Optimization for Machine Learning

Optimization for Machine Learning

7 Lectures · Dec 16, 2011

About

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

Related categories

Uploaded videos:

Invited Talks

video-img
01:09:15

Alternating Direction Method of Multipliers

Stephen P. Boyd

Jan 25, 2012

 · 

50839 Views

Invited Talk
video-img
51:54

Lock-Free Approaches to Parallelizing Stochastic Gradient Descent

Benjamin Recht

Jan 25, 2012

 · 

7799 Views

Invited Talk

Lectures

video-img
23:06

Stochastic optimization with non-i.id. noise

Alekh Agarwal

Jan 25, 2012

 · 

3978 Views

Lecture
video-img
19:44

Steppest descent analysis for unregularized linear prediction with strictly conv...

Matus Telgarsky

Jan 25, 2012

 · 

4422 Views

Lecture
video-img
19:55

Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization

Ohad Samir

Jan 25, 2012

 · 

4258 Views

Lecture
video-img
20:52

Fast first-order methods for convex optimization with line search

Katya Scheinberg

Jan 25, 2012

 · 

5018 Views

Lecture
video-img
24:24

Path coding penalties for directed acyclic graphs

Julien Mairal

Jan 25, 2012

 · 

4530 Views

Lecture