NIPS’11 Workshop on Choice Models and Preference Learning
Submission are invited for the NIPS’11 Workshop on Choice Models and Preference Learning.
Details can be found via: https://sites.google.com/site/cmplnips11/
Submission are invited for the NIPS’11 Workshop on Choice Models and Preference Learning.
Details can be found via: https://sites.google.com/site/cmplnips11/
PhD studentship in developing novel probabilistic modelling and
statistical inference methodology and applying these methods to
problems in computational systems biology
Department of Information and Computer Science, Aalto University
School of Science (previously Helsinki University of Technology,
http://ics.tkk.fi/en/)
Aalto University School of Science invites applications for a
doctoral student / research assistant
position for a fixed term starting 1 October 2011.
The position is located in the Department of Information and Computer
Science and Helsinki Institute for Information Technology HIIT
Statistical Machine Learning and Bioinformatics research group at the
Aalto School of Science. The focus of the Department’s research and
teaching activity is on advanced computational methods for modelling,
analysing, and solving complex tasks in technology and science. The
research aims at the development of fundamental computer science
methods for the analysis of large and high-dimensional data sets, and
for the modelling and design of complex software, networking and other
computational systems. The department employs approximately 150 people
and operates with a total annual budget of approximately 9 MEUR. The
department hosts two national Centres of Excellence and was ranked
among the top two departments of Aalto University in the Research
Assessment Exercise 2009.
The doctoral student will develop novel probabilistic modelling and
statistical inference methodology and apply these methods to problems
in computational systems biology. The position is related to the
inter-disciplinary European project on Systems approaches to gene
regulation biology through nuclear receptors (SYNERGY), which has been
funded under the ERASysBio+ initiative. The work will take place in
the group of Dr Antti Honkela but it will involve close collaboration
with other project partners, especially Profs. Magnus Rattray and Neil
D. Lawrence (University of Sheffield, UK).
A successful applicant must have a MSc degree in computer science,
electrical engineering, mathematics, physics, or a related field. It
is also possible to start as a research assistant working on one’s
Master’s thesis. A strong mathematical background and an interest in
Bayesian modeling and/or machine learning are necessary. An interest
in computational biology is essential but no prior experience is
necessary.
The application deadline is 13 September 2011.
For more details and application instructions, see
http://www.aalto.fi/en/current/jobs/teaching_and_research/doctoral_student/
NIPS 2011 Workshop
Melia Sierra Nevada & Melia Sol y Nieve, Sierra Nevada, Spain.
Saturday December 17, 2011.
http://learningsemanticsnips2011.wordpress.com
OVERVIEW
A key ambition of AI is to render computers able to evolve in and
interact with the real world. This can be made possible only if the
machine is able to produce a correct interpretation of its available
modalities (image, audio, text, …), upon which it would then build a
reasoning to take appropriate actions. Computational linguists use the
term “semantics” to refer to the possible interpretations (concepts)
of natural language expressions, and showed some interest in “learning
semantics”, that is finding (in an automated way) these
interpretations. However, “semantics” are not restricted to natural
language modality, and are also pertinent for speech or vision
modalities. Hence, knowing visual concepts and common relationships
between them would certainly bring a leap forward in scene analysis
and in image parsing akin to the improvement that language phrase
interpretations would bring to data mining, information extraction or
automatic translation, to name a few.
Progress in learning semantics has been slow mainly because this
involves sophisticated models which are hard to train, especially
since they seem to require large quantities of precisely annotated
training data. However, recent advances in learning with weak and
limited supervision lead to the emergence of a new body of research in
semantics based on multi-task/transfer learning, on learning with
semi/ambiguous supervision or even with no supervision at all.
The goal of this workshop is to explore these new directions and,
in particular, to investigate the following questions:
* How should meaning representations be structured to be easily
interpretable by a computer and still express rich and complex knowledge?
* What is a realistic supervision setting for learning semantics? How
can we learn sophisticated representations with limited supervision?
* How can we jointly infer semantics from several modalities?
INVITED SPEAKERS (confirmed)
Chris Burges – Microsoft
Pedro Domingos – University of Washington
Derek Hoiem – UIUC
Raymond Mooney – UT at Austin
Richard Socher – Stanford University
Josh Tenenbaum – MIT
DATES
– Submission deadline: 23:59 EST, Monday, September 26, 2011.
– Acceptance notification: Friday, October 21, 2011.
– Workshop date: Saturday, December 17, 2011.
SUBMISSION
We solicit submission of abstracts to the workshop. Abstracts should
be at most 2 pages long in the NIPS format (excluding references).
Selected abstracts will be presented as posters during a morning and
an afternoon sessions. Submissions should be sent by email to
antoine.bordes(at)hds.utc.fr .
Abstracts should be sent no later than 23:59 EST, Monday, September 26, 2011.
ORGANIZERS
Antoine Bordes
Jason Weston
Ronan Collobert
Léon Bottou
Submissions are invited for the NIPS workshop Relating Machine Learning Problems – An Approach to Unify the Field.
Details on the workshop website: http://rml.anu.edu.au
CALL FOR PARTICIPATION
SIMBAD 2011
1st International Workshop on Similarity-Based Pattern Analysis and Recognition
28-30 September, 2011
Venice, Italy
http://www.dsi.unive.it/~simbad
(Deadline for Early Registration: 20 September 2011)
MOTIVATIONS AND OBJECTIVES
Traditional pattern recognition techniques are intimately linked to
the notion of “feature spaces.” Adopting this view, each object is
described in terms of a vector of numerical attributes and is
therefore mapped to a point in a Euclidean (geometric) vector space so
that the distances between the points reflect the observed
(dis)similarities between the respective objects. This kind of
representation is attractive because geometric spaces offer powerful
analytical as well as computational tools that are simply not
available in other representations. Indeed, classical pattern
recognition methods are tightly related to geometrical concepts and
numerous powerful tools have been developed during the last few
decades, starting from the maximum likelihood method in the 1920’s, to
perceptrons in the 1960’s, to kernel machines in the 1990’s.
However, the geometric approach suffers from a major intrinsic
limitation, which concerns the representational power of vectorial,
feature-based descriptions. In fact, there are numerous application
domains where either it is not possible to find satisfactory features
or they are inefficient for learning purposes. This modeling
difficulty typically occurs in cases when experts cannot define
features in a straightforward way (e.g., protein descriptors vs.
alignments), when data are high dimensional (e.g., images), when
features consist of both numerical and categorical variables (e.g.,
person data, like weight, sex, eye color, etc.), and in the presence
of missing or inhomogeneous data. But, probably, this situation arises
most commonly when objects are described in terms of structural
properties, such as parts and relations between parts, as is the case
in shape recognition.
In the last few years, interest around purely similarity-based
techniques has grown considerably. For example, within the supervised
learning paradigm (where expert-labeled training data is assumed to be
available) the well-established kernel-based methods shift the focus
from the choice of an appropriate set of features to the choice of a
suitable kernel, which is related to object similarities. However,
this shift of focus is only partial, as the classical interpretation
of the notion of a kernel is that it provides an implicit
transformation of the feature space rather than a purely
similarity-based representation. Similarly, in the unsupervised
domain, there has been an increasing interest around pairwise or even
multiway algorithms, such as spectral and graph-theoretic clustering
methods, which avoid the use of features altogether.
By departing from vector-space representations one is confronted with
the challenging problem of dealing with (dis)similarities that do not
necessarily possess the Euclidean behavior or not even obey the
requirements of a metric. The lack of the Euclidean and/or metric
properties undermines the very foundations of traditional pattern
recognition theories and algorithms, and poses totally new
theoretical/computational questions and challenges.
The workshop will mark the end of the EU FP7 Projects SIMBAD
(http://simbad-fp7.eu), which was devoted precisely to these themes,
and is a follow-up of the ICML 2010 Workshop on “Learning in
non-(geo)metric spaces” (http://www.dsi.unive.it/~icml2010lngs). Its
aim is to consolidate research efforts in this area, and to provide an
informal discussion forum for researchers and practitioners interested
in this important yet diverse subject. We will be covering a wide
range of problems and perspectives, from supervised to unsupervised
learning, from generative to discriminative models, and from
theoretical issues to real-world practical applications.
PROGRAM
The workshop will feature contributed talks and posters as well as
invited presentations by:
– Ulrike Hahn, Cardiff University, UK
– Marco Gori, University of Siena, Italy
– John Shawe-Taylor, University College London, UK
A detailed program can be found at:
http://www.dsi.unive.it/~simbad/index.php/pages/program
We feel that the more informal the better, and we would like to
solicit open and lively discussions and exchange of ideas from
researchers with different backgrounds and perspectives. Plenty of
time will be allocated to questions, discussions, and breaks.
The workshop is supported by PASCAL 2 and IAPR.
ORGANIZATION
Program Chairs
Marcello Pelillo, University of Venice, Italy
Edwin Hancock, University of York, UK
Steering Committee
Joachim Buhmann, ETH Zurich, Switzerland
Robert Duin, Delft University of Technology, The Netherlands
Mario Figueiredo, Technical University of Lisbon, Portugal
Edwin Hancock, University of York, UK
Vittorio Murino, University of Verona, Italy
Marcello Pelillo (chair), University of Venice, Italy
Program Committee
Maria-Florina Balcan, Georgia Institute of Technology, USA
Manuele Bicego, University of Verona, Italy
Joachim Buhmann, ETH Zurich, Switzerland
Horst Bunke, University of Bern, Switzerland
Tiberio Caetano, NICTA, Australia
Umberto Castellani, University of Verona, Italy
Luca Cazzanti, University of Washington, Seattle, USA
Nicolo’ Cesa-Bianchi, University of Milan, Italy
Robert Duin, Delft University of Technology, The Netherlands
Francisco Escolano, University of Alicante, Spain
Mario Figueiredo, Technical University of Lisbon, Portugal
Ana Fred, Technical University of Lisbon, Portugal
Bernard Haasdonk, University of Stuttgart, Germany
Edwin Hancock, University of York, UK
Anil Jain, Michigan State University, USA
Robert Krauthgamer, Weizmann Institute of Science, Israel
Marco Loog, Delft University of Technology, The Netherlands
Vittorio Murino, University of Verona, Italy
Elzbieta Pekalska, University of Manchester, UK
Marcello Pelillo, University of Venice, Italy
Massimiliano Pontil, University College London, UK
Antonio Robles-Kelly, NICTA, Australia
Volker Roth, University of Basel, Switzerland
Amnon Shashua, The Hebrew University of Jerusalem, Israel
Andrea Torsello, University of Venice, Italy
Richard Wilson, University of York, UK
Organization Committee
Samuel Rota Bulo’ (chair), University of Venice, Italy
Nicola Rebagliati, University of Venice, Italy
Furqan Aziz, University of York, UK
Luca Rossi, University of Venice, Italy
Teresa Scantamburlo, University of Venice, Italy
Computational Systems Biology and Bioinformatics (CSBB,
http://www.cs.helsinki.fi/group/sysfys/) research group at University of
Helsinki, Department of Computer Science, has an opening for a
POST-DOCTORAL RESEARCHER
in the field of machine learning for biological network inference. The
position is funded by the EU FP7 project “BIO knowLEDGE Extractor and
Modeller for Protein Production” (BIOLEDGE) as well as the National
Centre of Excellence in Algorithmic Data Analysis (ALGODAN), funded by
Academy of Finland.
We expect the applicants to have a PhD (or to have submitted a
dissertation for evaluation) in Computer Science, Statistics,
Computational Biology or related field, with excellent publication
record, as well as experience in one or more of the following fields:
– Kernel methods
– Machine learning for structured data
– Optimisation algorithms
– Large-scale data analysis
In addition, we value experience in the following:
– Biological network reconstruction
– Structured output prediction
– Gene function and interaction prediction
Excellent programming and technical skills, as well as excellent written
and oral communication skills are required.
The successful candidate will join the CSBB research group to develop
new machine learning methods and large-scale optimization algorithms for
biological function and interaction prediction within the BIOLEDGE
project (EU FP7, 2011-2015), a collaboration between Universities of
Helsinki, Cambridge, Malaga as well as VTT Technical Research Centre of
Finland and three SMEs.
Initially a 2-year contract will be offered. Salary is based on demand
level 5 of the salary system of University of Helsinki, corresponding to
monthly gross salary of 3151-3642 euro, depending on individual
performance. An extension of the contract up to 4 years is possible,
depending on performance and availability of funding.
Department of Computer Science, University of Helsinki
(http://www.cs.helsinki.fi/en/home) carries out basic and applied
research in computer science, and offers advanced teaching based on that
research. The department is top-ranked in its field in Finland and has
been elected a national centre of excellence in higher education. The
department offers an excellent environment for high-quality research,
with several world-class research groups in the area of algorithms,
machine learning and data mining. The department has a top-of-the-line
infrastructure, for example a new large-scale computation cluster of ca.
1900 computation cores. University of Helsinki is within the top-20
Universities in Europe. It is continuously rated within top-100 in the
world, e.g., according to http://www.arwu.org/ARWU2010.jsp.
Helsinki is named as number 1 city in Monocle`s annual Quality of Life
survey 2011.
Please send your application to Docent Juho Rousu at
(rekry.bioledge[at]cs.helsinki.fi). Please include CV, transcripts of
your studies, a statement of research interests, as well as names of
possible referees. Applications received by September 23, 2011 will
receive full consideration.
For further information about the position, please contact
Docent Juho Rousu
Department of Computer Science
P.O Box 68
00014 University of Helsinki
tel: +358 50 415 1702
email: firstname.lastname[at]cs.helsinki.fi
The Neuroinformatics Laboratory (NILab) is a joint initiative between Fondazione Bruno Kessler (FBK) and the Center for Mind and Brain Sciences (CIMeC) of the University of Trento in order to promote interdisciplinary research in cognitive neuroscience. Neuroinformatics stands at the intersection of neuroscience and information science and it provides methods and technologies for managing, analyzing, and modeling neuroimaging data.
The NILab mission spans from scientific to technological aspects. The scientific research activity covers the design and the development of novel methods for the integration, the analysis and the interpretation of unimodal and multimodal neuroimaging data. The laboratory is co-located with the Neuroimaging Laboratory (LNIF) of CIMeC, which provides several facilities for cognitive neuroscience investigations such as MR (4T Brucker Medspec Scanner), MEG/EEG (Electa Neuromag), TMS and EyeTracking.
The successful candidate will work on the development of computational methods for brain data analysis approaching challenging tasks such as brain decoding, brain mapping and brain connectivity.
For further information, please contact info.nilab(at)fbk.eu.
Due to the FBK’s attempt to promote equal opportunity and gender balance, in case of equal applications, female candidates will be given preference.
The ideal candidate should have:
* Ph.D. in Computer Science or related fields.
* Solid background in Machine Learning and Pattern Recognition.
* Outstanding publication record.
* At least basic knowledge of the neuroimaging techniques (fMRI, dMRI, MEG, EEG).
* Good skills in scientific programming with Python.
* Proficient English both written and spoken.
Additional requirements / desiderata:
* Attitude to work in a multidisciplinary environment.
* Ability to quickly learn and use new technologies and tools.
* Ability to acquire knowledge from different application domains.
Type of Contract: research position for a 3 years contract starting November 2011. The gross salary offer will range between € 37,800.00 and € 45,000.00, depending on seniority and expertise.
Useful Links:
Neuroinformatics Laboratory – http://nilab.fbk.eu/en/Home
Fondazione Bruno Kessler – www.fbk.eu
Center for Mind/Brain Sciences – www.cimec.unitn.it
To apply online please send your detailed CV with two references to jobs(at)fbk.eu.
Emails should have Ref.Code: NILAB_2011
This is your opportunity to study for a PhD at the Centre for Vision, Speech and Signal Processing, one of the UK ‘s premier research centres in Computer Vision. The studentship is available from Oct and covers both tuition fees and a maintenance grant for 3.5 years. The funding is available to UK or EU students.
Successful applicants will join an expanding research group within the Centre for Vision Speech and Signal Processing which has over 120 people working in vision, machine learning and related disciplines. It has an international reputation for the excellence of its research and, in the last Research Assessment Exercise; the Department (of Electronic Engineering) was rated as second in the country with the highest return of staff for any institution.
The project is collaboration between the University of Surrey (Prof Bowden), the University of Oxford (Prof Zisserman) and the University of Leeds (Dr Everingham). The objective of this project is to develop automated tools that allow temporal visual content, such as a human gesturing, using sign language, or interacting with objects or other humans, to be learnt from standard TV broadcast signals using the transmitted annotation in the form of subtitles, scripts and annotation for the visually impaired as supervision.
Candidates should hold a 1st or strong 2.1 honours degree or Masters degree or equivalent in a scientific discipline (e.g., Engineering, Physics, Mathematics or Computing), and should have good written/spoken English and demonstrate an aptitude for the research area. Prior experience in computer vision, image processing or machine learning would be advantageous.
The studentship includes tuition fees for UK or EU candidates and a tax free maintenance grant of £13,920 for 3.5 years.
The post will remain open until filled. For further information please contact Prof Richard Bowden. Applicants should send a CV and covering letter to Prof Bowden – r.bowden(at)surrey.ac.uk.
University Partner: Centre for Vision Speech and Signal Processing, University of Surrey
Industrial Partner: BAE Systems Detica
The funded EPSRC PhD studentship covers tuition fees and a maintenance grant of £13,590 per annum for 3.5 years. There is also an industrial contribution to the project of £8,500 including £5500 enhanced stipend to the student and funds to cover travel and equipment. Note that both grant and stipend are exempt from tax.
Cyber crime is ever increasing and the tools used in its execution are becoming ever more sophisticated. At the same time, the volume of data transported by networks and requiring analysis is growing rapidly. Analysts must constantly refine and evolve methods of detection. This project will combine rule identification, data mining and machine learning with interactive data visualisation. It is believed that only through the unique combination of these tools can efficient methods for data exploration and behaviour identification be created. The visualisation will not only provide the output of learning but, through interactive manipulation, also form the input or control to the embedded machine learning tools.
Both the Centre for Vision Speech and Signal Processing and BAE Systems Detica’s cyber operations are located in the Guildford area, allowing easy collaboration between the two sites. The student will commence their studies with a 4-6 week placement at BAE Systems Detica as soon as security clearance is obtained. Following this the student will be based primarily at the University but with regular meetings with the company. Further placements at BAE Systems Detica will be scheduled at the beginning of years 2 and 3 with the student hopefully joining the BAE Systems Detica team full time upon completion of his studies.
The Centre for Vision, Speech and Signal Processing is one of the UK’s premier research centres in Computer Vision with over 120 people working in vision, machine learning and related disciplines. It has an international reputation for the excellence of its research and, in the last Research Assessment Exercise; the Department (of Electronic Engineering) was rated as second in the country with the highest return of staff for any institution. The studentship is available from Oct and covers both tuition fees and a maintenance grant for 3.5 years plus a top up payment from the sponsoring company for 3 years. The funding is only available to UK students and they will need to obtain security clearance which will be sponsored by the company.
Candidates should hold a high-level (first class or 2:1) degree in a relevant engineering, software, mathematics or science subject, and should have strong GCSEs including As or Bs in English and Maths; as well as at least 3 A-levels at grade A and B (equating to 320 UCAS points) or equivalent. Prior experience in graphics, vision, datamining or machine learning would be advantageous. Only those with the permanent and unrestricted right to live and work in the UK will be considered for this position.
The post will remain open until filled. For further information please contact Prof Richard Bowden. Applicants should send a CV and covering letter to Prof Bowden – r.bowden(at)surrey.ac.uk.
AISTATS*2012 Call for Papers
Fifteenth International Conference on Artificial Intelligence and Statistics
April 21-23, 2012, La Palma, Canary Islands
http://www.aistats.org
AISTATS is an interdisciplinary gathering of researchers at the
intersection of computer science, artificial intelligence, machine
learning, statistics, and related areas. Since its inception in 1985,
the primary goal of AISTATS has been to broaden research in these
fields by promoting the exchange of ideas among them. We encourage the
submission of all papers which are in keeping with this objective at
http://www.aistats.org.
In the 2012 edition of AISTATS we are particularly encouraging
submissions with a focus on scientific data sets. Examples could
include astronomical data, biological data etc. These submissions have
been assigned the keywords “scientific data analysis”.
The Conference Programme will include invited talks, contributed talks,
and posters. Contributed talks and posters will be selected via a
rigorous double-blind peer-review process. Accepted papers will be
published in the AISTATS Conference Proceedings to be published as a
volume of JMLR Workshop and Conference Proceedings.
This year’s invited talks will be given by Sir David Cox (Oxford),
Gábor Lugosi (Pompeu Fabra) and Rien van de Weygaert (Groningen).
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.
Solicited topics include, but are not limited to:
* Bayesian models and estimation (graphical models, causality,
Gaussian processes, approximate inference, …)
* Non-Bayesian models and estimation (kernel methods, nonparametric
models, statistical and computational learning theory, manifolds and
embedding, sparsity and compressed sensing, …)
* Classification, regression, density estimation, unsupervised and
semi-supervised learning, clustering, topic models, …
* Structured prediction, relational learning, logic and probability
* Reinforcement learning, planning, control
* Game theory, no-regret learning, multi-agent systems
* Algorithms and architectures for high-performance computation in AI
and statistics
* Software for and applications of AI and statistics
For a more detailed list of keywords, see http://www.aistats.org/keywords.php.
Submission Requirements
Electronic submission of papers is required. Papers may be up to 8
double-column pages in length, excluding references; formatting and
submission information will be made available on the conference
website at aistats.org/submit.php.
Submissions will be considered if they are received by 23:59, October
14th, 2011, UTC. See the conference website for additional important
dates: http://www.aistats.org/dates.php.
All accepted papers will be presented at the Conference either as
contributed talks or as posters, and will be published in the AISTATS
Conference Proceedings. Papers for talks and posters will be treated
equally in publication.
Submitted manuscripts should not have been previously published in a
journal or in the proceedings of a conference, and should not be under
consideration for publication at another conference at any point
during the AISTATS review process. It is acceptable to have a
substantially extended version of the submitted paper under
consideration simultaneously for journal publication, so long as the
journal version’s planned publication date is in 2012 or later, the
journal submission does not interfere with AISTATS’s right to publish
the paper, and the situation is clearly described at the time of
AISTATS submission. Please describe the situation in the appropriate
box on the submission page (and do not include author information in
the submission itself, to avoid accidental unblinding).
Programme Chairs:
Mark Girolami, University College London
Neil Lawrence, University of Sheffield
General Chair:
Bernhard Schökopf, Max Planck Institute, Tübingen
Local Arrangements Chair:
Fernando Perez Cruz, University Carlos III, Madrid, Spain
Fundraising Chair:
Suvrit Sra, Max Planck Institute, Tübingen
Publicity Chair:
Kun Zhang, Max Planck Institute, Tübingen
Publications Chair:
Nicolò Fusi, University of Sheffield
Senior Program Committee:
Ryan P. Adams, Harvard University
Edo Airoldi, Harvard University
Yoshua Bengio, Université de Montréal
Matthew Blaschko, University of Oxford
David Blei, Princeton University
Guillaume Bouchard, Xerox Research Centre Europe
Nicolas Chopin, CREST
Koby Crammer, Technion University
Arnaud Doucet, University of Oxford
Nial Friel, University College, Dublin
Matthias Hein, Saarland University
Katherine Heller, MIT
Dirk Husmeier, Biomathematics and Statistics Scotland
Sayan Mukherjee, Duke University
Brendan Murphy, University College, Dublin
Christian Robert, Université de Paris, Dauphine
Dan Roy, University of Cambridge
Ricardo Silva, University College, London
Oliver Stegle, Max Planck Institute, Tübingen
Charles Sutton, University of Edinburgh
Bill Triggs, CNRS
Aki Vehtari, Aalto University
Jean-Phillipe Vert, Mines ParisTech
Hao Helen Zhang, North Carolina State University
