posted by system || 2665 views || tracked by 6 users: [display]

NIPS-WEHYS 2008 : NIPS Workshop on Learning over Empirical Hypothesis Spaces

FacebookTwitterLinkedInGoogle

Link: http://www.kuleuven.be/wehys/
 
When Dec 13, 2008 - Dec 13, 2008
Where Vancouver, Canada
Submission Deadline Oct 31, 2008
Notification Due Nov 7, 2008
Categories    machine learning
 

Call For Papers

Call for Contributions - 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.


Related Resources

ICMLA 2024   23rd International Conference on Machine Learning and Applications
IEEE COINS 2024   IEEE COINS 2024 - London, UK - July 29-31 - Hybrid (In-Person & Virtual)
ICDM 2024   IEEE International Conference on Data Mining
ECAI 2024   27th European Conference on Artificial Intelligence
ICPE 2024   15th ACM/SPEC International Conference on Performance Engineering
JCICE 2024   2024 International Joint Conference on Information and Communication Engineering(JCICE 2024)
DSIT 2024   2024 7th International Conference on Data Science and Information Technology (DSIT 2024)
ACM-Ei/Scopus-CCISS 2024   2024 International Conference on Computing, Information Science and System (CCISS 2024)
CCBDIOT 2024   2024 3rd International Conference on Computing, Big Data and Internet of Things (CCBDIOT 2024)
EAIH 2024   Explainable AI for Health