Difference between revisions of "OPT 2008"
Jump to navigation
Jump to search
(Event created) |
Tim Holzheim (talk | contribs) (Added page provenance(#264) and contribution type(#271)) |
||
| (One intermediate revision by one other user not shown) | |||
| Line 2: | Line 2: | ||
| Acronym = OPT 2008 | | Acronym = OPT 2008 | ||
| Title = Optimization for Machine Learning (NIPS Workshop 2008) | | Title = Optimization for Machine Learning (NIPS Workshop 2008) | ||
| − | | Type = | + | | Type = Workshop |
| Field = Machine learning | | Field = Machine learning | ||
| Homepage = opt2008.kyb.tuebingen.mpg.de/ | | Homepage = opt2008.kyb.tuebingen.mpg.de/ | ||
| Line 14: | Line 14: | ||
| Notification = Nov 7, 2008 | | Notification = Nov 7, 2008 | ||
| Camera ready = Nov 20, 2008 | | Camera ready = Nov 20, 2008 | ||
| + | |pageCreator=127.0.0.1 | ||
| + | |pageEditor=Soeren | ||
| + | |contributionType=1 | ||
}} | }} | ||
| − | + | We invite high quality submissions for presentation as talks or posters during the workshop. We are especially interested in participants who can contribute in the following areas: | |
| − | |||
| − | + | * Non-Convex Optimization example problems in ML include | |
| − | + | ** Problems with sparsity constraints | |
| − | + | ** Sparse PCA | |
| − | + | ** Non-negative matrix and tensor approximation | |
| − | + | ** Non-convex quadratic programming | |
| − | + | * Combinatorial and Discrete Optimization example problems in ML include | |
| − | + | ** Estimating MAP solutions to discrete random fields | |
| − | + | ** Clustering and graph-partitioning | |
| − | + | ** Semi-supervised and multiple-instance learning | |
| − | + | ** Feature and subspace selection | |
| − | + | * Stochastic, Parallel and Online Optimization example problems in ML include | |
| − | + | ** Massive data sets | |
| − | + | ** Distributed learning algorithms | |
| − | + | * Algorithms and Techniques especially with a focus on an underlying application | |
| − | + | ** Polyhedral combinatorics, polytopes and strong valid inequalities | |
| − | + | ** Linear and higher-order relaxations | |
| − | + | ** Semidefinite programming relaxations | |
| − | + | ** Decomposition for large-scale, message-passing and online learning | |
| − | + | ** Global and Lipschitz optimization | |
| − | + | ** Algorithms for non-smooth optimization | |
| − | + | ** Approximation Algorithms | |
| − | |||
| − | |||
| − | |||
| − | |||
The above list is not exhaustive, and we welcome submissions on highly related topics too. | The above list is not exhaustive, and we welcome submissions on highly related topics too. | ||
| − | + | * Deadline for submission of papers: 17th October 2008 | |
| − | + | * Notification of acceptance: 7th November 2008 | |
| − | + | * Final version of submission: 20th November 2008 | |
| − | + | * Workshop date: 12th or 13th December 2008 | |
| − | |||
Latest revision as of 19:00, 1 April 2022
| OPT 2008 | |
|---|---|
Optimization for Machine Learning (NIPS Workshop 2008)
| |
| Dates | Dec 12, 2008 (iCal) - Dec 13, 2008 |
| Homepage: | opt2008.kyb.tuebingen.mpg.de/ |
| Location | |
| Location: | Whistler, Canada |
| Important dates | |
| Submissions: | Oct 17, 2008 |
| Notification: | Nov 7, 2008 |
| Camera ready due: | Nov 20, 2008 |
| Table of Contents | |
We invite high quality submissions for presentation as talks or posters during the workshop. We are especially interested in participants who can contribute in the following areas:
- Non-Convex Optimization example problems in ML include
- Problems with sparsity constraints
- Sparse PCA
- Non-negative matrix and tensor approximation
- Non-convex quadratic programming
- Combinatorial and Discrete Optimization example problems in ML include
- Estimating MAP solutions to discrete random fields
- Clustering and graph-partitioning
- Semi-supervised and multiple-instance learning
- Feature and subspace selection
- Stochastic, Parallel and Online Optimization example problems in ML include
- Massive data sets
- Distributed learning algorithms
- Algorithms and Techniques especially with a focus on an underlying application
- Polyhedral combinatorics, polytopes and strong valid inequalities
- Linear and higher-order relaxations
- Semidefinite programming relaxations
- Decomposition for large-scale, message-passing and online learning
- Global and Lipschitz optimization
- Algorithms for non-smooth optimization
- Approximation Algorithms
The above list is not exhaustive, and we welcome submissions on highly related topics too.
- Deadline for submission of papers: 17th October 2008
- Notification of acceptance: 7th November 2008
- Final version of submission: 20th November 2008
- Workshop date: 12th or 13th December 2008