16th and 17th November 2011
BFI SouthBank, London
www.cvmp-conference.org
Submission deadline: *Extended to 30 June 2011*
Full call details available at http://www.pascal-network.org/docs/cvmp11.pdf
NIPS 2011 Registration is now open:
https://nips.cc/Register/
Early registration pricing expires after November 6, 2011
This year the tutorials and conference sessions are being held at the
Palacio de Exposiciones y Congresos (Congress Center) in Granada, Spain
The workshops are being held in the Sierra Nevada, just outside Granada.
Program highlights may be found here:
http://nips.cc/Conferences/2011/Program/
Because the conference is in a new country there have been important
changes. Please read more at this URL:
http://nips.cc/ConferenceInformation/ChangeLog
For travel support information:
http://nips.cc/ConferenceInformation/TravelSupport
For volunteering information:
http://nips.cc/ConferenceInformation/Volunteering
Additional conference information can be found here:
http://nips.cc/ConferenceInformation/
We look forward to seeing you in Spain!
[General Information]
The ACM KDD-2012 organizing committee solicits proposals for workshops
to be held in conjunction with the main conference. Each workshop will
take place on August 12, 2012 (tentatively) and will either be a
full-day or a half-day workshop. The purpose of a workshop is to provide
an excellent opportunity for participants from academia, industry,
government and other related parties to present and discuss novel ideas
on current and emerging topics relevant to knowledge discovery and data
mining.
Each workshop should be organized under a well-defined basis focusing on
emerging research areas, challenging problems and
industrial/governmental applications. Organizers have free controls on
the format, style as well as building blocks of the workshop. Possible
contents of a workshop include but are not limited to invited talks,
regular papers/posters, panels, and other pragmatic alternatives. In
case workshop proposers need extra time to prepare their workshop, early
decisions may be considered if justified.
Organizers of accepted workshops are expected to announce the workshop
and disseminate call for papers, maintain the workshop website, gather
submissions, conduct the reviewing process and decide upon the final
workshop program. They are also required to prepare an informal set of
workshop proceedings to be distributed with the registration materials
at the main conference, with a proceedings format template provided by
KDD 2012. Workshop organizers may choose to form organizing or program
committees aiming to accomplish these tasks successfully.
[Important Dates]
· Workshop proposals due: January 15, 2012
· Notification of decision: March 5, 2012
· Suggested Workshop Paper Submission deadline: May 8, 2012
· Suggested Workshop Final Paper Due: June 18, 2012
· Workshop Proceedings Due: June 25, 2012
[Proposal Details]
Workshop proposals should be no more than 8 pages and must include the
following parts:
· Outline: abstract, topic(s), objectives, relevance, and expected
outcomes
· Motivation: why is SIGKDD workshop on this topic relevant at
this particular time
· Description of the target group(s) of attendees and anticipated
number of participants
· Potential list of invited speakers (if any)
· Preliminary list of core program committee members
· Duration of the workshop (full-day or half-day)
· For workshops previously held at KDD or other conferences,
details on venue, attendance and number of submissions/accepted papers
from past editions
· For new workshops, a list of possible attendees/submissions
and/or a justification of the expected attendees/submissions
· Short bio as well as contact information (address, email, and
phone) for each organizer
· A designated contact person
Proposers are encouraged to have their drafts reviewed by potential
workshop participants before submission.
Workshop proposals should be submitted by January 15, 2011 at the
following url:
https://www.easychair.org/conferences/?conf=kdd2012ws
[Notes]
The ACM KDD-2012 organizing committee grants each accepted workshop with
one FREE conference registration, which can be offered to one of the
workshop organizers themselves or the workshop invited speakers. The
workshops are also encouraged to seek their own sponsors. Workshop
organizers are not allowed to publish more than two papers in their own
workshop. At least one author of each accepted paper is required to
register to the workshop and present their work.
[Workshop Co-Chairs]
· Zhi-Hua Zhou, Nanjing University
· Sofus A. Macskassy, ISI/USC
Contact info: workshops(at)kdd2012.com
Research Area: Low-Rank Matrix Recovery and Approximation, Sparse Coding.
Project Description: Applications are invited for an open Postdoctoral
Research Scientist position at SUNY at Buffalo, Department of
Computer Science and Engineering, in the area of
machine learning. Qualified candidates must have a Ph.D. in
machine learning or related areas with
outstanding research record and experience. The grant support will be
3 years. Successful candidates will conduct basic research and
interact with the principal investigator, graduate students, and
collaborators. The Computer Science department at SUNY Buffalo is among
the oldest CS departments nationwide with a strong focus on computer
vision and machine learning.
See http://www.cse.buffalo.edu/ for more information.
Salary is sufficiently competitive. If you are interested in joining
this research project as a Postdoctoral Fellow, please contact:
Yun (Raymond) Fu, Principal Investigator
Department of Computer Science and Engineering
State University of New York (SUNY) at Buffalo
201 Bell Hall Buffalo, NY 14260-2000, USA
Ph: +1 (716) 645 2670
Email: yunfu(at)buffalo.edu
Web: http://www.cse.buffalo.edu/~yunfu/
New Frontiers in Model Order Selection
NIPS-2011 Workshop, Granada, Spain,
December 16, 2011
http://people.kyb.tuebingen.mpg.de/seldin/fimos.html
UPDATE
New deadline: October 24!
We are glad to announce that we will provide travel support for workshop contributors (sponsored by PASCAL2). See the workshop page for details.
DESCRIPTION
Model order selection, which is a trade-off between model complexity and its empirical data fit, is one of the fundamental questions in machine learning. It was studied in detail in the context of supervised learning with i.i.d. samples, but received relatively little attention beyond this domain. The goal of our workshop is to raise attention to the question of model order selection in other domains, share ideas and approaches between the domains, and identify perspective directions for future research. Our interest covers ways of defining model complexity in different domains, examples of practical problems, where intelligent model order selection yields advantage over simplistic approaches, and new theoretical tools for analysis of model order selection. The domains of interest span over all problems that cannot be directly mapped to supervised learning with i.i.d. samples, including, but not limited to, reinforcement learning, active learning, learning with delayed, partial, or indirect feedback, and learning with submodular functions.
An example of first steps in defining complexity of models in reinforcement learning, applying trade-off between model complexity and empirical performance, and analyzing it can be found in [1-4]. An intriguing research direction coming out of these works is simultaneous analysis of exploration-exploitation and model order selection trade-offs. Such an analysis enables to design and analyze models that adapt their complexity as they continue to explore and observe new data. Potential practical applications of such models include contextual bandits (for example, in personalization of recommendations on the web [5]) and Markov decision processes.
References:
[1] N. Tishby, D. Polani. “Information Theory of Decisions and Actions”, Perception-Reason-Action Cycle: Models, Algorithms and Systems, 2010.
[2] J. Asmuth, L. Li, M. L. Littman, A. Nouri, D. Wingate, “A Bayesian Sampling Approach to Exploration in Reinforcement Learning”, UAI, 2009.
[3] N. Srinivas, A. Krause, S. M. Kakade, M. Seeger, “Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design”, ICML, 2010.
[4] Y. Seldin, N. Cesa-Bianchi, F. Laviolette, P. Auer, J. Shawe-Taylor, J. Peters, “PAC-Bayesian Analysis of the Exploration-Exploitation Trade-off”, ICML-2011 workshop on online trading of exploration and exploitation.
[5] A. Beygelzimer, J. Langford, L. Li, L. Reyzin, R. Schapire, “Contextual Bandit Algorithms with Supervised Learning Guarantees”, AISTATS, 2011.
CALL FOR ABSTRACTS
We invite submission of abstracts to the workshop. Abstracts should be at most 4 pages long in the NIPS format (appendices are allowed, but the organizers reserve the right to evaluate submissions based on the first 4 pages only). Selected abstracts will be presented as posters during the workshop. Submissions should be sent by email to seldin at tuebingen dot mpg dot de.
IMPORTANT DATES
Submission Deadline: October 24, 8:00am GMT.
Notification of Acceptance: October 15.
INVITED SPEAKERS
Shie Mannor (tentative)
Sanjoy Dasgupta (tentative)
John Langford (tentative)
Naftali Tishby (tentative)
Peter Auer (tentative)
ORGANIZERS
Yevgeny Seldin, Max Planck Institute for Intelligent Systems
Koby Crammer, Technion
Nicolò Cesa-Bianchi, University of Milano
François Laviolette, Université Laval
John Shawe-Taylor, University College London
Faculty of Engineering
University of Sheffield – Department of Computer Science
Job Reference Number: UOS003396
Contract Type: Fixed-term for up to 36 months
Salary: Grade 7 £28,251 – £35,788 per annum.
Closing Date: 3 November 2011
Further details and online application: http://jobs.ac.uk/job/ADI311/
Summary:
The objective of this research project is to develop new machine learning techniques for predictive modelling of financial and political indices using text from social media sources (e.g., Twitter, Facebook and blogs). The project will develop algorithms for modelling the correlations between streaming social media data and the movement of various indices, viewed as a time-series. This problem presents unique challenges, both in terms of learning algorithms and in terms of efficient development for deployment in a real-time setting.
The appointee will work on the machine learning components of the project, namely developing new statistical models for our time-series data, and associated algorithms for training and prediction. The appointee will be responsible for developing new efficient algorithms for Bayesian inference. A central focus of the role will be developing fast online algorithms suitable for real-time application. The role will require strong programming skills, particularly for cluster and cloud computing infrastructure (e.g., MapReduce, Amazon EC2) or GPU computing (e.g., CUDA).
This is an opportunity to work in a well-connected international team with world-leading reputations in both the Natural Language Processing (NLP) and Machine Learning (ML) research groups at The University of Sheffield. The NLP group is well known internationally for its research, and is one of the largest research groups in computational linguistics and text engineering in the UK. The ML group is also very well respected, with expertise in fundamental machine learning and a range of application domains.
Candidates must have a PhD and a strong publication record in a relevant discipline. Solid knowledge of machine learning and natural language processing is required, as is excellent programming ability. The candidate should also have experience in one or more of the following areas: time-series modelling, dimensionality reduction/clustering, statistical machine translation, probabilistic graphical models, Markov Chain Monte Carlo and reinforcement learning.
This post is fixed-term for up to 36 months.
Researcher Position in Machine Learning for Neuroscience is available in the Neuroinformatics Laboratory (NiLab) at Fondazione Bruno Kessler
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 – nilab.fbk.eu
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@fbk.eu.
Emails should have Ref.Code: NILAB_2011
This is to announce the “Machine Learning for Neuroimaging” Workshop that will be held on Nov. 8 and 9, 2011 in Marseille, France.
The main goal of this workshop is to bring together people from the machine learning community and people from the neuroimaging community that are keen to discuss their expertises. Potential outcomes to this workshop are for instance: the formal/machine learning setting of common problems in neuroimaging, the identification of new problems that can be readily tackled using machine learning techniques, the creation of new collaborations. It is also expected that discussions will build around important challenges of machine learning posed by neuroimaging data such as feature selection in presence of few data, transfer learning, structured prediction…
Further details can be found here:
http://mlni2011.sciencesconf.org/
** Important note ** : registration is free but limited to the first 40 participants; registration will be closed on Oct. 25, 2011.
Invited speakers: John Ashburner (FIL / UCL, London, UK), Francis Bach (Sierra / Inria, Paris, France), Olivier Colliot (Cogimage / CRICM, Paris, France), Edouard Duchesnay (LNAO / Neurospin, Gif sur Yvette, France), Thomas Gärtner (KDML / Universität Bonn, Bonn, Germany), Arthur Gretton (Gatsby / UCL, London, UK), Janaina Mourao-Miranda (UCL, London, UK), Alain Rakotomamonjy (LITIS / Université de Rouen, Rouen, France), Jonas Richiardi (EPFL, Lausanne, Switzerland), Marie Szafranski (ENSIIE, IBISC, Evry, France), Sylvain Takerkart (INCM / INT, Marseille, France), Bertrand Thirion (Parietal / Inria, Gif sur Yvette, France), Jean-Philippe Vert (Institut Curie / Mines ParisTech, Paris, France), Marcel Van Gerven (Donders Institute, Nijmegen, The Netherlands).
Looking forward to see you in Marseille
The Machine Learning for Document Access and Translation group of the
Xerox Research Centre Europe conducts research in Statistical Machine
Translation and Information Retrieval, Categorization and Clustering
using advanced machine learning methods.
We are opening two positions for researchers with a background in
Statistical Machine Translation to support a combination of internal
and EU-funded projects.
Required experience and qualifications:
– PhD in computer science or computational linguistics with focus on
SMT or statistical NLP.
– Good publication record and evidence of implementing systems.
– A good command of English, as well as open-mindedness and the will
to collaborate within a team.
Preferred starting date: January 2012
Contract duration: 18 months
Application instructions
Please email your CV and covering letter, with message subject
“Statistical Machine Translation Researcher” to xrce-candidates and to
Nicola.Cancedda at xrce.xerox.com. Inquiries can be sent to
Nicola.Cancedda at xrce.xerox.com.
XRCE is a highly innovative place and we strongly encourage
publication and interaction with the scientific community.
Job announcement URLs:
http://www.xrce.xerox.com/About-XRCE/Career-opportunities/Researcher-Statistical-Machine-Translation
http://www.xrce.xerox.com/About-XRCE/Career-opportunities/Researcher-Statistical-Machine-Translation2
The newly established Learning and Adaptive Systems group at ETH
Zurich ( http://las.ethz.ch/ ), led by Andreas Krause, has an open
position for a postdoctoral scholar. The project involves large scale
active learning and sequential decision making based on
high-dimensional data, and will be carried out in collaboration with
researchers at the California Institute of Technology.
Applicants should have finished, or be about to finish their Ph.D.
degrees. They must have an exceptional background in machine learning
or optimization. Successful candidates need to have a strong track
record of publications at top machine learning, AI or theory
conferences (NIPS, ICML, COLT, AISTATS, AAAI, IJCAI, …) and/or
premier journals in the area (JMLR, JAIR, PAMI, …), and have
experience in at least one of the following areas:
– Active learning
– Online learning / bandits
– Combinatorial optimization / approximation algorithms
– Application of discrete optimization in ML and computer vision
– Sequential decision making under uncertainty / stochastic optimal control
– High dimensional statistics
– Algorithms for large scale probabilistic inference
The initial appointment is for 12 months, with possible extensions up
to 3 years.
Working language at ETH Zurich is English — German is not required.
The salary is highly competitive (among the highest in Europe).
Applicants are requested to send their
– CV incl. publication list
– two strongest publications relevant to this position
– contact information for three recommenders
to Andreas Krause ( krausea(at)ethz.ch ). Review of applications will
start immediately, and continue until the position is filled.
