NIPS Workshop on Optimization for Machine Learning, Whistler 2008

NIPS Workshop on Optimization for Machine Learning, Whistler 2008

7 Lectures · Dec 12, 2008

About

Classical optimization techniques have found widespread use in machine learning. Convex optimization has occupied the center-stage and significant effort continues to be still devoted to it. New problems constantly emerge in machine learning, e.g., structured learning and semi-supervised learning, while at the same time fundamental problems such as clustering and classification continue to be better understood. Moreover, machine learning is now very important for real-world problems with massive datasets, streaming inputs, the need for distributed computation, and complex models.

These challenging characteristics of modern problems and datasets indicate that we must go beyond the ""traditional optimization"" approaches common in machine learning. What is needed is optimization ""tuned"" for machine learning tasks. For example, techniques such as non-convex optimization (for semi-supervised learning, sparsity constraints), combinatorial optimization and relaxations (structured learning), stochastic optimization (massive datasets), decomposition techniques (parallel and distributed computation), and online learning (streaming inputs) are relevant in this setting. These techniques naturally draw inspiration from other fields, such as operations research, polyhedral combinatorics, theoretical computer science, and the optimization community.

More information about workshop - http://opt2008.kyb.tuebingen.mpg.de/

Related categories

Uploaded videos:

video-img
52:19

Optimization in Machine Learning: Recent Developments and Current Challenges

Stephen J. Wright

Dec 20, 2008

 · 

8687 Views

Lecture
video-img
19:33

Online and Batch Learning Using Forward-Looking Subgradients

Dec 20, 2008

 · 

4651 Views

Lecture
video-img
21:44

Robustness and Regularization of Support Vector Machines

Huan Xu

Dec 20, 2008

 · 

4564 Views

Lecture
video-img
21:01

Training a Binary Classifier with the Quantum Adiabatic Algorithm

Hartmut Neven

Dec 20, 2008

 · 

18743 Views

Lecture
video-img
48:14

Polyhedral Approximations in Convex Optimization

Dimitri Bertsekas

Dec 20, 2008

 · 

13114 Views

Lecture
video-img
53:46

Large-scale Machine Learning and Stochastic Algorithms

Léon Bottou

Dec 20, 2008

 · 

6615 Views

Lecture
video-img
19:39

Tuning Optimizers for Time-Constrained Problems using Reinforcement Learning.

Paul Ruvolo

Dec 20, 2008

 · 

3158 Views

Lecture