searchFast first-order methods for convex optimization with line search Katya Scheinberg Katya Scheinberg	[syn] 4510 views, 24:24 Path coding penalties for directed acyclic graphsPath coding penalties for directed acyclic graphs Julien Mairal Julien Mairal