News Archives

Summer School on Cognitive Science and Machine Learning

We invite applications for the PASCAL2 Summer School on Cognitive Science and Machine Learning that will be held at Sardegna Ricerche (Italy) in May 2010.

http://www.mlss.cc/sardinia10

The summer school will take place immediately before and near the AISTATS 2010 conference.

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Theme of the Summer School:

Cognitive science aims to reverse engineer human intelligence; machine learning provides one of our most powerful sources of insight into how machine intelligence is possible. Cognitive science therefore raises challenges for, and draws inspiration from, machine learning; and insights about the human mind may help inspire new directions for machine learning. This summer school brings together leading researchers from both fields, and those working at the interface between them. It is aimed at graduate students, post-docs and established researchers from both the cognitive science and machine learning communities, interested in exploring the interface between human and machine intelligence.

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Confirmed Speakers (partial list):

Nick Chater, University College London
Alex Clark, Royal Holloway University of London
Silvia Chiappa, Cambridge University
Peter Dayan, University College London
Tom Griffiths, UC Berkeley
Konrad Körding, University of Chicago
Neil Lawrence, Manchester University
Bernhard Schölkopf, Max Planck Institute for Biological Cybernetics
Satinder Singh, University of Michigan
Josh Tenenbaum, MIT
Chris Watkins, Royal Holloway University of London
Felix Wichmann, Technical University of Berlin

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Organisers:

Nick Chater, University College London
Silvia Chiappa, Cambridge University
Tom Griffiths, UC Berkeley
Neil Lawrence, Manchester University
Bernhard Schölkopf, Max Planck Institute for Biological Cybernetics
Josh Tenenbaum, MIT

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Important Dates:

Application Submission Deadline: March 1 2010
Notification: March 26 2010
Subscription Deadline: April 1 2010
School: from May 6 to May 12 2010

Two Postdoc Positions: Vision group at Xerox Research Centre Europe

Xerox Research Centre Europe, based in Grenoble, France, is currently looking for two researchers with skills than can both integrate well and complement the current team; one focused on large-scale methods and the other on image analysis and feature extraction.

The Text and Visual Pattern Analysis Area (TVPA) group at Xerox Research Centre Europe, Grenoble, is a world-leading team that specializes in understanding, organizing, retrieving and enhancing both visual and hybrid content. Our research is the result of combining state of the art knowledge and skills from different fields such as machine learning, large-scale data mining, image analysis, and text retrieval. We have extensive experience and state of the art methods in image categorization, image enhancement, quality assessment and document image processing for both text and image content. Our main research lines currently focus on using this knowledge and expertise in the challenging areas of applied visual aesthetics and hybrid information access; going beyond the standard classification approach and developing techniques applicable in a variety of domains for assisted content creation and management.

Two candidates are sought with skills than can both integrate well and complement the current team; one focused on large-scale methods and the other on image analysis and feature extraction. This is an opportunity to join the group and research centre at a key time, and we are seeking researchers who will relish the challenge of not only carrying out leading research — through strong collaboration with Xerox researchers and also the wider academic community — but also influence the research agenda and potentially see strong deployment to be used worldwide in client systems.

For both positions, a strong background in machine learning and image or text processing is essential. Working with the team, the responsibilities will include inventing and developing novel techniques for document content analysis, both in terms of visual, text or hybrid media content. More specifically, there is currently a strong focus on large-scale learning and leveraging different types of media and social aspects to improve performance. The particular requirements for each position are detailed below:

Large-scale methods for retrieval and processing: the amount of digital content now stored and processed in a wide variety of application domains is every-increasing and the need for truly effective large scale techniques is clear. We are looking to develop retrieval and analysis methods that are computationally effective and can be used on image content, scanned forms, text-image hybrid data and even handwritten text. We are particularly seeking individuals with experience in one or more of large-scale methods, use of hybrid image/text data and handwriting recognition.

Image analysis and feature extraction: the team currently has state-of-the-art image processing techniques which have been repeatedly shown to be successful, through the strong publication record of the group, through excellent performance in competitions such as ImageCLEF and through deploying the technologies in various industrial applications. We are seeking a researcher with a blend of image analysis and machine learning skills that can use these technologies in different domains, and also develop the next generation of such methods. Experience with video analysis is desirable, as digital media of this type is increasingly common and is one of the focuses of Xerox customers for future digital asset management and real-time use of image processing technology.

Requirements (both positions):

* PhD in Computer Science in the area(s) of Machine Learning and/or Computer Vision

* Strong publication record and evidence of implementing systems

* Strong English-language written and oral communications skills

Informal enquiries are welcome and can be made in the first instance to the area manager: craig.saunders(at)xrce.xerox.com

To submit an application, please send your CV and cover letter to both xrce-candidates(at)xrce.xerox.com and craig.saunders(at)xrce.xerox.com.

About XRCE

More information about the position can be found at: http://www.xrce.xerox.com/About-XRCE/Jobs/Two-researchers-sought-for-leading-Textual-Visual-Pattern-Analysis-group

Workshop: Foundations and New Trends of PAC Bayesian Learning

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Workshop:

Foundations and New Trends of PAC Bayesian Learning

University College London, UK

22 – 23 March 2010

http://www.cs.ucl.ac.uk/staff/rmartin/pacbayes/

CALL FOR PAPERS
Deadline: Friday, 12th February 2010
============================================

PAC-Bayes theory is a framework for deriving some of the tightest generalization bounds available. Many well established learning algorithms can be justified in the PAC-Bayes framework and even improved. PAC-Bayes bounds were originally applicable to classification, but over the last few years the theory has been extended to regression, density estimation, and problems with non iid data. The theory is well established within a small group of the statistical learning community, and has now matured to a level where it is relevant to a wider audience. The workshop will include tutorials on the foundations of the theory as well as recent findings through peer reviewed presentations.

Workshop topics

PAC Bayes theory or applications. In particular: application to:

* regression
* density estimation
* hypothesis testing
* structured density estimation
* non-iid data
* sequential data

The Invited Speakers include:

Olivier Catoni
CNRS U.M.R. 8553

David McAllester
Toyota Technological Institute at Chicago

Matthias Seeger
Saarland University and Max Planck Institute for Informatics

Organisers: Jean-Yves Audibert, Matthew Higgs, Steffen Grünewälder, François Laviolette and John Shawe-Taylor

Contact:
Steffen Grünewälder
steffen(at)cs.ucl.ac.uk

Call for papers: The Fifth European Workshop on Probabilistic Graphical Models (PGM’2010), Helsinki, Finland, September 13-15, 2010

PGM’2010 – FIRST CALL FOR PAPERS
================================

The Fifth European Workshop on Probabilistic Graphical Models (PGM’2010)
Helsinki, Finland, September 13-15, 2010
http://www.helsinki.fi/pgm2010/

Key dates:
==========
* Deadline for submissions: June 4, 2010
* Notification of acceptance: July 16, 2010
* Final versions due: July 30, 2010
* Workshop: September 13-15, 2010

Call for papers:
================

The European Workshop on Probabilistic Graphical Models (PGM) is a biennial workshop, which was first held in Cuenca, Spain, in 2002, followed by workshops in Leiden (2004), Prague (2006) and Hirtshals (2008). The fifth PGM workshop will be held in Helsinki, Finland, September 13-15, 2010.

The aim of the workshop is to bring together people interested in probabilistic graphical models and provide a forum for discussion of the latest research developments in this field. The workshop is organized so as to facilitate discussions and collaboration among the participants also outside the workshop sessions. We welcome theoretical and applied contributions related to the following topics:
* Principles of Bayesian (belief) networks, chain graphs, decision networks, influence diagrams, and other probabilistic graphical models (PGMs)
* Information processing in PGMs, exact and approximate inference
* Learning in the context of PGMs: machine learning approaches, statistical methods, data/graph mining, criteria for model selection/validation/regularization, optimization algorithms for searching the model space
* Exploitation of results from different disciplines for the construction and use of PGMs, e.g., computer science, statistics, information theory, mathematics, physics, optimization, decision theory
* Software systems based on PGMs
* Applications of PGMs to real-world problems

We invite submissions that concern one or more of the above topics, or any other aspects related to probabilistic graphical models. Papers submitted for review should report on original, previously unpublished work. Each submitted paper will be reviewed by at least two reviewers.

Submission procedure: see http://pgm2010.hiit.fi/cfp.html

Programme Co-Chairs:
Petri Myllymäki, University of Helsinki
Teemu Roos, Helsinki Institute for Information Technology HIIT
Tommi Jaakkola, MIT

Contact: pgm2010(at)helsinki.fi
Home page: http://www.helsinki.fi/pgm2010/

Helsinki Institute for Information Technology HIIT: Postdoc and senior researcher positions

Postdoctoral and senior researcher positions in computing research

Helsinki Institute for Information Technology HIIT and the Aalto
University Department of Information and Computer Science are inviting
applications for postdoctoral and senior researcher positions in several
areas of computing research including: machine learning and data
analysis; computational methods for networks, interaction and
economics; large constraint models; nanoscale self-assembly;
enhancement of internet infrastructure; human-centric ubiquitous IT.

The closing date for applications is 15 March 2010. The positions will
be filled for three years maximum starting at the earliest 1 August 2010.

Further details of the posts and the application procedure are available at:
http://www.hiit.fi/jobs (3 positions)
http://ics.tkk.fi/en/vacancies/ (4 positions)

Aalto University is a newly created research university resulting from
the merger of three Finnish universities: Helsinki University of
Tehnology TKK, the Helsinki School of Economics, and the University of
Art and Design Helsinki. The new university was launched in January
2010, and opens up a new world of possibilities for multidisciplinary
education and research. For further information, see www.aalto.fi/en/.

HIIT is a joint research institute of Aalto University and the
University of Helsinki conducting basic and strategic research on
information technology.

COGS ERC grant candidate selection panel

We are advertising a four year PhD interdisciplinary studentship in the Department of Physics and Astronomy and the Center for Computational Statistics and Machine Learning (CSML) at University College London (UCL), to start October 2010.

The successful applicant will work in the area of gravitational cosmic shear, with an emphasis on new interdisciplinary approaches to the measurement of gravitational shear from images (e.g. GREAT08, GREAT10 and beyond) in collaboration with computer scientists, including UCL’s Center for Computational Statistics and Machine Learning (CSML) directed by Prof. John Shawe-Taylor. A background in gravitational lensing is not essential but candidates should have studied cosmology, computer science and/or statistical analysis. This is part of the five year Capitalising on Gravitational Shear (COGS) programme funded by the European Research Council (ERC), for which we are also in the process of appointing two postdoctoral positions. There is potential to apply the techniques developed to data including that from the Dark Energy Survey (DES) and simulations for the proposed Euclid ESA satellite.

Informal enquiries can be made to Dr Sarah Bridle (sarah(at)star.ucl.ac.uk), Prof. John Shawe-Taylor (jst(at)cs.ucl.ac.uk), Prof. Ofer Lahav (lahav(at)star.ucl.ac.uk) or Dr Fililpe Abdalla (fba(at)star.ucl.ac.uk).

Final phase of the Active Learning challenge (deadline March 3)

Six new final datasets are now available at:
http://clopinet.com/al

Register your team and take part in the challenge to win up to
USD 3200 and travel grants to attend the challenge workshop
after AISTATS (May 16, 2010) in Sardinia, Italy.
Or, submit a paper to WCCI 2010, Barcelona, Spain (deadline Feb. 7, hurry!)

No prior knowledge of active learning necessary. Learn on the spot, use classical “passive learning”, invent new strategies, and have fun!

The organizing team

Post-doctoral position in Machine Learning at LIF-Marseille, France

Post-doctoral position in Machine Learning
Laboratoire d’Informatique Fondamentale de Marseille
Université Aix-Marseille I

The machine learning group of the Laboratoire
d’Informatique Fondamentale de Marseille (LIF) at the University of
Aix-Marseille 1 is looking for a post-doctoral researcher to study
statistical machine learning methods for structured data.

The position is funded by the french National Agency of Research (ANR)
and is part of the Lampada project: Learning Algorithms, Models and
sPArse representations for structured DAta.
http://lampada.gforge.inria.fr/

This project has begun in 2010 and involves 4 machine learning groups
from Inria Lille Nord Europe, Laboratoire d’Informatique de Paris 6,
Laboratoire Hubert Curien de Saint-Etienne and the Laboratoire
d’Informatique Fondamentale de Marseille.

The candidate will investigate many statistical machine learning
methods for structured data among density estimation, kernel methods,
transfer learning, …

Some interactions with people working on Natural Language Processing
and Multimedia mining are also possible.

The subject of the work can be adapted to the profile of the
candidate.
The candidate must have a PhD in machine learning or related fields.

The position can start in September or October 2010 for 12 months.
Salary: 2100 euros per month (after taxes, medical insurance included).

Candidates should send a detailed CV, a letter of motivation and research
interests and the names of (at least) two references.

First interviews are planned for the end of April, in case of
interest, contact us as soon as possible.

Please send applications or requests in electronic form to:
– amaury.habrard(at)lif.univ-mrs.fr
– liva.ralaivola(at)lif.univ-mrs.fr

Call for Papers: Foundations and New Trends of PAC Bayesian Learning

Workshop:

Foundations and New Trends of PAC Bayesian Learning

University College London, UK

22 – 23 March 2010

http://www.cs.ucl.ac.uk/staff/rmartin/pacbayes/

CALL FOR PAPERS
Deadline: Friday, 12th February 2010

PAC-Bayes theory is a framework for deriving some of the tightest generalization bounds available. Many well established learning algorithms can be justified in the PAC-Bayes framework and even improved. PAC-Bayes bounds were originally applicable to classification, but over the last few years the theory has been extended to regression, density estimation, and problems with non iid data. The theory is well established within a small group of the statistical learning community, and has now matured to a level where it is relevant to a wider audience. The workshop will include tutorials on the foundations of the theory as well as recent findings through peer reviewed presentations.

Workshop topics

PAC Bayes theory or applications. In particular: application to:

* regression
* density estimation
* hypothesis testing
* structured density estimation
* non-iid data
* sequential data

The Invited Speakers include:

Olivier Catoni
CNRS U.M.R. 8553

David McAllester
Toyota Technological Institute at Chicago

Matthias Seeger
Saarland University and Max Planck Institute for Informatics

Organisers: Jean-Yves Audibert, Matthew Higgs, Steffen Grünewälder, François Laviolette and John Shawe-Taylor

Contact:
Steffen Grünewälder
steffen(at)cs.ucl.ac.uk

PhD (and postdoc) opportunities in Bristol

There are currently a number of opportunities if you are interested in research
at the Intelligent Systems Laboratory of the University of Bristol –
both at the postdoc level and PhD student level,
some for UK students and other for overseas. Deadlines are tight, so please read carefully.

Rsearch topics in the ISL range from statistical learning to web mining, including large scale
data mining and bioinformatics. My own group is mostly focussed on massive scale
pattern analysis, web mining, intelligent systems design, theoretical models of machine learning.

The University of Bristol is offering postgraduate scholarships (competitive)
(Home/EU and Overseas). The deadline for applications is 1st March 2010 for *both*.

http://www.bristol.ac.uk/studentfunding/overseas_pg/overseas_schols.html
http://www.bris.ac.uk/studentfunding/home_pg/schols.html

Separately, if you are interested in postdoc research in the areas listed above
Bristol could be the ideal place for this postdoc scheme:
fellowships starting in January 2011 – information is available at www.newtonfellowships.org . The deadline for applications is 8 February 2010.