Optimization for Machine Learning

Optimization for Machine Learning

7 Lectures · Dec 10, 2010

About

Our workshop focuses on optimization theory and practice that is relevant to machine learning. This proposal builds on precedent established by two of our previously well-received NIPS workshops: (@NIPS08) http://opt2008.kyb.tuebingen.mpg.de/ (@NIPS09) http://opt.kyb.tuebingen.mpg.de/

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 require tailoring algorithmic tools from optimization, which in turn depends on a deeper understanding of the ML requirements. In fact, ML applications and researchers are driving some of the most cuttingedge 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. The workshop should realize its aims by: *Providing a platform for increasing the interaction between researchers from optimization, operations research, statistics, scientific computing, and machine learning; *Identifying key problems and challenges that lie at the intersection of optimization and ML; *Narrowing the gap between optimization and ML, to help reduce rediscovery, and thereby accelerating new advances.

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

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Uploaded videos:

Invited Talks

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53:13

Limited-memory quasi-Newton and Hessianfree Newton methods for non-smooth optimi...

Mark Schmidt

Jan 13, 2011

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7909 Views

Invited Talk
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53:22

Efficiency of Quasi-Newton Methods on Strictly Positive Functions

Yurii Nesterov

Jan 13, 2011

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4699 Views

Invited Talk

Lectures

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27:33

Information-theoretic lower bounds on the oracle complexity of sparse convex opt...

Alekh Agarwal

Jan 13, 2011

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4031 Views

Lecture
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24:57

Hierarchical Classification via Orthogonal Transfer

Lin Xiao

Jan 13, 2011

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3855 Views

Lecture
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18:14

An Optimization Based Framework for Dynamic Batch Mode Active Learning

Shayok Chakraborty

Jan 13, 2011

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3551 Views

Lecture
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17:52

Augmenting Dual Decomposition for MAP Inference

André F. T. Martins

Jan 13, 2011

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4131 Views

Lecture
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25:02

An Incremental Subgradient Algorithm for Approximate MAP Estimation in Graphical...

Jeremy Jancsary

Jan 13, 2011

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3609 Views

Lecture