NIPS 2011 Registration Is Open

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!

KDD-2012 CALL FOR WORKSHOPS

[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

Machine Learning Postdoc position

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/

Travel Support: New Frontiers in Model Order Selection — NIPS-2011 Workshop — Call for Abstracts

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

Research Associate in Natural Language Processing and Machine Learning

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

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

[MLNI2011] Call for participation to the “Machine Learning for NeuroImaging Workshop”

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

Xerox – Researcher, Statistical Machine Translation (2 positions)

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

Postdoctoral scholar position in Machine Learning and Optimization at ETH Zurich

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.

PhD student position in statistical machine learning, exploratory data analysis, visualization, and multi-task learning (Helsinki, Finland)

Aalto University School of Science invites applications for a

doctoral student / research assistant

position for a fixed term (initially 1 year, extension possible).
Start date: 14 November 2011 (negotiable).
Application deadline: 31 October 2011, 3pm Finnish local time.

You will develop advanced machine learning methods for nonlinear dimensionality reduction, visualization, and exploratory data analysis with multiple data sources. You will create novel probabilistic models for the structure and dependencies within and between multiple partly related data sets. The developed methods will be used in bioinformatics, neuroinformatics, analysis of structured data like graphs, and other applications. We have recently developed novel frameworks for visualization from an information retrieval perspective, and for multitask learning in asymmetric scenarios; your work will build on and extend these research lines.

Research Site:

The position is at the Department of Information and Computer Science, Aalto University School of Science (previously Helsinki University of Technology). 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 the Finnish Centre of Excellence in Adaptive Informatics Research (AIRC) and part of the Finnish Centre of Excellence in Algorithmic Data Analysis Research, and will host part of the new Finnish Centre of Excellence in Computational Inference Research (COIN) which starts in 2012. The depart!
ment was ranked among the top two departments of Aalto University in the Research Assessment Exercise 2009.

The position is located in the Statistical Machine Learning and Bioinformatics research group at the Department. The group is part of Helsinki Institute for Information Technology HIIT in Aalto University. The group is a member of the Finnish Centre of Excellence in Adaptive Informatics Research (AIRC) and the new Finnish Centre of Excellence in Computational Inference Research (COIN), and is also a member in the EU PASCAL2 network of excellence. The work will be supervised by the principal investigator of the project, academy research fellow Jaakko Peltonen. The work involves collaboration with Finnish researchers including members of the Statistical Machine Learning and Bioinformatics research group led by Prof. Samuel Kaski, and international researchers from the UK, Belgium, and the USA.

The research site is located on the Aalto University campus in Otaniemi, a short bus ride away from the centre of Finland’s capital Helsinki. Helsinki and the capital area are a great place to live, with numerous local attractions and events, scenic landscapes of small forests, islands and urban areas, a high standard of living, and excellent travel connections. Helsinki is the World Design Capital in 2012.

Department website: http://ics.tkk.fi/en/
HIIT website: http://www.hiit.fi/
Research group website: http://research.ics.tkk.fi/mi/
Principal investigator website: http://users.ics.tkk.fi/jtpelto/

Required and Desired Qualities of the Applicant:

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 probabilistic modeling and/or machine learning are necessary.
– An interest in some of the following topics is essential: dimensionality reduction, manifold learning, visualization, and multi-task learning. Experience in these topics is an advantage.
– A strong study record and strong track record in research are advantages.
– Good programming skills in languages such as C/C++/Matlab/R/Python and good written and spoken communication skills are desired.

Contract Details:

The salary will be determined based on the Aalto University salary system (2200-3200 euro per month before tax for a doctoral student depending on qualifications and performance). The initial appointment will be for one year. Extension will be possible depending on the availability of funding.

Application Procedure:

The application deadline is 31 October 2011, 3pm Finnish local time. The application materials must include a curriculum vitae, a copy of study records, contact details of at least two references, and any other materials deemed relevant. Applications must be submitted to the Registry of Aalto University, no later than the deadline. Applications must be submitted preferably by email to kirjaamo@aalto.fi or alternatively by physical mail to The Registry of Aalto University, Aalto University, P.O.Box 11000, FI-00076 Aalto, Finland (street address Otakaari 1, Espoo).

Candidates may be asked for an interview at Aalto University or via phone or Skype.

For additional information, please contact academy research fellow Jaakko Peltonen or HR Coordinator Stefan Ehrstedt. E-mail: firstname.lastname@aalto.fi
See also this ad online: http://www.aalto.fi/en/current/jobs/teaching_and_research/doctoral_student-research_assistant/