MASH: Phd and post-doc positions in machine learning

* ABSTRACT

The MASH project is a three-year research initiative which brings
together five institutions with expertise in statistics, machine
learning, goal planning and computer vision to investigate the
collaborative design of complex hand-designed priors for machine
learning.

MASH is funded by the Information and Communication Technologies
division of the European Commission, Cognitive Systems and Robotics
unit, under the 7th Research Framework Programme.

Research will start in January 2010 and will be carried out in
Switzerland (IDIAP), France (CNRS and INRIA), Germany (WIAS) and
Czech Republic (CVUT). Open positions are listed below.

You can already register on http://www.mash-project.eu to get
updated by mail on the status of the project.

* SUMMARY

The goal of the MASH project is to create new tools for the
collaborative development of large families of feature extractors.
It aims at starting a new generation of learning software with
great prior model complexity.

The project is structured around a web platform which will be open
to external contributors early in 2010. It comprises collaborative
tools such as a wiki-based documentation and a forum, and an
experiment center which runs and analyzes experiments on a
continuous basis.

The applications targeted by the project are classical vision
problems, and goal-planning in a 3D video game and with a real
robotic arm.

Contributors will participate to the project by uploading the
source codes of “feature extractors” into the platform. Each one of
these extractors processes an input image to generate values
relevant to the system. This purposely broad definition spans from
classical vision processing such as edge detector or color
histogram estimation, to highly dedicated hand-designed templates
or event-based memory for the robotic applications. The system
concatenates all these extractors to create a very large feature
vector, which is used as an input signal for a machine learning
algorithm.

In practice, anybody can upload such a module at any time. It will
be immediately compiled and integrated in the next starting
experiment. Preliminary performance measures will be provided in a
matter of minutes, and complete results a few hours later. The
system encourages contributors to improve upon the work on other
and focus on the main weaknesses of the overall system.

The scientific issues to be tackled along the course of the project
are numerous, from standard machine learning questions such as
learning and prediction with very large feature spaces and tight
computational constraints, to original problems related to
clustering in a functional space.

* CONSORTIUM

– Idiap Research Institute, Switzerland (IDIAP)

– Centre National de la Recherche Scientifique, France (CNRS)

– Weierstrass Institute for Applied Analysis and Stochastics,
Germany (WIAS)

– Institut National de Recherche en Informatique et en Automatique,
France (INRIA)

– Czech Technical University in Prague, Czech Republic (CVUT)

* OPEN PHD POSITION AT IDIAP, SWITZERLAND

Contact point: Dr. François Fleuret,
francois.fleuret (at) idiap.ch,
http://www.idiap.ch/~fleuret/

On-line application at http://www.idiap.ch/~fleuret/hiring-mash.html

The selected candidate will be a doctoral student at EPFL EDEE
doctoral school. Research will be done at the Idiap Research
Institute, under the supervision of Dr. François Fleuret.

The research to be carried out will be the study of prediction
techniques for goal-planning with very large feature space. The
candidate will investigate prediction from images, mimicking to
learn policies provided by human operators, and extensions of
classical Markovian Modeling to the specificity of the MASH
project.

This work will mix theoretical developments in statistical learning
with the implementation of algorithms working on real-world data.

Applicants must have a strong background in mathematics and be
self-sufficient in programming. They must be familiar with several
of the following topics and interested in all of them:
probabilities, applied statistics, information theory, signal
processing, optimization, algorithmic, and C++ programming.

* OPEN POSITIONS AT CNRS, FRANCE

Contact point: Dr. Yves Grandvalet,
yves.grandvalet (at) utc.fr,
http://www.hds.utc.fr/~grandval/

We have open PhD and PostDoc positions to develop clustering and
block-clustering algorithms that will summarize heuristic behaviors
across tasks. We aim at providing feedback to the heuristic
designers by detecting similar heuristics across similar tasks,
thus empowering designers to analyze coexisting strategies, and to
detect critical failures.

We will develop clustering and block-clustering methods based on
probabilistic models and factorization techniques. We will also
study the relationships between these approaches.

The candidates will hold a Master/PhD in applied mathematics or
computer science, and should have interest in both areas. They will
work under the supervision of Y. Grandvalet and G. Govaert at the
Heudiasyc lab. http://www2.hds.utc.fr/ at University of Technology
of Compiègne http://www.utc.fr/the_university/index.php

* OPEN POSITIONS AT WIAS, GERMANY

Contact point: Dr. Gilles Blanchard,
gilles.blanchard (at) wias-berlin.de,
http://www.wias-berlin.de/people/blanchar/

The research will be carried out at the Weierstrass Institute,
Berlin, under the supervision of Dr. G. Blanchard; the selected
candidate will be a doctoral student at the Humboldt University,
Berlin.

The research will concentrate on theoretical and practical
developments of prediction techniques from a large set of
heterogeneous features: aggregation, sparsification, grouping and
reduction techniques, in particular under a strong limitation
constraint of the computational burden. Automated construction of a
similarity or distance measure between features will be also
addressed.

Specific Requirements: university degree (at least master/diploma)
in mathematics, computer, science or engineering. We expect from
potential candidates very good programming skills (C++) and at
least basic knowledge in mathematical statistics, theory of machine
learning and/or optimization.

* OPEN POSITIONS AT INRIA, FRANCE

Contact point: Dr. Olivier Teytaud,
olivier.teytaud (at) inria.fr,
http://www.lri.fr/~teytaud/

The research will be carried out at the LRI, Université Paris-Sud,
under the supervision of Olivier Teytaud (INRIA research
fellow). We have open PhD and PostDoc positions.

The research will focus on theoretical and practical developments
of planing techniques from a large set of heterogenous features.

Specific Requirements: university degree (at least master) in
mathematics, computer science or engineering. We expect from
potential candidates very good programming skills (C++) and at
least basic knowledge in machine learning and/or planning.

Faculty Position in Barcelona

The Department of Economics and Business at the Pompeu Fabra University, Barcelona, Spain
invites applications for faculty appointments at the level of tenure-track Assistant Professor of Statistics to begin September 2010. Applicants should hold a Ph.D. (or should be near completion of their doctoral studies), and should have a demonstrated potential for research excellence.

Qualified candidates are invited to apply online at:
http://www.econ.upf.edu/recruiting/. Applicants should provide a curriculum vitae, submit a copy of their most recent work and provide three letters of recommendation and the list of courses taken.

Deadline for application is November 30, 2009

Additional information can be found at http://www.econ.upf.edu.

Reader: Oxford Brookes Computer Science £46,509 rising annually to £52,346

http://www.jobs.ac.uk/job/AAF163/reader-in-computer-science/

The School of Technology wishes to strengthen its research base with the appointment of two Readers, one in an area of computer science and the other in an area of engineering. We are looking for individuals who will take a leading role in the development of research and knowledge transfer, including management of PhD students and research groups where appropriate.

The Computer Vision group in the Department of Computing was formed in 2005 by Philip Torr and William Clocksin, and is led by Philip Torr. It comprises 8 PhD’s (with vacancies for two more if you know any bright applicants) and 4 post docs, in addition we have some semi regular visitors and joint grants with other universities that account for another 3 PhD’s and 2 postdocs.

The aim of the group is to engage in state of the art research into the mathematical theory of computer vision and artificial intelligence, but to keep the mathematical research relevant to the needs of society. Our research is focused on Bayesian methods, in particular the study of the mathematics underlying Markov Random Fields, combinatorial optimization and Bayesian nets.

The applications come in many forms, and we are involved with several major companies and organizations. With Sony we are working on human computer interaction (via a camera, the “EyeToy”) for the Play Stations 2 and 3, with Sharp we are working on generation of content for 3D displays, with Oxford Metrics Group we are working on computer understanding of films (e.g. what is the shape of objects in the scene etc) in order to make better special effects, we also work on motion capture of humans (and animals) in order to drive computer generated avatars. We work on medical image analysis and on surveillence. We also do collaborative work with Microsoft Research, London, Cambridge and Oxford Universities.

PhD Studentships at Gatsby Computational Neuroscience Unit

Gatsby Computational Neuroscience Unit, UCL
4 year PhD Programme

The Gatsby Unit is a centre for theoretical neuroscience and machine
learning, focusing on unsupervised, semi-supervised and reinforcement
learning, neural dynamics, population coding, Bayesian and
nonparametric statistics, kernel methods and applications of these to
the analysis of perceptual processing, neural data, natural language
processing, machine vision and bioinformatics. It provides a unique
opportunity for a critical mass of theoreticians to interact closely
with each other, and with other world-class research groups in related
departments at UCL (University College London), including Anatomy,
Computer Science, Functional Imaging, Physics, Physiology, Psychology,
Neurology, Ophthalmology and Statistics, the cross-faculty Centre for
Computational Statistics and Machine Learning. We also have links with
other UK and overseas universities including Cambridge in the UK,
Columbia, New York and the Max Planck Institute in Germany.

The Unit always has openings for exceptional PhD candidates.
Applicants should have a strong analytical background, a keen interest
in machine learning and/or neuroscience and a relevant first degree,
for example in Computer Science, Engineering, Mathematics,
Neuroscience, Physics, Psychology or Statistics.

The PhD programme lasts four years, including a first year of
intensive instruction in techniques and research in machine learning
and theoretical neuroscience.

Competitive fully-funded studentships are available each year (to
students of any nationality) and the Unit also welcomes students with
pre-secured funding or with other scholarship/studentship applications
in progress.

Full details of our programme, and how to apply, are available at:
http://www.gatsby.ucl.ac.uk/teaching/phd/

For further details of research interests please see:
http://www.gatsby.ucl.ac.uk/research.html

Applications for 2010 entry (commencing late September 2010) should be
received no later than 6th January 2010. Shortlisted applicants will
be invited to attend interview in the week commencing 8th March 2010.

Call for papers: NIPS 2009 workshop on The Generative and Discriminative Learning Interface

CALL FOR PAPERS – The Generative and Discriminative Learning Interface
(supported by PASCAL 2)

Workshop at the 23rd Annual Conference on Neural Information
Processing Systems (NIPS 2009)
December 12, 2009, Whistler, Canada
http://gen-disc2009.wikidot.com/call

Submission Deadline: October 25, 2009

OVERVIEW

Generative and discriminative learning are two of the major paradigms
for solving prediction problems in machine learning, each offering
important distinct advantages. They have often been studied in
different sub-communities, but over the past decade, there has been
increasing interest in trying to understand and leverage the
advantages of both approaches. The goal of this workshop is to map out
our current understanding of the empirical and theoretical advantages
of each approach as well as their combination, and to identify open
research directions.

BACKGROUND AND OBJECTIVES

In generative approaches for prediction tasks, one models a joint
distribution on inputs and outputs and parameters are typically
estimated using a likelihood-based criterion. In discriminative
approaches, one directly models the mapping from inputs to outputs
(either as a conditional distribution or simply as a prediction
function); parameters are estimated by optimizing various objectives
related to a loss function. Discriminative approaches have shown
better performance with enough data, as they are better tuned to the
prediction task and are more robust to model misspecification. Despite
the strong empirical success of discriminative methods in a wide range
of applications, when the structures to be learned become complex
(e.g. in machine translation, scene understanding, biological process
discovery), even large training sets become sparse relative to the
task, and this sparsity can only be mitigated if some other source of
information comes into play to constrain the space of fitted models,
such as unlabeled examples, related data sources or human prior
knowledge about the problem. Generative modeling is a principle way of
encoding this additional information, e.g. through probabilistic
graphical models or stochastic grammar rules. Moreover, they provide a
natural way to make use of unlabeled data and can be more
computationally efficient for some models.

See http://gen-disc2009.wikidot.com/call for a more detailed
background with references.

The aim of this workshop is to provide a platform for both theoretical
and applied researchers from different communities to discuss the
status of our understanding on the interplay between generative and
discriminative learning, as well as to identify forward-looking open
problems of interest to the NIPS community. Examples of topics of
interest to the workshop are as follows:
* Theoretical analysis of generative vs. discriminative learning
* Techniques for combining generative / discriminative approaches
* Successful applications of hybrids
* Empirical comparison of generative vs. discriminative learning
* Inclusion of prior knowledge in discriminative methods
(semi-supervised approaches, generalized expectation criteria,
posterior regularization, etc.)
* Insights into the role of generative / discriminative interface for
deep learning
* Computational issues in discriminatively trained generative
models/hybrid models
* Map of possible generative / discriminative approaches and combinations
* Bayesian approaches optimized for predictive performance
* Comparison of model-free and model-based approaches in statistics or
reinforcement learning

INVITED SPEAKERS / PANELISTS

Dan Klein, UC Berkeley
http://www.cs.berkeley.edu/~klein/

Tony Jebara, Columbia University
http://www1.cs.columbia.edu/~jebara/

Phil Long, Google
http://www.phillong.info/

Ben Taskar, University of Pennsylvania
http://www.seas.upenn.edu/~taskar/

John Winn, Microsoft Research Cambridge
http://johnwinn.org/

IMPORTANT DATES

Deadline for abstract submission: October 25, 2009
Notification of acceptance: November 5, 2009
(NIPS early registration deadline is November 6)
Final version: November 20, 2009
Workshop: December 12, 2009

LOCATION

Westin Resort and Spa / Hilton Whistler Resort and Spa
Whistler, B.C., Canada
http://nips.cc/Conferences/2009/

CALL FOR PARTICIPATION

Researchers interested in presenting their work and ideas on the above
themes are invited to submit an extended abstract of 2-4 pages in pdf
format using the NIPS style available at
http://nips.cc/PaperInformation/StyleFiles (author names don’t need to
be anonymized). Submissions will be accepted either as contributed
talks or poster presentations, and we expect the speakers to provide a
final version of their paper by November 20 to be posted on the
workshop website.

Sign on at:
https://cmt.research.microsoft.com/GDLI2009
to submit your paper (you’ll need to create a login first).

WORKSHOP FORMAT

This 1 day workshop will have a mix of invited talks (3), contributed
talks (4-8), a poster session as well as a panel discussion. We will
leave plenty of time and encourage discussion throughout the day.

We also encourage the participants to visit the online forum in
December to discuss the submitted papers and the themes of the
workshop.

SPONSOR: PASCAL 2 (non-core workshop)..

ORGANIZERS

Simon Lacoste-Julien (University of Cambridge)
Percy Liang (UC Berkeley)
Guillaume Bouchard (Xerox Research Centre Europe)

CONTACT

gen.disc.nips09 at gmail.com

AERFAI Winter School on Eye-Tracking Methodology

AERFAI Winter School on Eye-Tracking Methodology
(WSETM 2009)

Computer Vision Centre
Universitat Autònoma de Barcelona
Barcelona, Spain. November 24-27, 2009

HOME PAGE: http://www.cvc.uab.es/wsetm2009
————————————————–

We are pleased to announce that the Winter School on Eye-Tracking
Methodology (WSETM’2009) will be held at the Computer Vision Centre (CVC),
Barcelona, Spain during November 24-27, 2009.

WSETM’2009 is organized by the Computer Vision Center (CVC), in the
Universitat Autònoma de Barcelona (UAB). AERFAI members will be able to
apply for financial support for attending the School. In addition,
SensoMotoric Instruments (SMI) will also offer a limited number of
stipends to help participants cover their travel expenses.

This educational activity will be the first major course on -tracking
in Spain. The course is devoted to provide the students with both
theoretical background and a strong hands-on experience. In this sense
the students will enjoy direct access to different eye-tracker hardware
and software systems.

By the end of the school the participants will have a thorough
background on eye-tracking methodology and will be able to use
eye-tracking in their own research. The programme is structured to
appeal to researchers and industry participants alike regardless of
their specific topic of research or application.

The preliminary programme of the school is available online at:
http://www.cvc.uab.es/wsetm2009/WSETM2009_program.pdf

Highly esteemed academics in the field are invited to deliver the
lectures on the state-of-the-art of eye-tracking methodology. The
curriculum is planned to correspond to the real-life workflow of
eye-tracking based research and will cover the following topics:

1. Eye movements and visual attention.
2. Setting up and testing a hypothesis.
3. Experimental design (stimuli preparation).
4. Recording data for different applications.
5. Data analysis.
6. Applications for computer vision.

ENROLMENT AND FEES:
====================
Enrolment deadline: November 1st, 2009
Registration fees: 400Eu per student. (optional +35Eu Banquet Dinner
tickets). Discounts are available for AERFAI members.

For further information please visit the WSETM2009 Home Page:
www.cvc.uab.es/wsetm2009

Call for Papers: NIPS 2009 Workshop on Transfer Learning for Structured Data (TLSD-09)

Call for Papers: NIPS 2009 Workshop on Transfer Learning for Structured
Data (TLSD-09)
in conjunction with NIPS 2009, Dec 7-12, 2009, Vancouver, B.C., Canada

http://www.cse.ust.hk/~sinnopan/nips09tlsd/

Description and background
————————
Recently, transfer learning (TL) has gained much popularity as an approach
to reduce the training-data calibration effort as well as to improve
generalization performance of learning tasks. Unlike traditional learning,
transfer learning methods make the best use of data from one or more
source tasks in order to learn a target task. Many previous works on
transfer learning have focused on transferring the knowledge across
domains where the data are assumed to be i.i.d. In many real-world
applications, such as identifying entities in social networks or
classifying web pages, data are often intrinsically non i.i.d., which
poses a major challenge to transfer learning. In this workshop, we call
for papers on the topic of transfer learning for structured data.
Structured data are those that have certain intrinsic structures such as
network topology, and present several challenges to knowledge transfer. A
first challenge is how to judge the relatedness between tasks and avoid
negative transfer. Since data are non i.i.d., standard methods for
measuring the distance between data distributions, such as KL divergence,
Maximum Mean Discrepancy (MMD) and A-distance, may not be applicable. A
second challenge is that the target and source data may be heterogeneous.
For example, a source domain is a bioinformatics network, while a target
domain may be a network of webpage. In this case, deep transfer or
heterogeneous transfer approaches are required.

Heterogeneous transfer learning for structured data is a new area of
research, which concerns transferring knowledge between different tasks
where the data are non-i.i.d. and may be even heterogeneous. This area has
emerged as one of the most promising areas in machine learning. In this
workshop, we wish to boost the research activities of knowledge transfer
across structured data in the machine learning community. We welcome
theoretical and applied disseminations that make efforts (1) to expose
novel knowledge transfer methodology and frameworks for transfer mining
across structured data. (2) to investigate effective (automated,
human-machined-cooperated) principles and techniques for acquiring,
representing, modeling and engaging transfer learning on structured data
in real-world applications.

Goals
—————
This workshop on “Transfer Learning for Structured Data” will bring active
researchers in artificial intelligence, machine learning and data mining
together to develop methods or systems, and to explore methods
for solving real-world problems associated with learning on structured
data. The workshop invites researchers interested in transfer learning,
statistical relational learning and structured data mining to contribute
their recent works on the topic of interest.

Topics of Interest
————————
(The topics of interest include but are not limited to the following)

Transfer learning for networked data.
Transfer learning for social networks.
Transfer learning for relational domains.
Transfer learning for non-i.i.d. and/or heterogeneous data.
Transfer learning from multiple structured data sources.
Transfer learning for bioinformatics networks.
Transfer learning for sensor networks.
Theoretical analysis on transfer learning algorithms for structured data.

Paper submission
———————–
We encourage authors submit extended abstracts of up
to 4 pages. To encourage that the best work in this field can be presented
TLSD, we also allow authors to submit their published or submitted work of up
to 9 pages. Submissions should be using NIPS style files (available at
http://nips.cc/PaperInformation/StyleFiles), and should include the title,
authors’ names, institutions and email addresses, and a brief abstract.
Accepted papers will be either presented as a talk or poster (with poster
spotlight). Details of submission instructions are available at
http://www.cse.ust.hk/~sinnopan/nips09tlsd/ .

Important Dates
————————
Deadline for submissions: October 26, 2009
Notification of acceptance: November 9, 2009
Deadline for camera-ready version: November 26, 2009
Workshop date: December 12, 2009 (Saturday)

Invited Speakers (Confirmed)
————————
Arthur Gretton, Carnegie Mellon University, USA
Shai Ben-David, University of Waterloo, Canada

Workshop Co-chairs
————————
Sinno Jialin Pan, Hong Kong University of Science and Technology, Hong Kong
Ivor W. Tsang, Nanyang Technological University, Singapore
Le Song, Carnegie Mellon University, USA
Karsten Borgwardt, MPI for Biological Cybernetics, Germany
Qiang Yang, Hong Kong University of Science and Technology, Hong Kong

Program Committee
————————
Andreas Argyriou, Toyota Technological Institute at Chicago, USA
Shai Ben-David, University of Waterloo, Canada
John Blitzer, University of California, USA
Hal Daume III, University of Utah, USA
Jesse Davis, University of Washington, USA
Jing Gao, University of Illinois, Urbana-Champaign, USA
Steven Hoi, Nanyang Technological University, Singapore
Jing Jiang, Singapore Management University, Singapore
Honglak Lee, Stanford University, USA
Lily Mihalkova, University of Maryland, USA
Raymond Mooney, University of Texas at Austin, USA
Massimiliano Pontil, University College London, UK
Masashi Sugiyama, Tokyo Institute of Technology, Japan
Koji Tsuda, AIST Computational Biology Research Center, Japan
Jingdong Wang, Microsoft Research Asia, China
Dong Xu, Nanyang Technological University, Singapore

If you have any questions, please contact us via tlsd09nips@gmail.com.

NIPS 2009 Causality and Time Series Analysis Mini Symposium

NIPS 2009
Causality and Time Series Analysis Mini Symposium
Thurday, December 10, 2009
Hyatt Regency, Vancouver, Canada

Sponsored by Pascal 2

The “NIPS 2009 Causality and Time Series Analysis Mini Symposium” will take place Thursday, December 10, 2009 at the Hyatt Regency, Vancouver, Canada, right after the conference. It is part of the program of the conference, but is an autonomous event.
For this mini symposium we have invited leading experts in the field to give presentation having both a tutorial component and presenting leading edge methods: http://clopinet.com/isabelle/Projects/NIPS2009/

Open PhD positions in Tuebingen, Germany

The

Max Planck research group “Machine Learning in Biology”
led by Gunnar Raetsch (http://www.fml.mpg.de/raetsch) and the

Interdepartmental Bioinformatics Max Planck research group
led by Karsten Borgwardt (http://www.kyb.mpg.de/kb)

have openings for several PhD positions in the field of Machine
Learning & Computational Biology.

Interested applicants shall apply through the official PhD programme of
the Max Planck Institute for Developmental Biology and the Friedrich
Miescher Laboratory available at:

http://phd.eb.tuebingen.mpg.de

Deadline: November 25, 2009.

With two Max Planck institutes and the Friedrich Miescher Laboratory,
the Max Planck campus in Tübingen offers an ideal environment for
interdisciplinary research at the interface of Machine Learning and
Biology. The Max Planck Institute for Biological Cybernetics hosts an
excellent department for Machine Learning, led by Bernhard Schoelkopf,
and the Max Planck Institute for Developmental Biology comprises six
departments led by world leaders in their field, including Nobel Prize
winner Christiane Nuesslein-Volhard and Leibniz Prize winner Detlef
Weigel.

COLT 2010 – Preliminary Call for Papers

The 23rd Annual Conference on Learning Theory (COLT 2010) will take place in Haifa, Israel, on June 27-29, 2010 and will be co-located with ICML 2010. We invite submissions of papers addressing theoretical aspects of machine learning and empirical inference. We strongly support a broad definition of learning theory, including:

* Analysis of learning algorithms and their generalization ability
* Computational complexity of learning
* Bayesian analysis
* Statistical mechanics of learning systems
* Optimization procedures for learning
* Kernel methods
* Inductive inference
* Boolean function learning
* Unsupervised and semi-supervised learning and clustering
* On-line learning and relative loss bounds
* Learning in planning and control, including reinforcement learning
* Learning in games, multi-agent learning
* Mathematical analysis of learning in related fields, e.g., game theory, natural language processing, neuroscience, bioinformatics, privacy and security, machine vision, data mining, information retrieval

We are also interested in papers that include viewpoints that are new to the COLT community. We welcome experimental and algorithmic papers provided they are relevant to the focus of the conference by elucidating theoretical results in learning. Also, while the primary focus of the conference is theoretical, papers can be strengthened by the inclusion of relevant experimental results.

Papers that have previously appeared in journals or at other conferences, or that are being submitted to other conferences, are not appropriate for COLT. Papers that include work that has already been submitted for journal publication may be submitted to COLT, as long as the papers have not been accepted for publication by the COLT submission deadline (conditionally or otherwise) and that the paper is not expected to be published before the COLT conference (June 2010).
Feedback on Review Quality

There will be no rebuttal phase this year. However, authors will be given the opportunity to assess the quality of reviews and provide feedback to the reviewers, after the decisions have been made. These assessments will be used in particular to determine the Best Reviewer award (see below).
Paper and Reviewer Awards

This year, COLT will award both best paper and best student paper awards. Best student papers must be authored or coauthored by a student. Authors must indicate at submission time if they wish their paper to be eligible for a student award. This does not preclude the paper to be eligible for the best paper award.

To further emphasize the importance of the reviewing quality, this year, COLT will also award a best reviewer award to the reviewer who has provided the most insightful and useful comments.
Open Problems Session

We also invite submission of open problems (see separate call). These should be constrained to two pages. There is a shorter reviewing period for the open problems. Accepted contributions will be allocated short presentation slots in a special open problems session and will be allowed two pages each in the proceedings.
Paper Format and Electronic Submission Instructions

Formatting and submission instructions will be available in early December at the conference website.

Important Dates

Preliminary call for papers issued
October 15, 2009

Electronic submission of papers (due by 5:59pm PST)
February 19, 2010

Electronic submission of open problems
March 13, 2010

Notice of acceptance or rejection
May 07, 2010

Submission of final version
May 21, 2010

Feedback on reviews due
May 28, 2010

Joint ICML/COLT workshop day
June 25, 2010

2010 COLT conference
June 27-29, 2010

Organization

Program Co-chairs:

* Adam Tauman Kalai (Microsoft Research)
* Mehryar Mohri (Courant Institute of Mathematical Sciences and Google Research)

Program Committee:

Shivani Agarwal
Mikhail Belkin
Shai Ben-David
Nicolò Cesa-Bianchi
Ofer Dekel
Steve Hanneke
Jeff Jackson
Sham Kakade
Vladimir Koltchinskii
Katrina Ligett
Phil Long
Gabor Lugosi
Ulrike von Luxburg
Yishay Mansour
Ryan O’Donnell
Massimiliano Pontil
Robert Schapire
Rocco Servedio
John Shawe-Taylor
Shai Shalev-Shwartz
Gilles Stoltz
Ambuj Tewari
Jenn Wortman Vaughan
Santosh Vempala
Manfred Warmuth
Robert Williamson
Thomas Zeugmann
Tong Zhang

Local Arrangements Chair:

* Shai Fine (IBM Research Haifa)

Invited speakers

* Prof. Noga Alon – School of Mathematical Sciences, Tel Aviv University
* Prof. Noam Nisan – School of Computer Science and Engineering, The Hebrew University Jerusalem