Call for Papers – KDD2012

CALL FOR PAPERS
18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2012)
August 12-16, 2012
Beijing, China

http://www.kdd.org/kdd2012/

Key Dates:
Papers due: February 10, 2012
Acceptance notification: May 4, 2012

Paper submission and reviewing will be handled electronically. Authors should consult the conference Web site for full details regarding paper preparation and submission guidelines.

Papers submitted to KDD 2012 should be original work and substantively different from papers that have been previously published or are under review in a journal or another conference/workshop.

As per KDD tradition, reviews are not double-blind, and author names and affiliations should be listed.

Due to the large number of submissions, papers submitted to the research track will not be considered for publication in the industry/government track and vice-versa. Authors are encouraged to carefully read the conference CFP and choose an appropriate track for their submissions. In case of doubts, authors are encouraged to get in touch with the chairs of the corresponding track at least a week before the submission deadline.

RESEARCH TRACK

We invite submission of papers describing innovative research on all aspects of knowledge discovery and data mining. Examples of topic of interest include (but are not limited to): association analysis, classification and regression methods, semi-supervised learning, clustering, factorization, transfer and multi-task learning, feature selection, social networks, mining of graph data, temporal and spatial data analysis, scalability, privacy, security, visualization, text analysis, Web mining, mining mobile data, recommender systems, bioinformatics, e-commerce, online advertising, anomaly detection, and knowledge discovery from big data, including the data on the cloud. Papers emphasizing theoretical foundations, novel modeling and algorithmic approaches to specific data mining problems in scientific, business, medical, and engineering applications are particularly encouraged. We welcome submissions by authors who are new to the KDD conference, as well as visionary papers on new and emerging topics. Authors are explicitly discouraged from submitting papers that contain only incremental results and that do not provide significant advances over existing approaches. Application oriented papers that make innovative technical contributions to research are welcome.

Submitted papers will be assessed based on their novelty, technical quality, potential impact, and clarity of writing. For papers that rely heavily on empirical evaluations, the experimental methods and results should be clear, well executed, and repeatable. Authors are strongly encouraged to make data and code publicly available whenever possible.

INDUSTRY & GOVERNMENT TRACK

The Industrial/Government Applications Track solicits papers describing implementations of KDD solutions relevant to industrial or government settings. The primary emphasis is on papers that advance the understanding of practical, applied, or pragmatic issues related to the use of KDD technologies in industry and government and highlight new research challenges arising from attempts to create such real KDD applications. Applications can be in any field including, but not limited to: e-commerce, medical and pharmaceutical, defense, public policy, finance, engineering, environment, manufacturing, telecommunications, and government.

The Industrial/Government Applications Track will consist of competitively-selected contributed papers. Submitters must clearly identify in which of the following three sub-areas their paper should be evaluated as distinct review criteria will be used to evaluate each category of submission.

· Deployed KDD systems that are providing real value to industry, Government, or other organizations or professions. These deployed systems could support ongoing knowledge discovery or could be applications that employ discovered knowledge, or some combination of the two.

· Discoveries of knowledge with demonstrable value to Industry, Government, or other users (e.g., scientific or medical professions). This knowledge must be “externally validated” as interesting and useful; it can not simply be a model that has better performance on some traditional KDD metric such as accuracy or area under the curve.

· Emerging applications and technology that provide insight relevant to the above value propositions. These emerging applications must have clear user interest and support to distinguish them from KDD research papers, or they must provide insight into issues and factors that affect the successful use of KDD technology and methods. Papers that describe infrastructure that enables the large-scale deployment of KDD techniques also are in this area.

ON BEHALF OF THE KDD-2012 ORGANIZERS

Research Program Co-chairs:

· Deepak Agarwal, Yahoo! Research

· Jian Pei, Simon Fraser University

Industry and Government Program Co-chairs:

· Michael Zeller, Zementis

· Hui Xiong, Rutgers University

General Chair:

· Qiang Yang, HKUST

Associate General Chair

· Dou Shen, CityGrid Media

ICMI 2012 Call for Multimodal Grand Challenges

International Conference on Multimodal Interaction (ICMI) 2012
Santa Monica, CA, Oct. 22-26, 2012
First Call for Multimodal Grand Challenges
—————————————————————–

The International Conference on Multimodal Interaction (ICMI) is the
premier international forum for multidisciplinary research on multimodal
human-human and human-computer interaction, interfaces, and system development. The
conference focuses on theoretical and empirical foundations, component
technologies, and combined multimodal processing techniques that define the
field of multimodal interaction analysis, interface design, and system
development.

Multimodal technologies are fundamentally changing the ways people interact
with each other and with computers. Multimodal systems – recognizing
language and gesture exchanged among people or as input to interfaces in physical
environments or mobile devices – facilitate and enrich social
interactions and allow people to naturally interact with computer systems with minimal
training, for domains such as communications, education, entertainment, and robotics.

Developing systems that can robustly understand human-human
communication or respond to human input requires identifying the best algorithms and their
failure modes. In fields such as computer vision, speech recognition, and
computational linguistics, the availability of datasets and common tasks have
led to great progress. We invite the research community to collectively define
and tackle the scientific Grand Challenges in our domain for the next 5 years.
Multimodal Grand Challenges are driven by ideas that are bold,innovative, and
inclusive. They should inspire new ideas in the multimodal interaction
community and create momentum for future collaborative work. Analysis,
synthesis, and interactive tasks are all possible.

We are seeking organizers to propose and run Grand Challenge events. Both
academic and corporate organizers are welcome. We are looking for three
types of challenges:

1. Dataset-driven challenge. This challenge will provide a dataset that is
exemplary of the complexities of current and future multimodal problems,
and one or more multimodal tasks whose performance can be objectively measured.
Participants in the Challenge will evaluate their methods against the
challenge data in order to identify areas of strength and weakness.

2. User case challenge. This challenge will provide an interactive
problem or system (e.g. dialog-based) and the associated resources,
which allow people to participate through the integration of specific
modules or alternative full systems. Proposers should also establish
systematic evaluation procedures.

3. Concept challenge. This challenge proposes new ideas (e.g. involving new
sensors) that, while not fully tested now, could lead to breakthroughs
if the community decided to tackle them together or individually.

Prospective organizers should submit a five-page max proposal containing
the following information:

– Title
– Abstract appropriate for possible Web promotion of the Challenge
– Detailed description of the challenge and its relevance to multimodal
interaction.
– Plan for soliciting participation
– Proposed schedule for releasing datasets and receiving submissions.
– Short bio of the organizers
– Funding source (if any) that supports or could support the challenge
organization.
– Preference (if any) for special session or workshop format.

Proposals will be evaluated based on originality, ambition, feasibility,
and implementation plan. The ICMI organizers offer support with basic
logistics and with the identification of additional funding sources (e.g. for awards).

Proposals should be emailed to the ICMI 2012 Challenge Co-Chairs.
Prospective organizers are also encouraged to contact them in case of questions.

Important dates

Proposal submissions: December 9, 2011
Notifications: December 16, 2011
ICMI 2012: October 22-26, 2012

ICMI 2012 Grand Challenge Chairs

Daniel Gatica-Perez
Idiap Research Institute
gatica(at)idiap.ch

Stefanie Tellex
MIT
stefie10(at)csail.mit.edu

CFP – CVMP (Conference for Visual Media Production) 2011 – EXTENDED DEADLINE

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 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