Difference between revisions of "NIPS-WEHYS 2008"
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| Acronym = NIPS-WEHYS 2008 | | Acronym = NIPS-WEHYS 2008 | ||
| Title = NIPS Workshop on Learning over Empirical Hypothesis Spaces | | Title = NIPS Workshop on Learning over Empirical Hypothesis Spaces | ||
| − | | Type = | + | | Type = Workshop |
| Field = Machine learning | | Field = Machine learning | ||
| Homepage = www.kuleuven.be/wehys/ | | Homepage = www.kuleuven.be/wehys/ | ||
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| − | + | We solicit discussions and insights (controversial or otherwise) into any of the following topics: | |
| − | |||
| − | + | * Relations between the luckiness framework, compatibility functions and empirically defined regularization strategies in general. | |
| − | + | * Luckiness and compatibility can be seen as defining a prior in terms of the (unknown but fixed) distribution generating the data. To what extent can this approach be generalised while still ensuring effective learning? | |
| − | + | * Models of prior knowledge that capture both complexity and distribution dependence for powerful learning. | |
| − | + | * Theoretical analysis of the use of additional (empirical) side information in the form of unlabeled data or data labeled by related problems | |
| − | + | * Examples of proper or natural luckiness or compatibility functions in practical learning tasks. How could, for example, luckiness be defined in the context of collaborative filtering? | |
| − | + | * The effect of (empirical) preprocessing of the data not involving the labels as for example in PCA, other data-dependent transformations or cleaning, as well as using label information as for example in PLS or in feature selection and construction based on the training sample. | |
| − | + | * Empirically defined theoretical measures such as Rademacher complexity or sparsity coefficients and their relevance for analyzing empirical hypothesis spaces. | |
This workshop is intended for researchers interested in the theoretical underpinnings of learning algorithms which do not comply to the standard learning theoretical assumptions. | This workshop is intended for researchers interested in the theoretical underpinnings of learning algorithms which do not comply to the standard learning theoretical assumptions. | ||
| − | Workshop Chairs | + | ==Workshop Chairs== |
| − | + | * Maria-Florina Balcan | |
| − | + | * Shai Ben-David | |
| − | + | * Avrim Blum | |
| − | + | * Kristiaan Pelckmans | |
| − | + | * John Shawe-Taylor | |
Submissions - should be in the form of a 2-page abstract (i) summarizing a formal result, (ii) a discussion of its relevance to the workshop and (iii) pointers to the relevant literature. The abstract can be supported by an additional paper (either published or technical report), that contain detailed proofs of any assertions. We especially encourage contributions which describe how to bring in results from other formal frameworks. | Submissions - should be in the form of a 2-page abstract (i) summarizing a formal result, (ii) a discussion of its relevance to the workshop and (iii) pointers to the relevant literature. The abstract can be supported by an additional paper (either published or technical report), that contain detailed proofs of any assertions. We especially encourage contributions which describe how to bring in results from other formal frameworks. | ||
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Program - One day divided in to four sessions, two morning, two afternoon with coffee between. Each session would have one invited contributor talking for 45 mins followed by 15 mins discussion, except the first where there would be two 45 min tutorial presentations. The sessions would each have an additional part: | Program - One day divided in to four sessions, two morning, two afternoon with coffee between. Each session would have one invited contributor talking for 45 mins followed by 15 mins discussion, except the first where there would be two 45 min tutorial presentations. The sessions would each have an additional part: | ||
| − | + | * Session 1 Tutorials by S. Ben-David and A. Blum; | |
| − | + | * Session 2 Invited talk plus two contributed 15 mins presentations (posters to be shown in the afternoon); | |
| − | + | * Session 3 Invited talk plus spotlight (2min) presentations for posters with poster session following during coffee break; | |
| − | + | * Session 4 Invited talk followed by discussion aimed at identifying 10 key open questions. | |
Both John Langford and Csaba Szepesvari will present their take on the problem. The final program will be announced soon. | Both John Langford and Csaba Szepesvari will present their take on the problem. The final program will be announced soon. | ||
| − | |||
| − | |||
Latest revision as of 20:46, 14 October 2008
| NIPS-WEHYS 2008 | |
|---|---|
NIPS Workshop on Learning over Empirical Hypothesis Spaces
| |
| Dates | Dec 13, 2008 (iCal) - Dec 13, 2008 |
| Homepage: | www.kuleuven.be/wehys/ |
| Location | |
| Location: | Vancouver, Canada |
Loading map... | |
| Important dates | |
| Submissions: | Oct 31, 2008 |
| Notification: | Nov 7, 2008 |
| Table of Contents | |
Contents | |
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The following coordinate was not recognized: Geocoding failed.
We solicit discussions and insights (controversial or otherwise) into any of the following topics:
- Relations between the luckiness framework, compatibility functions and empirically defined regularization strategies in general.
- Luckiness and compatibility can be seen as defining a prior in terms of the (unknown but fixed) distribution generating the data. To what extent can this approach be generalised while still ensuring effective learning?
- Models of prior knowledge that capture both complexity and distribution dependence for powerful learning.
- Theoretical analysis of the use of additional (empirical) side information in the form of unlabeled data or data labeled by related problems
- Examples of proper or natural luckiness or compatibility functions in practical learning tasks. How could, for example, luckiness be defined in the context of collaborative filtering?
- The effect of (empirical) preprocessing of the data not involving the labels as for example in PCA, other data-dependent transformations or cleaning, as well as using label information as for example in PLS or in feature selection and construction based on the training sample.
- Empirically defined theoretical measures such as Rademacher complexity or sparsity coefficients and their relevance for analyzing empirical hypothesis spaces.
This workshop is intended for researchers interested in the theoretical underpinnings of learning algorithms which do not comply to the standard learning theoretical assumptions.
Workshop Chairs
- Maria-Florina Balcan
- Shai Ben-David
- Avrim Blum
- Kristiaan Pelckmans
- John Shawe-Taylor
Submissions - should be in the form of a 2-page abstract (i) summarizing a formal result, (ii) a discussion of its relevance to the workshop and (iii) pointers to the relevant literature. The abstract can be supported by an additional paper (either published or technical report), that contain detailed proofs of any assertions. We especially encourage contributions which describe how to bring in results from other formal frameworks.
Submissions should be directed to this address. They should be submitted before Friday 9am GMT, October 31, 2008. Notification of acceptance will be given Friday, November 7, 2008.
Program - One day divided in to four sessions, two morning, two afternoon with coffee between. Each session would have one invited contributor talking for 45 mins followed by 15 mins discussion, except the first where there would be two 45 min tutorial presentations. The sessions would each have an additional part:
- Session 1 Tutorials by S. Ben-David and A. Blum;
- Session 2 Invited talk plus two contributed 15 mins presentations (posters to be shown in the afternoon);
- Session 3 Invited talk plus spotlight (2min) presentations for posters with poster session following during coffee break;
- Session 4 Invited talk followed by discussion aimed at identifying 10 key open questions.
Both John Langford and Csaba Szepesvari will present their take on the problem. The final program will be announced soon.
Facts about "NIPS-WEHYS 2008"
| Acronym | NIPS-WEHYS 2008 + |
| End date | December 13, 2008 + |
| Event type | Workshop + |
| Has location city | Vancouver + |
| Has location country | Category:Canada + |
| Homepage | http://www.kuleuven.be/wehys/ + |
| IsA | Event + |
| Notification | November 7, 2008 + |
| Start date | December 13, 2008 + |
| Submission deadline | October 31, 2008 + |
| Title | NIPS Workshop on Learning over Empirical Hypothesis Spaces + |
