*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)

—————————————————————————

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.

—————————————————————————

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’

—————————————————————————

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

————————————————————————-

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).

MLCB 2009 “New Problems and Methods in Computational Biology”, Call for Contribution

Call for contributions
New Problems and Methods in Computational Biology
http://www.mlcb.org

A workshop at the Twenty-Third Annual Conference on Neural Information Processing Systems (NIPS 2009) Whistler, BC, Canada, December 11 or 12, 2009.

Deadline for submission of extended abstracts: September 27, 2009,

WORKSHOP DESCRIPTION

The field of computational biology has seen dramatic growth over the past few years, in terms of newly available data, new scientific questions and new challenges for learning and inference. In particular, biological data is often relationally structured and highly diverse, and thus requires combining multiple weak evidence from heterogeneous sources. These sources include sequenced genomes of a variety of organisms, gene expression data from multiple technologies, protein sequence and 3D structural data, protein interaction data, gene ontology and pathway databases, genetic variation data (such as SNPs), and an enormous amount of text data in the biological and medical literature. These new types of scientific and clinical problems require novel
supervised and unsupervised learning approaches that can use these growing resources.

The workshop will host presentations of emerging problems and machine learning techniques in computational biology. We encourage contributions describing either progress on new bioinformatics problems or work on established problems using methods that are substantially different from standard approaches. Kernel methods, graphical models, semi-supervised approaches, feature selection and other techniques applied to relevant bioinformatics problems
would all be appropriate for the workshop.

SUBMISSION INSTRUCTIONS

Researchers interested in contributing should upload an extended abstract of 1-6 pages in PDF format to the MLCB submission web site http://www.easychair.org/conferences/?conf=mlcb2009 by September 27, 2009, 11:59pm (Samoa time).

No special style is required. Authors may use the NIPS style file, but are also free to use other styles as long as they use standard font size (11-12 pt) and margins (1 in).

All submissions will be anonymously peer reviewed and will be evaluated on the basis of their technical content. A strong submission to the workshop typically presents a new learning method
that yields new biological insights, or applies an existing learning method to a new biological problem. However, submissions that improve upon existing methods for solving previously studied problems will also be considered. Examples of research presented in previous years
can be found online at http://www.mlcb.org/nipscompbio/previous/.

Please note that accepted abstracts will be posted online at www.mlcb.org. Authors may submit two versions of their abstract, a longer version for review and a shorter version for posting to the web page. In addition, presentations will be video taped and published online as part of the videolectures.net website supported by Pascal.

The workshop allows submissions of papers that are under review or have been recently published in a conference or a journal. This is done to encourage presentation of mature research projects that are interesting to the community. The authors should clearly state any overlapping published work at time of submission. Authors of accepted abstracts will be invited to submit full length versions of their contributions for publication in a special issue of BMC
Bioinformatics.

ORGANIZERS

Gal Chechik,
Google Research
Tomer Hertz,
Fred Hutchinson Cancer Research Center
William Stafford Noble,
Department of Genome Sciences, University of Washington
Yanjun Qi,
Machine Learning Department, NEC Research
Jean-Philippe Vert,
Mines ParisTech, Institut Curie
Alexander Zien,
LIFE Biosystems

PROGRAM COMMITTEE

Mathieu Blanchette, McGill University
Florence d’Alche-Buc, Université d’Evry-Val d’Essonne, Genopole,
Eleazar Eskin, UC Los Angeles,
Nir Friedman, The Hebrew University of Jerusalem ,
David Heckerman, Microsoft Research ,
Michael I. Jordan, UC Berkeley ,
Christina Leslie, Memorial Sloan-Kettering Cancer Research Center,
Michal Linial, The Hebrew University of Jerusalem ,
Quaid Morris, University of Toronto,
Klaus-Robert Müller, Fraunhofer FIRST ,
Dana Pe’er, Columbia University ,
Uwe Ohler, Duke University ,
Günnar Rätsch, Friedrich Miescher Laboratory of the Max Planck Society,
Alexander Schliep, Rutgers University,
Koji Tsuda, Computational Biology Research Center
Eric Xing, Carnegie-Mellon University ,

PhD opportunities in bioinformatics and biomathematics

Bristol Centre for Systems Biomedicine
Doctoral Training Programme in Bioinformatics and Biomathematics

Bristol Centre for Systems Biomedicine represents an innovative interdisciplinary doctoral training programme offering fully funded 4 (1+3) year studentships, five available in October 2009, five available in 2010. Under the Medical Research Council Capacity Building scheme, these studentships have a £2K pa top up added to the standard stipend of £13K pa, plus tuition fees
and travel/meetings allowance.

High calibre graduates from mathematical, engineering and computationally related disciplines
with a strong interest in developing these skills and applying their abilities to biomedical research, will be trained in a structured and broad programme of relevant short courses, seminars, short (3x3month) projects, and a full PhD project. Programme topics range from population dynamics to the different ‘omics’ (e.g. genomics, proteomics) and pathway and molecular applications. It includes mathematical, statistical and genetic epidemiology, evolutionary, behaviour and mutation theory, evidence synthesis, decision sciences and trials, cell signalling, omics technologies
network inference, DNA, RNA and protein prediction and mathematical modelling in neurophysiological contexts.

In the first core foundation year, students will have a group base in Oakfield House
http://www.bristol.ac.uk/university/maps/google-precinct/index.html.
The programme is led from the Department of Social Medicine with close co-supervisory arrangements with the Departments of Mathematics, Engineering Mathematics, Computer Science and other groups from the medical faculties at the University of Bristol. The host departments all gained very high ratings in RAE2008. BCSBmed is also closely interlinked with five other MRC, Wellcome Trust and EPSRC doctoral training centres in Bristol.

For information about the university, please visit http://www.bristol.ac.uk/ and for a further details: http://www.findaphd.com/

For application forms, please contact the Programme Director, Prof. Ian Day, ian.day (at) bristol.ac.uk.
Please mark your subject line ‘BCSBmed application forms request’ : you will receive an automatic reply.

For information pack or informal enquiries, please contact ian.day (at) bristol.ac.uk, subject line marked ‘BCSBmed information request’.

Further informal enquiries can be directed to C.Campbell (at) bris.ac.uk, subject line marked ‘BCSBmed enquiry’

Open Position : Postdoc in Computational Biology, Cambridge, UK

The genetics lab of Prof. Sir Bruce Ponder and the computational biology lab of Dr. Florian Markowetz at the Cancer Research UK Cambridge Research Institute offer a joint position for a postdoctoral researcher interested in statistical and computational approaches to systems genetics in cancer.

The recent whole-genome scan for breast cancer [1] has identified five novel susceptibility loci. In follow-up work the strongest locus has been narrowed down to two SNPs in the intronic region of the FGFR2 gene [2]. However, a detailed understanding of the disease mechanism is still missing. This project will use a systems biology approach to elucidate the functional roles of FGFR2 and other cancer susceptibility genes. We will integrate diverse genomic data sources (including gene expression, SNPs, copy number variants and others) using statistical network methods [3]. The resulting networks will be used to identify key drivers of disease and their functional mechanisms. The methods developed in breast cancer will also be applied to other cancer types, e.g. lung cancer.

The position bridges between an experimental and a computational lab and is ideal if you are interested in data analysis and method development motivated by close collaborations with experimentalists.

The ideal applicant has a strong background in data analysis and statistical modelling (including knowledge of R or Matlab). Experience in medical or biological research is desireable.

If you are highly motivated to work in an interdisciplinary and very collaborative environment at an internationally recognized research institute, apply by sending your CV to Florian Markowetz at
florian.markowetz (at) cancer.org.uk.

For more information please visit http://www.markowetzlab.org

References

1. DF Easton, …, BAJ Ponder Nature 2007. PMID 17529967
2. KB Meyer, … , BAJ Ponder PLoS Biology 2008. PMID 18462018
3. F Markowetz and R Spang, BMC Bioinf, 2007. PMID 17903286