NIPS Workshop on Grammar Induction, Representation of Language and Language Learning – call for participation

NIPS Workshop on Grammar Induction, Representation of Language and Language Learning
December 11th or 12th, 2009, Whistler, Canada

URL: http://www.cs.ucl.ac.uk/staff/rmartin/grll09/

Deadline for submission: 25th October 2009

Workshop Description

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Now is the time to revisit some of the fundamental grammar/language learning tasks such as grammar acquisition, language acquisition, language change, and the general problem of automatically inferring generic representations of language structure in a data driven manner.

Though the underlying problems have been known to be computationally intractable for the standard representations of the Chomsky hierarchy, such as regular grammars and context free grammars, progress has been made by modifying or restricting these classes to make them more observable. Generalisations of distributional learning have shown promise in unsupervised learning of linguistic structure using tree based representations, or using non-parametric approaches to inference. More radically, significant advances in this domain have been made by switching to different representations such as the work in Clark, Eyrand & Habrard (2008) that addresses the issue of language acquisition, but has the potential to cross-fertilise a wide range of problems that require data driven representations of language. Such approaches are starting to make inroads into one of the fundamental problems of cognitive science: that of learning complex representations that encode meaning. This adds a further motivation for returning to this topic at this point.

Grammar induction was the subject of an intense study in the early days of Computational Learning Theory, with the theory of query learning largely developing out of this research. More recently the study of new methods of representing language and grammars through complex kernels and probabilistic modelling together with algorithms such as structured output learning has enabled machine learning methods to be applied successfully to a range of language related tasks from simple topic classification through parts of speech tagging to statistical machine translation. These methods typically rely on more fluid structures than those derived from formal grammars and yet are able to compete favourably with classical grammatical approaches that require significant input from domain experts, often in the form of annotated data.

Call for Papers

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Target Audience The target audience of the workshop is machine learners with an interest in text modelling and processing who are interested in extending their work to more complex language tasks, cognitive systems and knowledge representation, moving beyond models that are implicitly or explicitly based on variants of finite state automata/Hidden Markov Models.

Format The workshop will be single-day, comprising of a tutorial introduction, invited talks (30 mins each + 30 mins for discussion), and presentations of contributed work, with time for discussions. Depending on quality and compatibility with workshop aims, slots for brief talks and posters will be allocated.

Contributions should be communicated to the program committee (the organizers) in form of an extended abstract (from 4 to 8 pages in the NIPS conference paper style), sent to: d.glowacka (at) cs.ucl.ac.uk

Important Dates

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25 October 2009 Deadline for abstract submission 2 November 2009 Notification of Acceptance 11/12 December 2009 Workshop taking place at Whistler, Canada

Invited Speakers

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Partha Niyogi University of Chicago

Mark Johnson Brown University

Dan Klein University of California at Berkeley

Workshop Organisers

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Alex Clark Royal Holloway, University of London Dorota Glowacka University College London John Shawe-Taylor University College London Yee Whye Teh University College London Chris Watkins Royal Holloway, University of London

Point of Contact

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Dorota Glowacka, Workshop Organiser d.glowacka (at) cs.ucl.ac.uk
John Shawe-Taylor, Workshop Organiser jst (at) cs.ucl.ac.uk

NIPS 2009 workshop on Learning from Multiple Sources with Applications to Robotics, Call for contributions

CALL FOR CONTRIBUTIONS

NIPS 2009 workshop on Learning from Multiple Sources with Applications to Robotics
Whistler, BC, Canada, December 11 or 12, 2009

http://www.dcs.gla.ac.uk/~srogers/lms09/index.htm

Important Dates:
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Submission of extended abstracts: October 27, 2009

Notification of acceptance: November 6, 2009

Workshop Description:
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Learning from multiple sources denotes the problem of jointly learning from a set of (partially) related learning problems / views / tasks. This general concept underlies several subfields receiving increasing interest from the machine learning community, which differ in terms of the assumptions made about the dependency structure between learning problems. In particular, the concept includes topics such as data fusion, transfer learning, multitask learning, multiview learning, and learning under covariate shift. Several approaches for inferring and exploiting complex relationships between data sources have been presented, including both generative and discriminative approaches.

The workshop will provide a unified forum for cutting edge research on learning from multiple sources; the workshop will examine the general concept, theory and methods, and will also examine *robotics* as a natural application domain for learning from multiple sources. The workshop will address methodological challenges in the different subtopics and further interaction
between them. The intended audience is researchers working in fields of multi-modal learning, data fusion, and robotics.

(More detailed background information is available at the workshop website.)

The workshop includes a morning session focused on theory/methods, and an afternoon session focused on the robotics application.

The workshop is a core event of the PASCAL2 Network of Excellence.

PASCAL2 Invited Speakers:
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Morning Session: Chris Williams – University of Edinburgh

Afternoon Session: to be announced

Submission Instructions:
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We invite submission of extended abstracts to the workshop. Extended abstracts should be 2-4 pages, formatted in the NIPS style: http://nips.cc/PaperInformation/StyleFiles
Unlike the main NIPS conference, identities of authors do not need to be removed from the extended abstracts.

Extended abstracts should be sent in .PDF or .PS file format by email, to either D.Hardoon (at) cs.ucl.ac.uk or gleen (at) cis.hut.fi. Acceptance to the workshop will be determined based on peer
review of each extended abstract.

Submissions are expected to represent high-quality, novel contributions in theory/methods of learning from multiple sources,or high-quality, novel contributions in application of learning
from multiple sources to robotics (see below).

To encourage participants from the machine learning community to test their algorithms in the domain of robotics, we will make available a dataset, with computed features, representative of
open research issues in robotics. Robotics-oriented papers submitted to the workshop are strongly encouraged to contain an experimental evaluation on the database made available by the organizers. The obtained results will be presented by the organizers during the workshop.

Submitted extended abstracts may be accepted either as an oral presentation or as a poster presentation; there will be only a limited number of oral presentations in the morning and afternoon sessions.

Accepted extended abstracts will be made available online at the workshop website.

Depending on the quality of submissions, we will consider preparing a special issue of a journal or a collected volume on the topic of the workshop. A separate call for papers will then be issued after the workshop for the special issue/collected volume. Last year’s “Learning from Multiple Sources” workshop led to a special issue in Machine Learning (currently in progress).

Organisers
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* Barbara Caputo – Idiap Research Institute.
* Nicolò Cesa-Bianchi – Università degli Studi di Milan.
* David Hardoon – Institute for Infocomm Research (I2R).
* Gayle Leen – Helsinki University of Technology.
* Francesco Orabona – Idiap Research Institure.
* Jaakko Peltonen – Helsinki University of Technology.
* Simon Rogers – University of Glasgow.

Update: Pascal Challenge on Large Scale Hierarchical Text Classification

*** Change of important dates ***
*** End of testing: November 16 ***
*** End of scalability tests: December 14 ***

The challenge is well underway now and we are happy to observe very active participation by
various research teams. As mentioned above, we have decided to extend the end of the challenge, which will hopefully give more time to the participants to improve their methods.

For more information about the challenge, please refer to its Web site.

If you have not already done so, you can register for the challenge and gain access to the DMOZ datasets, by creating a new account at the Web site.

Original Announcement
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Website: http://lshtc.iit.demokritos.gr/
Email: lshtc_info (at) iit.demokritos.gr

We are pleased to announce the launch of the Large Scale Hierarchical Text classification (LSHTC) Pascal Challenge. The LSHTC Challenge is a hierarchical text classification competition using large datasets based on the ODP Web directory data (www.dmoz.org).

Hierarchies are becoming ever more popular for the organization of text documents, particularly on the Web. Web directories are an example. Along with their widespread use, comes the need for automated classification of new documents to the categories in the hierarchy. As the size of the hierarchy grows and the number of documents to be classified increases, a number of interesting machine learning problems arise. In particular, it is one of the rare situations where data sparsity remains an issue despite the vastness of available data. The reasons for this are the simultaneous increase in the number of classes and their hierarchical organization. The latter leads to a very high imbalance between the classes at different levels of the hierarchy. Additionally, the statistical dependence of the classes poses challenges and opportunities for the learning methods.

The challenge will consist of four tasks with partially overlapping data. Information regarding the tasks and the challenge rules can be found at challenge Web site, under the “Tasks, Rules and Guidelines” link.

We plan a two-stage evaluation of the participating methods: one measuring classification performance and one computational performance. It is important to measure both, as they are dependent. The results will be included in a final report about the challenge and we also aim at organizing a special NIPS’09 workshop.

In order to register for the challenge and gain access to the datasets, please create a new account at challenge Web site.

Key dates:
Start of testing: July 10, 2009.
End of testing, submission of executables and short papers: September 29, 2009.
End of scalability test and announcement of results: October 25, 2009.
NIPS’09 workshop (subject to approval): December 11-12, 2009

Organisers:
Eric Gaussier, LIG, Grenoble, France
George Paliouras, NCSR “Demokritos”, Athens, Greece
Aris Kosmopoulos, NCSR “Demokritos”, Athens, Greece
Sujeevan Aseervatham, LIG, Grenoble & Yakaz, Paris, France

*Rescheduled* Call for participation: µTOSS – Multiple hypotheses testing in an open software system

Berlin Institute of Technology
Faculty IV, Machine Learning Department
Franklinstrasse 28/29, D-10587 Berlin, Germany
January 18th – February 12th, 2010
Organizers: T. Dickhaus, G. Blanchard
Further information: http://user.cs.tu-berlin.de/~dickhaus/mutoss.html

In the framework of the “Harvest” programme of the PASCAL2 European network of excellence, we are happy to announce an initiative to create a open software platform for multiple testing. The goal aimed at is to produce a toolbox written in R with the following characteristics:

– broad-spectrum: with the ambition of becoming a reference implementation
– user-friendly: intended for use by practitioners
– extensible: able to easily accomodate new contributed methods
– open-source: coded in the R language and freely available for all

To this end, an intensive 4-week coding period is planned in the period Jan. 18 to Feb. 12, 2010 at the Berlin Institute of Technology, Germany, starting with a tutorial on multiple testing methods on Jan. 18-19.

This is a call for participation to this project under different forms:

* Core package coding team: we want to create a core team of about 4-5 persons to write the R package in the announced period. We are looking for researchers, practitioners or students willing to participate in the project for the duration of this period, with a previous experience either in statistics (ideally: multiple testing methods) or in R coding. The PASCAL2 network will cover travel and subsistence costs for the duration of the project. (Deadline for application: December 4th, 2009)

* Request for wanted features: If you are a user or developer of multiple testing methods and would be interested in the end software, you are welcome to communicate wanted features (interface, available methods, data types and formats handled) to the package wishlist (available under http://user.cs.tu-berlin.de/~dickhaus/mutoss-wishlist.html). Please write an informal email to Thorsten Dickhaus (dickhaus@cs.tu-berlin.de)

* Tutorial: persons interested in participating specifically in the tutorials should contact the organizers.

Thorsten Dickhaus (dickhaus (at) cs.tu-berlin.de)
Gilles Blanchard (gilles.blanchard (at) wias-berlin.de)

Call for Contributions: NIPS 2009 Workshop on Nonparametric Bayes

Call for Abstracts and Participation
NIPS 2009 WORKSHOP on NONPARAMETRIC BAYES
December 11/12, 2009
http://npbayes-2009.wikidot.com

abstract submission deadline: Wednesday, October 21st

BACKGROUND

One of the major problems driving current research in statistical machine learning is the search for ways to exploit highly-structured models that are both expressive and tractable. Bayesian nonparametrics provides a framework for developing robust and flexible models that can accurately represent the complex structure in the data. Model flexibility is achieved by assigning priors with unbounded capacity and overfitting is prevented by integrating out all parameters and
latent variables.

Current work has established results on asymptotic behaviour of simple Bayesian nonparametric models. Most of the results are for simple cases, such as density estimation and Gaussian process regression. However, there is a steady development of tools, which are starting to allow us to tackle much more challenging models. We have invited experts to comment and provide guidance on discussion on this topic. We also invite theoreticians within the NIPS community to participate
in this focus.

It is essential to provide easy to follow guidance for the model structure specification and the choice of hyperparameters. A step towards this direction is the discussion of an objective or empirical Bayes treatment. Additionally, developing general purpose software that can scale up inference techniques to massive datasets would is another step necessary for the wide applicability of these models. This workshop will help us summarize the current state of the
practical use of nonparametric Bayesian models and focus on the requirements of the field to extend its use in other application domains.

WORKSHOP

We aim to bring together researchers to create a forum for discussing recent advances in Bayesian nonparametrics, to understand better the asymptotic properties of the models and to inspire research on new techniques for better models and inference algorithms. The workshop will focus mainly on two important issues. 1) Theoretical properties of complex Bayesian nonparametric models, in particular asymptotics (e.g. consistency, rates of convergence, and Bernstein von- Mises results). 2) Practical matters to enable the use of Bayesian nonparametrics in real world applications such as developing general purpose software, discussion of an objective or empirical Bayes treatment. Each focus will be given a specific session during the workshop.

This is the fifth in a series of successful workshops on this topic. The first two were at NIPS 2003 and 2005 and the last two were at ICML 2006 and 2008. The field attracts researchers from a broad range of disciplines, ranging from theoretical statisticians and probabilists to people working on specialized applications. This workshop aims to enhance the interaction between these communities which has been initiated by previous workshops in order to exchange ideas, discuss
future directions, and build collaborative efforts.

The workshop will consist of 5 invited talks, 4 contributed talks, a session for informal impromptu talks and a poster session.

INVITED SPEAKERS (confirmed)

Zoubin Ghahramani, University of Cambridge
http://learning.eng.cam.ac.uk/zoubin/

Subhashis Ghoshal, North Carolina State University
http://www4.stat.ncsu.edu/~sghosal/

Tom Griffiths, University of California, Berkeley
http://cocosci.berkeley.edu/tom/

Alejandro Jara, Universidad de Concepción
http://www2.udec.cl/~ajarav/

CALL FOR PARTICIPATION

Researchers interested in presenting their work and ideas on Bayesian nonparametrics at the workshop should send an email to npbayes2009 (at) googlemail.com with the following information:

Title
Authors
Abstract (maximum 3 pages, NIPS style pdf)
Preferred contribution (talk, poster, and/or round-table
participation)

We expect authors to provide a final version of their papers by early December for inclusion on the workshop home page. Papers chosen for contributed talks shall also be expected to liaise with a discussion leader who will be in charge of stimulating discussion of the work at the workshop.

DATES
Abstracts due: Oct 21, 2009
Notifications: Nov 4, 2009
Final paper due: Dec 2, 2009
Workshop: Dec 11/12, 2009

LOCATION

Westin Resort and Spa / Hilton Whistler Resort and Spa
Whistler, B.C., Canada
https://nips.cc/Conferences/2009/

ORGANIZERS

Dilan Gorur, Gatsby Computational Neuroscience Unit
François Caron, INRIA Bordeaux Sud-Ouest
Yee Whye Teh, Gatsby Computational Neuroscience Unit
David Dunson, Duke University
Zoubin Ghahramani, University of Cambridge
Michael I. Jordan, University of California at Berkeley

CONTACT

npbayes2009@googlemail.com

Your expertise, experience and perspective are very valuable to making the workshop a success. Thank you very much, and we hope to see you at the workshop!

Dilan Gorur, François Caron, Yee Whye Teh, David Dunson, Zoubin
Ghahramani and Michael I. Jordan.

http://npbayes-2009.wikidot.com/

Call for Contributions: MiniSymposia on Assistive Machine Learning for People with Disabilities

Call for contributions

MiniSymposia on Assistive Machine Learning for People with Disabilities

http://www.davidroihardoon.com/AMD09

A mini-symposium at Advances on Neural Information Processing Systems (NIPS 2009) Whistler, BC, Canada, December 10, 2009.

Deadline for submission of extended abstracts: October 23, 2009,

DESCRIPTION

Nowadays, there are massive amounts of heterogeneous electronic information available on the Web. People with disabilities, among other groups potentially influenced by the digital gap, face great barriers when trying to access information. Sometimes their disability makes their interaction the ICT environment (eg., computers, mobile phones, multimedia players and other hardware devices) more difficult. Furthermore, the contents are delivered in such formats that cannot be accessed by people with disability and the elderly. The challenge for their complete integration in information society has to be analyzed from different technology approaches.

Recent developments in Machine Learning are improving the way people with disabilities access to digital information resources. From the hardware perspective, Machine Learning can be a core part for the correct design of accessible interaction systems of such users with computers (such as BCI). From the contents perspective, Machine Learning can provide tools to adapt contents (for instance changing the modality in which it is accessed) to users with special needs. From the users’ perspective, Machine Learning can help constructing a good user modeling, as well as the particular context in which the information is accessed.

SUBMISSION INSTRUCTIONS

Researchers interested in contributing should send a PDF file with an extended abstract (1-4 pages long) to symp (at) tsc.uc3m.es

ORGANIZERS

Fernando Perez-Cruz (Universidad Carlos III de Madrid)
Emilio Parrado-Hernandez (Universidad Carlos III de Madrid)
David R. Hardoon (Institute for Infocomm Research)
Jaisiel Madrid-Sanchez (INREDIS Management Office. Technosite. ONCE Foundation)

CFP: Machine Translation Journal, Special Issue on “Pushing the frontier of Statistical Machine Translation

CALL FOR PAPERS

Special Issue on Pushing the frontier of Statistical Machine Translation

THE MACHINE TRANSLATION JOURNAL

http://www.springer.com/computer/artificial/journal/10590

Guest editors:
Lucia Specia (University of Wolverhampton)
and Nicola Cancedda (Xerox Research Centre Europe)

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Many current approaches to statistical machine translation are based on incremental improvements over a standard framework: word-alignments produced by IBM-models, rules extracted from word alignments using heuristics, a log-linear model combining commonly used features whose weights are optimized on a development set using reference translations,
and a beam-search decoder to generate final translations.

This special issue is dedicated to alternative approaches to statistical MT radically diverging in as many as possible of the aspects involved in the process, including (but not limited to) the
following:

– Word / phrase alignment (including hierarchical alignment)
– Rule / phrase extraction
– Statistical translation models
– Features / factors and ways to combine them
– Combination of linguistic and statistical methods
– Language modelling
– Real-time adaptation of MT with user feedback
– Domain adaptation of MT
– Parameter optimization
– Decoding

Contributions on approaches which still achieve performance levels below the state of the art can be suitable for inclusion in this issue, provided they stem from sound directions with clear potential
for further developments.

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Submission guidelines:

– Authors should follow the “Instructions for Authors” available on the MT Journal website:

http://www.springer.com/computer/artificial/journal/10590

– Submissions must be limited to 15 pages (including references)

– Papers should be submitted online directly on the MT journal’s submission website: http://www.editorialmanager.com/coat/default.asp, indicating this special issue in ‘article type’

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Important dates

– Deadline for ‘Submission Intent’ e-mail: September 15, 2009 – contributors must send an email with the abstract of the submission to lspecia (at) gmail.com

– Deadline for submissions: October 30, 2009

– Notification to the authors: December 20, 2009

– Deadline for camera-ready: January 30, 2009

CONFERENCE: Artificial Intelligence and Statistics (AISTATS) 2010

AISTATS*2010 Call for Papers
Thirteenth International Conference on Artificial Intelligence and Statistics
May 13-15, 2010, Chia Laguna, Sardinia, Italy
http://www.aistats.org

This is the thirteenth conference on Artificial Intelligence and Statistics (AISTATS*2010), an interdisciplinary gathering of researchers at the intersection of computer science, artificial
intelligence, statistics, and related areas.

Since its inception the AISTATS conference has been held every two years in North America. At the 2009 conference, with the support of the EU funded PASCAL II Network of Excellence
(www.pascal-network.org), the decision was made to bring the conference to Europe for the first time. Starting in 2010 AISTATS will be held every year, alternating the venue between Europe and North America.

The Conference Programme will include invited talks, contributed talks, and posters. Contributed talks and posters are selected via a rigorous peer-review process based on 8 page papers. Accepted papers will be published as a special issue in the Journal of Machine Learning Research (JMLR) Workshop and Conference Proceedings Series.

As an innovation for AISTATS*2010, some time at the conference will be set aside for “breaking news” posters submitted on the basis of a one-page abstract. These are reports on ongoing or unpublished projects, projects already published elsewhere, partially developed ideas, negative results etc, and are meant as informal forums to encourage discussion. The review process of these posters will be very light-touch but presentation of these at the Conference will not
lead to publication in the Proceedings.

Since its inception in 1985, the primary goal of this conference has been to broaden research at the interface between artificial intelligence and statistics. Papers and abstracts on all aspects of
this interface are strongly encouraged, including but not limited to:

active learning and experimental design
applications
approximate and exact inference
Bayesian statistics
causality
classification and regression
graphical models
kernel and large margin methods
latent variable models
model selection and structure learning
neural networks
online learning
optimization and search
unsupervised and semi-supervised learning
reinforcement learning and decision making
statistical databases
statistical software
statistical learning theory
structured and relational learning
visualization of datasets

Submission Requirements:

Peer-Reviewed Papers:

Electronic submission of papers is required. Papers may be up to 8 double-column pages in length; formatting and submission information can be found on the AI and Statistics Conference Management page. Submissions will be considered if they are received by 23:59, Friday
November 6th, 2009, Universal Time.

Submitted papers will undergo a rigorous double-blind review process. Acceptance notifications will be emailed by February 13th, and camera-ready final versions (same format) will be due on March 13th, 2010. These papers will be presented at the Conference either as contributed talks or posters, and will be published as a special issue in the JMLR Workshop and Conference Proceedings Series. Papers for talks and posters will be treated equally in publication.

Breaking-news Posters:

Electronic submission of one page double-column abstracts is required. Formatting and submission information can be found on the AI and Statistics Conference Management page. Submissions will be considered if they are received by 23:59, Friday February 26th, 2010, Universal
Time.

Abstracts will be lightly reviewed. Acceptance notifications will be emailed by March 26th. These will be presented as “breaking news” posters at the conference and will not be published.

Programme Chairs:

Yee Whye Teh, University College London, U.K.
Mike Titterington, University of Glasgow, U.K.

General Chair:

Neil Lawrence, University of Manchester, U.K.

Special Issue on “Robot Learning in Practice”, IEEE Robotics and Automation Magazine

CALL FOR PAPERS
Special Issue of the
IEEE Robotics and Automation Magazine
“Robot Learning in Practice”

Guest Editors:

Jun Morimoto (ATR Computational Neuroscience Laboratories, Japan)
Chad Jenkins (Brown University, USA)
Marc Toussaint (TU Berlin, Germany)

Scope:

IEEE Robotics and Automation Magazine (RAM) seeks articles for this special issue, scheduled for publication in June 2010.

There is an increasing interest in machine learning and statistics within the robotics community. At the same time, there has been a growth in the learning community in using robots as motivating
applications for new algorithms and formalisms. Considerable evidence of this exists in the use of learning in high-profile competitions such as RoboCup and the DARPA Challenges, and the growing number of research programs funded by governments around the world.

The proposed special issue is intended to publish contributions on robot learning algorithms with practical applications. Areas of research interest include:

* learning models of robots, task or environments.

* learning hierarchical representations from sensor inputs and motor outputs to task abstractions.

* learning of plans and control policies by imitation and reinforcement learning.

* extraction of low-dimensional task relevant representations for robot learning.

* learning robust policies that work in real environments.

* state estimation algorithms for robot learning.

Submission instructions:

Articles must be around a nominal length of eight pages each. We encourage submission of supplementary material such as experiment videos and source code. For further details see the instruction page:

http://www.ieee-ras.org/ram/for_authors

Submission deadline: October 1st, 2009
Issue date: June 2010

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Jun Morimoto (point of contact)
Department of Brain Robot Interface
ATR Computational Neuroscience Laboratories
2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan
E-mail : xmorimo (at) atr.jp

Chad Jenkins
Department of Computer Science
Brown University,
115 Waterman St, 4th Floor
Providence, RI, USA 02912-1910
E-mail: cjenkins (at) cs.brown.edu

Marc Toussaint
TU Berlin
Franklinstr. 28/29 FR6-9
10587 Berlin, Germany
E-mail: mtoussai (at) cs.tu-berlin.de

IEEE-RAS TC on Robot Learning web page:
http://www.learning-robots.de/

CfPPP: MLSB-09, SEP 5-6 2009, Ljubljana, Slovenia

MLSB 09: 3rd International Workshop on
Machine Learning in Systems Biology
5-6 September 2009, Ljubljana, Slovenia
http://mlsb09.ijs.si/

3 AUG: Poster abstract submission deadline
17 AUG: Early registration deadline

We kindly invite you to participate and/or present a poster at MLSB-09, the 3rd International Workshop on Machine Learning in Systems Biology. The workshop aims to contribute to the cross-fertilization between the research in machine learning methods and their applications to systems biology. The program of the workshop will include 6 invited talks by renowned researchers and
12 oral presentations of reviewed contributions (see http://mlsb09.ijs.si/program.html for details).
It will also include a poster session: Abstracts for poster presentations can be submitted by 3 AUG 2009.

The Workshop is organized as “core – event” of PASCAL2, Network of Excellence in Pattern Analysis, Statistical Modelling and Computational Learning (http://www.pascal-network.org/) The workshop will take place 5-6 September 2009 at the Jozef Stefan Institute, Ljubljana, Slovenia. It will immediately precede ECML PKDD 2009, taking place 7-11 September 2009 in Bled, Slovenia (Bled is 30 miles from Ljubljana, transport will be organized).