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Pascal Exploration & Exploitation Challenge 2011 – Call for submissions

http://explo.cs.ucl.ac.uk/

Are you interested in online learning and website optimisation?

The Exploration & Exploitation Challenge, running under the auspices of the Pascal Network of Excellence and Adobe-Omniture, seeks to improve the relevance of content presented to visitors of a website, based on their individual interests. Participants will submit algorithms whose job will be to predict which visitors are likely to click on which piece of content, and to learn from experience. Submissions will be evaluated on how many clicks they get.

There is information to get started and a screencast at http://explo.cs.ucl.ac.uk/getting-started/ . Please contact us at http://explo.cs.ucl.ac.uk/app/contact if you need any help!

There will also be an ICML workshop on the topic of Exploration & Exploitation, on 2 July 2011, where the final results will be revealed and the winner will be declared. More information is available at http://explo.cs.ucl.ac.uk/workshop/ .

The organisers
Louis Dorard, John Shawe-Taylor, Suzanne Weller, Dorota Glowacka

PhD position in Computational Linguistics and Machine Learning, Saarland University

* Computational Linguistics and Machine Learning
* Saarland University, Saarbruecken, Germany
Cluster of Excellence “Multimodal Computing and Interaction”
* Position open from 1 September 2011
(earlier or later start dates are negotiable)
* Application deadline: 15 April 2011

PROJECT DESCRIPTION

Applications are invited for a PhD position in Computational Linguistics and
Machine Learning. The research will be focused on statistical methods for
inducing structured semantic representation using labelled and unlabelled
data, and different types of prior linguistic knowledge. The goal of this
project is to develop a wide-coverage semantic parser useful for a variety
of natural language processing tasks including question answering,
information extraction and textual entailment. The methods used in this
project will be based on recent advances in non-parametric Bayesian models
for structured prediction and techniques for injecting prior knowledge in
models with latent variables. The prospective student will be co-supervised
by Dr. Caroline Sporleder (http://www.coli.uni-saarland.de/~csporled/)
and Dr. Ivan Titov (http://people.mmci.uni-saarland.de/~titov/).
Additional linguistic expertise will be provided by Dr. Alexis Palmer
(http://comp.ling.utexas.edu/apalmer/).

SAARLAND UNIVERSITY

Saarland University is a European leader in Computer Science research and
teaching, and is especially renowned for its research in Computational
Linguistics and Natural Language Processing. The groups of Caroline
Sporleder and Ivan Titov are a part of the interdisciplinary MMCI Cluster of
Excellence formed in partnership with top-ranked research institutions
located on the Saarland University campus: Max Planck Institute for Computer
Science, Max Planck Institute for Software Systems and German Research
Centre for Artificial Intelligence (DFKI). Researchers come from all over
the world and the research language is English.

The city of Saarbruecken is in the south-west of Germany, close to
France and Luxembourg. Thanks to its proximity to France and its many
students who come from all over the world, it is a very international
city with a lot of French savoir-vivre. Both Paris and Frankfurt can
be reached by train in under two hours. Luxembourg, and the beautiful
French cities of Strasbourg, Nancy, and Metz are also very close by
(1-2 hours by train). The region around Saarbruecken is renowned for
its nature and its mild climate, which makes it an important
wine-growing region. It also boasts several UNESCO World Heritage
Sites (the Voelklinger Huette Ironworks, the Roman-founded city of
Trier, the historic centre of Strasbourg, and the Old Quarters and
Fortifications of Luxembourg).

REQUIREMENTS AND BENEFITS

The ideal candidate for the PhD position should have a masters degree in
computer science or computational linguistics. Some background in machine
learning and computational linguistics is desirable. Minimally, the
candidate should have prior exposure to statistical natural language
processing or strong background in machine learning. The candidate should
also have strong interest in linguistically-motivated statistical models of
language. An excellent academic record, analytical skills and a clear
aptitude for autonomous, creative research will be priority selection
criteria.

The candidates should have strong programming skills as well as excellent
verbal and written communication skills in English.

The salary and social benefits are according to Germany’s public sector TVL
E13 scale, approximately 40,000 Euro p.a. (before taxes), depending on
qualifications and professional experience. The candidate will have the
opportunity of pursuing a doctoral degree at Saarland University.

APPLICATION

The applications should include:

* CV
* brief statement of research interests
* 2-3 references (with email and phone number)
* academic transcript
* list of publications (if any)
* a sample of strongest publications or course work (e.g., Master thesis,
term papers): 1 – 2 papers
Applications (preferably, in a single PDF file) and inquiries should be
directed by email to: Ivan Titov, titov(at)mmci.uni-saarland.de

The position is open until filled. Applications received before April 15
will receive full consideration. The starting date is September 1, 2011 (a
later or earlier starting date is negotiable).

Saarland University wishes to increase the proportion of women in research
and strongly encourages qualified female candidates to apply. Priority will
be given to handicapped candidates with equivalent qualifications.

MORE INFORMATION

Cluster of Excellence MMCI: http://www.mmci.uni-saarland.de/
Saarland University: http://www.uni-saarland.de/en/
The city of Saarbruecken: http://www.saarbruecken.de/en/
The region: http://www.saarland.de/15229.htm

NIPS 2011 Call For Papers

Submissions are solicited for the Twenty-Fifth Annual Conference on Neural
Information Processing Systems, an interdisciplinary conference that brings
together researchers in all aspects of neural and statistical information
processing and computation, and their applications. The conference is a
highly selective, single track meeting that includes invited talks as well
as oral and poster presentations of refereed papers. Submissions by authors
who are new to NIPS are encouraged. In a switch from its previous Vancouver
venue, the 2011 conference will be held on December 13-15 in Granada,
Spain. One day of tutorials (December 12) will precede the main conference,
and two days of workshops (December 16-17) will follow it at the Sierra
Nevada ski resort.

Deadline for Paper Submissions: Thursday June 2, 2011, 23:59 Universal Time
(4:59pm Pacific Daylight Time). Submit at:
https://cmt.research.microsoft.com/NIPS2011/

Technical Areas: Papers are solicited in all areas of neural information
processing and statistical learning, including, but not limited to:

*Algorithms and Architectures: statistical learning algorithms, kernel
methods, graphical models, Gaussian processes, neural networks,
dimensionality reduction and manifold learning, model selection,
combinatorial optimization, relational and structured learning.

*Applications: innovative applications that use machine learning, including
systems for time series prediction, bioinformatics, systems biology,
text/web analysis, multimedia processing, and robotics.

*Brain Imaging: neuroimaging, cognitive neuroscience, EEG
(electroencephalogram), ERP (event related potentials), MEG
(magnetoencephalogram), fMRI (functional magnetic resonance imaging), brain
mapping, brain segmentation, brain computer interfaces.

*Cognitive Science and Artificial Intelligence: theoretical, computational,
or experimental studies of perception, psychophysics, human or animal
learning, memory, reasoning, problem solving, natural language processing,
and neuropsychology.

*Control and Reinforcement Learning: decision and control, exploration,
planning, navigation, Markov decision processes, game playing, multi-agent
coordination, computational models of classical and operant conditioning.

*Hardware Technologies: analog and digital VLSI, neuromorphic engineering,
computational sensors and actuators, microrobotics, bioMEMS, neural
prostheses, photonics, molecular and quantum computing.

*Learning Theory: generalization, regularization and model selection,
Bayesian learning, spaces of functions and kernels, statistical physics of
learning, online learning and competitive analysis, hardness of learning
and approximations, statistical theory, large deviations and asymptotic
analysis, information theory.

*Neuroscience: theoretical and experimental studies of processing and
transmission of information in biological neurons and networks, including
spike train generation, synaptic modulation, plasticity and adaptation.

*Speech and Signal Processing: recognition, coding, synthesis, denoising,
segmentation, source separation, auditory perception, psychoacoustics,
dynamical systems, recurrent networks, language models, dynamic and
temporal models.

*Visual Processing: biological and machine vision, image processing and
coding, segmentation, object detection and recognition, motion detection
and tracking, visual psychophysics, visual scene analysis and
interpretation.

Evaluation Criteria: Submissions will be refereed on the basis of technical
quality, novelty, potential impact, and clarity.

Submission Instructions: All submissions will be made electronically, in
PDF format. As in previous years, reviewing will be double-blind — the
reviewers will not know the identities of the authors. Papers are limited
to eight pages, including figures and tables, in the NIPS style. An
additional ninth page containing only cited references is allowed. Complete
submission and formatting instructions, including style files, are
available from the NIPS website, http://nips.cc.

Supplementary Material: Authors can submit up to 10 MB of material,
containing proofs, audio, images, video, or even data or source code. Note
that the reviewers and the program committee reserve the right to judge the
paper solely on the basis of the 9 pages of the paper; looking at any extra
material is up to the discretion of the reviewers and is not required.

Electronic submissions will be accepted until Thursday June 2, 2011, 23:59
Universal Time (4:59 pm Pacific Daylight Time). As was the case last year,
final papers will be due in advance of the conference.

Dual Submissions Policy: Submissions that are identical (or substantially
similar) to versions that have been previously published, or accepted for
publication, or during the NIPS review period are in submission to another
peer-reviewed and published venue are not appropriate for NIPS, with three
exceptions listed below. These exceptions, which have been approved by the
NIPS Foundation board in the interests of speeding up scientific
communication and improving the efficiency of peer review, are as follows:

1. Concurrent submission to other venues is acceptable provided that: (a) The
concurrent submission or intention to submit to other venues is declared to
all venues, (b) NIPS and the concurrent venues are given permission by the
author(s) to coordinate reviewing, and (c) acceptance to one venue imposes
withdrawal from all other venues with the exception stated in 2 below.

2. NIPS submissions that summarize a longer journal paper, whether published,
accepted, or in submission, are acceptable if the authors inform NIPS and
the journal and give them permission to coordinate reviewing.

3. It is acceptable to submit to NIPS 2011 work that has been made available
as a technical report (or similar, e.g. in arXiv) as long as the conditions
above are satisfied.

None of the above should be construed as overriding the requirements of
other publishing venues. In addition, keep in mind that author anonymity to
NIPS reviewers might be compromised for authors availing themselves of
exceptions 2 and 3.

Authors’ Responsibilities: If there are papers that may appear to violate
any of these conditions, it is the authors’ responsibility to (1) cite
these papers (preserving anonymity), (2) argue in the body of your paper
why your NIPS paper is non-trivially different from these concurrent
submissions, and (3) include anonymized versions of those papers in the
supplemental material.

Demonstrations and Workshops: There is a separate Demonstration track at
NIPS. Authors wishing to submit to the Demonstration track should consult
the Call for Demonstrations. The workshops will be held at the Sierra
Nevada ski resort December 16-17. The upcoming call for workshop proposals
will provide details.

Web URL: http://nips.cc/Conferences/2011/CallForPapers

Scientists piece together EU media structure

PASCAL2 researchers have been involved in a study which analyses how influential the media is in shaping the news agenda in EU Member States.

The researchers, led by Professor Nello Cristianini of University of Bristol, evaluated more than 1 million news articles in 22 languages to identify the factors that make this impact felt. The study, the first mega content analysis of cross-linguistic text using artificial intelligence tools, is presented in the journal PLoS ONE.

More details are available at http://cordis.europa.eu/fetch?CALLER=EN_NEWS_FP7&ACTION=D&DOC=1&CAT=NEWS&QUERY=012ccb4c3b95:66d7:5b8ca464&RCN=32852.

New Book Announcement

Learning with Support Vector Machines

by

Colin Campbell (University of Bristol) and Yiming Ying (University of Exeter)

Published in the Series:
Synthesis Lectures on Artificial Intelligence and Machine Learning
by
Morgan & Claypool Publishers, San Rafael, USA (February 15, 2011)
95 pages
ISBN-10: 1608456161
ISBN-13: 978-1608456161

Support Vectors Machines have become a well established tool within machine learning.
In this book we give a concise overview of this subject. We start with a simple Support
Vector Machine for performing binary classification before considering multi-class
classification and learning in the presence of noise. We show that this framework can
be extended to many other scenarios such as prediction with real-valued outputs,
novelty detection and the handling of complex output structures such as parse trees.
Finally, we give an overview of the main types of kernels which are used in practice and
how to learn and make predictions from multiple types of input data.

Four year post-doctoral position at Royal Holloway, UK

Seeking a talented researcher to do fundamental research on reinforcement learning, working with Chris Watkins. This position is part of the CompLACS project (Composing Learning for Artificial Cognitive Systems).

I am looking for someone who is fascinated by the deep questions of how to model the learning of complex behaviour and the development of intelligence, and who wants to break new ground and develop new approaches in this area. You should also be capable of implementing new algorithms on simulated and real application platforms.

There will be opportunities for collaboration with other consortium members.See here for details of the appointment and how to apply. The deadline is 28th March. I’d be happy to discuss this position informally: my email is chrisw (at) cs.rhul.ac.uk

Deadline 21 March: PhD studentships in Complex and Disordered Systems at King’s College London

Two PhD studentships (Graduate Teaching Assistantships) in Complex and Disordered Systems
King’s College London, Department of Mathematics

The Disordered Systems group at King’s College London expects to have several vacancies for entry into the PhD programme in autumn 2011. The group (see http://www.kcl.ac.uk/schools/nms/maths/research/dissys) has broad-ranging research interests in the application of tools from statistical mechanics to complex and disordered systems, including

– physics: soft matter (phase behaviour and flow), fracture and packing, non-equilibrium and glassy systems
– mathematics: sparse random matrix spectra, localization
– biology: metabolic and protein interaction networks, random graph ensembles, DNA stretching, survival statistics
– econophysics: collective effects in operational risk
– machine learning: learning and statistical inference on graphs

Two PhD studentships have just become available through a Graduate Teaching Assistantship (GTA) scheme, see http://www.kcl.ac.uk/nms/depts/mathematics/people/vacancies.aspx
Successful candidates will be able to gain teaching experience of up to 6 hours per week on average, and receive relevant training in teaching methods. They will receive a studentship payment of around GBP15,000 per year over three years as well as a fee waiver, i.e. they will not have to pay any course fees. This makes the GTAs attractive also to overseas students, for whom course fees can otherwise be substantial.

Interested applicants should contact lucy.ward(at)kcl.ac.uk as soon as possible. The application deadline is very soon, on 21 March 2011. Queries regarding research interests etc can be addressed to Prof Peter Sollich (peter.sollich(at)kcl.ac.uk) or any other member of the research group.

CVPR workshop on gesture recognition

CVPR Workshop on Gesture Recognition
and launching of a benchmark program
June 20, 2011
Colorado Springs, USA
http://clopinet.com/isabelle/Projects/CVPR2011/

We are organizing a workshop of gesture and sign language recognition from video data and still images. Gestures can originate from any body motion or state but commonly originate from the face or hand. Much recent research has focused on emotion recognition from face and hand gestures, with applications in gaming, marketing, and computer interfaces. Many approaches have been made using cameras and computer vision algorithms to interpret sign language for the deaf. However, the identification and recognition of postures and human behaviors is also the subject of gesture recognition techniques and has gained importance in applications such as video surveillance. This workshop aims at gathering researchers from different application domains working on gesture recognition to share algorithms and techniques.

Papers due: April 2, 2011

Invited speakers:
Aleix Martinez, Ohio State University, USA.
Greg Mori, Simon Fraser University, Canada.
Richard Bowden, Univ. Surrey, UK.
Graham Taylor, NYU, New-York.
David Forsyth, University of Illinois at Urbana-Champaign.
Sudeep Sarkar, University of South Florida.
Dimitri Metaxas, Rutgers University, New Jersey.
Maja Pantic, Imperial College, London.
Christian Vogler, Institute for Language and Speech Processing, Athens, Greece.
Takeo Kanade, Carnegie Mellon University.

Organizing committee:
Isabelle Guyon, Clopinet, Berkeley, California
Vassilis Athitsos, University of Texas at Arlington
Jitendra Malik, UC Berkeley, California
Ivan Laptev, INRIA, France

Manchester Centre for Doctoral Training

Please see below details of our new Centre for Doctoral Training – with Manchester’s strong
community in machine learning, computer vision, and pattern recognition, we will particularly
welcome applicants from the PASCAL network.

Centre for Doctoral Training in Computer Science – Join us!
===========================================================

Students considering PhD study are invited to apply to the new Manchester Centre for
Doctoral Training in Computer Science, the first of its kind in the UK.

http://cdt.cs.manchester.ac.uk/

The centre, funded by a £2.2 million grant from the UK Engineering and Physical Sciences
Research Council (EPSRC), will admit at least 75 students over the next five years.

Our mission is to train the computer scientists of the future, thorough offering a radically
different approach to the doctoral training process. Students admitted to the centre will be
given the chance to become “the complete researcher”, supported by funded studentships the
cover the full duration of the 4 year programme.

Programme Details
=================
Students will receive training in all aspects of the research process:
creativity and innovation, research problem solving in collaboration with industrial users and
carrying out research with real world impact.
At the same time students will complete significant research in collaboration with world-
leading academic staff.

The programme consists of an initial ‘foundation period’ of six months of taught
components, followed by a 3.5 year period of focused PhD level research, carried out under
the supervision of a PhD supervisor or team of supervisors. For further information about the
programme, please see details here:

http://cdt.cs.manchester.ac.uk/programme/

Studentships
============
Studentships pay home tuition fees and a full stipend for 4 years. We have over 15 fully-
funded studentships available for UK and EU students.
For further information regarding funding, please see our funding page:

http://cdt.cs.manchester.ac.uk/fees/funding/

Who should apply?
=================
We welcome applications from students with a First or Upper Second Class UK Honours
degree or equivalent in a relevant science or engineering discipline. Further information can
be obtained by e-mail:
cdt(at)cs.man.ac.uk
or telephone:
+44 (0)161 275 0699

Or by visiting our website: http://cdt.cs.manchester.ac.uk/

CDT Background
==============
The University won the grant despite strong competition from other leading computer science
departments across the UK. The awarding panel felt strongly that the proposal represented an
excellent opportunity to make a real impact to doctoral training in the UK.

Professor Steve Furber, director of the new centre, said: “We are delighted to have been
chosen by EPSRC for this flagship Centre for Doctoral Training in Computer Science, and
look forward to rising to the challenge of using this opportunity to transform computing
PhDs in the UK.”

ICML workshop on Unsupervised and Transfer Learning

ICML workshop on Unsupervised and Transfer Learning
July 2, 2011, Bellevue, Washington state, USA

and UTL Challenge (phase 2: transfer learning — $3000 in prizes)
http://clopinet.com/isabelle/Projects/ICML2011/

If you are working on transfer learning or kernel learning, you may want to consider entering the challenge we are currently running.
The goal is to demonstrate that you can learn data representations or kernels, which can be re-used across similar tasks.

We have an accepted workshop at ICML [entering the challenge is not a requisite to submit a paper to the workshop and vice versa].

– Challenge deadline: April 15
– Paper deadline: April 29
– Challenge website: http://clopinet.com/ul
– YouTube video (only 15 min) if you want to learn about transfer learning: http://www.youtube.com/watch?v=9ChVn3xVNDI
– The proceedings will be published in JMLR W&CP and as a book in the CiML series of Microtome publishing.