Extended Deadline: ICML Workshop on Machine Learning Open Source Software 2010

To accomodate researchers waiting for decisions on their ICML papers (due
April 16) before committing to travel to Haifa, the submission deadline for the
Machine Learning Open Source Software (MLOSS) 2010 workshop has been
extended to April 23. As a result of this, we have also pushed back the
acceptance notification to May 8.

**********************************************************************

Call for Contributions

Workshop on Machine Learning Open Source Software 2010
http://mloss.org/workshop/icml10/

at ICML 2010, Haifa, Israel,
25th of June, 2010

**********************************************************************

The ICML workshop on Workshop on Machine Learning Open Source Software
(MLOSS) will held in Haifa, Israel on the 25th of June 2010.

Important Dates
===============

* Submission Date: April 23rd, 2010
* Notification of Acceptance: May 8th, 2010
* Workshop date: June 25th, 2010

Call for Contributions
======================

The organizing committee is currently seeking abstracts for talks
at MLOSS 2010. MLOSS is a great opportunity for you to tell the
community about your use, development, or philosophy of open source
software in machine learning. This includes (but is not limited to)
numeric packages (as e.g. R,octave,numpy), machine learning toolboxes
and implementations of ML-algorithms. The committee will select several
submitted abstracts for 20-minute talks.

The submission process is very simple:

* Tag your mloss.org project with the tag icml2010

* Ensure that you have a good description (limited to 500 words)

* Any bells and whistles can be put on your own project page, and
of course provide this link on mloss.org

On April 23rd 2010, we will collect all projects tagged with icml2010
for review.

Note: Projects must adhere to a recognized Open Source License
(cf. http://www.opensource.org/licenses/ ) and the source code must
have been released at the time of submission. Submissions will be
reviewed based on the status of the project at the time of the
submission deadline.

Description
===========

We believe that the wide-spread adoption of open source software
policies will have a tremendous impact on the field of machine
learning. The goal of this workshop is to further support the current
developments in this area and give new impulses to it. Following the
success of the inaugural NIPS-MLOSS workshop held at NIPS 2006, the
Journal of Machine Learning Research (JMLR) has started a new track
for machine learning open source software initiated by the workshop’s
organizers. Many prominent machine learning researchers have
co-authored a position paper advocating the need for open source
software in machine learning. To date 11 machine learning open source
software projects have been published in JMLR. Furthermore, the
workshop’s organizers have set up a community website mloss.org where
people can register their software projects, rate existing projects
and initiate discussions about projects and related topics. This
website currently lists 221 such projects including many prominent
projects in the area of machine learning.

The main goal of this workshop is to bring the main practitioners in
the area of machine learning open source software together in order to
initiate processes which will help to further improve the development
of this area. In particular, we have to move beyond a mere collection
of more or less unrelated software projects and provide a common
foundation to stimulate cooperation and interoperability between
different projects. An important step in this direction will be a
common data exchange format such that different methods can exchange
their results more easily.

This year’s workshop sessions will consist of three parts.

* We have two invited speakers: Gary Bradski and Victoria Stodden.

* Researchers are invited to submit their open source project to
present it at the workshop.

* In discussion sessions, important questions regarding the future
development of this area will be discussed. In particular, we
will discuss what makes a good machine learning software project
and how to improve interoperability between programs. In
addition, the question of how to deal with data sets and
reproducibility will also be addressed.

Taking advantage of the large number of key research groups which
attend ICML, decisions and agreements taken at the workshop will have
the potential to significantly impact the future of machine learning
software.

Invited Speakers
================

* Gary Bradski One of the main authors of OpenCV. (tentatively
confirmed)

Gary Bradski was previously responsible for the Open Source
Computer Vision Library (OpenCV) that is used globally in
research, government and commercial applications. He has also
been responsible for the open source statistical Machine
Learning Library and the Probabilistic Network Library. More
recently Dr. Bradski led the vision team for Stanley, the
Stanford robot that won the DARPA Grand Challenge autonomous
race in 2005 and most recently helped found the Stanford
Artificial Intelligence Robot (STAIR) project under the
leadership of Professor Andrew Ng. Dr. Bradski recently published
a new book for O’Reilly Press: Learning OpenCV: Computer Vision
with the OpenCV Library.

* Victoria Stodden

Victoria Stodden is a Postdoctoral Associate in Law and a Kauffman
Fellow in Law at the Information Society Project at Yale Law
School. After completing her PhD in statistics at Stanford
University in 2006 with advisor David Donoho, she obtained a
Master in Legal Studies in 2007 from Stanford Law School. She is
developing a new licensing structure for computational research
and author of the award winning paper “Reproducible Research
Standard” that describes her ideas.

Workshop Program
================

The 1 day workshop will be a mixture of talks (including a mandatory
demo of the software) and panel/open/hands-on discussions.

Morning session: 09:00 – 12:00

* Introduction and overview
* Contributed Talks
* Invited Talk: OpenCV (Gary Bradski)
* Contributed Talks
* Discussion: Exchanging Software and Data

Afternoon session: 14:00 – 17:00

* Contributed Talks
* Invited Talk: The Reproducible Research Standard
(Victoria Stodden)
* Discussion: Reproducible research

Program Committee
=================

* Jason Weston (Google Research, NY, USA)
* Leon Bottou (NEC Princeton, USA)
* Tom Fawcett (Stanford Computational Learning Laboratory, USA)
* Sebastian Nowozin (Microsoft Research, UK)
* Konrad Rieck (Technische Universität Berlin, Germany)
* Lieven Vandenberghe (University of California LA, USA)
* Joachim Dahl (Aalborg University, Denmark)
* Torsten Hothorn (Ludwig Maximilians University, Munich, Germany)
* Asa Ben-Hur (Colorado State University, USA)
* Klaus-Robert Mueller (Fraunhofer Institute First, Germany)
* Geoff Holmes (University of Waikato, New Zealand)
* Peter Reutemann (University of Waikato, New Zealand)
* Markus Weimer (Yahoo Research, California, USA)
* Alain Rakotomamonjy (University of Rouen, France)

Organizers
==========

* Soeren Sonnenburg,
Technische Universität Berlin, Franklinstr. 28/29, FR 6-9,
10587 Berlin, Germany

* Mikio Braun
Technische Universität Berlin, Franklinstr. 28/29, FR 6-9,
10587 Berlin, Germany

* Cheng Soon Ong
ETH Zürich, Universitätstr. 6, 8092 Zürich, Switzerland

* Patrik Hoyer
Helsinki Institute for Information Technology,
Gustaf Hällströmin katu 2b, 00560 Helsinki, Finland

Funding
=======

The workshop is supported by PASCAL (Pattern Analysis, Statistical
Modelling and Computational Learning)

Extended Deadline: Special issue of JMLR on Grammar Induction, Representation of Language and Language Learning

Grammar Induction, Representation of Language and Language Learning
– A special issue of Journal of Machine Learning Research –
Extended submission deadline: 3 May 2010

Alex Clark, Dorota Glowacka, Colin de la Higuera, Mark Johnson and John Shawe-Taylor,
guest editors

We would like to invite submissions for a special issue of the Journal
of Machine Learning Research on “Grammar Induction, Representation of
Language and Language Learning”.

We believe now is the time to revisit some of the fundamental grammar/
language learning tasks such as grammar acquisition, language
acquisition, language change, and the general problem of automatically
inferring generic representations of language structure in a data driven
manner.

Though the underlying problems have been known to be computationally
intractable for the standard representations of the Chomsky hierarchy,
such as regular grammars and context free grammars, progress has been
made either by modifying or restricting these classes to make them more
observable or by revisiting these classes with the added insights from
statistical machine learning or optimisation. Generalisations of
distributional learning have shown promise in unsupervised learning of
linguistic structure using tree based representations, access to queries
or using non-parametric approaches to inference. Such approaches are
starting to make inroads into one of the fundamental problems of cognitive
science: that of learning complex representations that encode meaning.

Grammar induction, also known as grammatical inference, was the subject
of an intense study in the early days of Computational Learning Theory,
with the theory of query learning largely developing out of this
research. More recently the study of new methods of representing language
and grammars through complex kernels and probabilistic modelling together
with algorithms such as structured output learning has enabled machine
learning methods to be applied successfully to a range of language
related tasks from simple topic classification through parts of speech
tagging to statistical machine translation.

These methods sometimes rely on more fluid structures than those derived
from formal grammars and yet are able to compete favourably with
classical grammatical models. Furthermore, new approaches have appeared
requiring less significant input from domain experts, often in the form
of annotated data.

Guest editors:
Alex Clark, Royal Holloway, University of London
Dorota Glowacka, University College London
Colin de la Higuera, Nantes University
Mark Johnson, Macquarie University
John Shawe-Taylor, University College London

Submissions:
The journal special issue is aimed at machine learners with an interest
in text modelling and processing who are interested in extending their
work to more complex language tasks, cognitive systems and knowledge
representation moving beyond models that are implicitly or explicitly
based on variants of finite state automata.

Submissions are expected to represent high-quality, significant
contributions in the area of machine learning algorithms and/or
applications of machine learning. Application papers are expected to
describe the application in detail and to present novel solutions that
have some general applicability (beyond the specific application). The
authors should follow standard formatting guidelines for Journal of
Machine Learning Research manuscripts
(http://jmlr.csail.mit.edu/author-info.html). Submissions and reviewing
will be handled electronically using standard procedures for Journal of
Machine Learning Research (http://jmlr.csail.mit.edu/manudb).

Funded PhD position in Information Retrieval and Social Networks – University of Saint-Étienne, France

The “Data Mining and Information Retrieval of structured data” group from the University of Saint-Étienne (France) invites applications for a *fully funded 3-year PhD position* at the Hubert-Curien lab.

Title: Impact of social networks on the Information Retrieval Process

Starting date of the thesis: October, 1st 2010.

Short abstract:
The explosion of Web 2.0 (blogs, wikis, sharing sites, social networks, etc.) opens up new perspectives for sharing and managing information.
In this context, this thesis deals with the impact of Social Networks on the Information Retrieval (IR) Process.
The IR models and algorithms, originally developed to represent and retrieve flat and homogeneous documents, are evolving in order to handle new types of documents: structured (XML), multimedia, Web, etc. This thesis aims at developping a theoretical IR model integrating the social dimension of social networks in focusing on two issues:
1) identification and representation of social relations (extracted from: user profiles, readings, clicks, bookmarks, tags, votes, links, etc.).
2) impact of these social relations on the IR process (document and query model, user profile, weighting function, matching function), and particularly on the one hand on the content representation by improving the document model, and on the other hand, on the selection of relevant information by improving the matching model.

Applicants should have or be in the process of getting a *Master’s degree in Computer Science*.
Skills in information retrieval and data mining would be appreciated. Good programming capabilities are required!
A good level in English is also required.

The selected candidate will join the “Machine Learning” group composed of 20 researchers working at the crossroads of information retrieval, data mining, social networks and machine learning.

Applications (including CV, motivation letter, grades of the last two academic years) should be sent to:
mathias.gery(at)univ-st-etienne.fr and christine.largeron(at)univ-st-etienne.fr
by April, 22th 2010 at the latest.

Some links:
– Research team: http://labh-curien.univ-st-etienne.fr/MachineLearning/
– Saint-Étienne is a medium size city near Lyon, France.
– Some facts about Saint-Étienne can be found here: http://en.wikipedia.org/wiki/Saint-Étienne
– The University of Saint-Étienne is a member of the University of Lyon consortium.
– Paris can be reached from Saint-Étienne in less than 3 hours via a direct train. Saint-Étienne has a small Airport connected by RyanAir (http://www.saint-etienne.aeroport.fr). The closest international airport is Lyon Saint-Exupéry (http://www.lyon.aeroport.fr).
– The city is surrounded by the “Pilat Regional Parc” in which almost any outdoor activity can be practiced (http://www.parc-naturel-pilat.fr/en.html).
– The art museum in Saint-Étienne holds the second national contemporary art collection, and classical music concerts, dance shows, and operas are performed at the Saint-Étienne “Massenet Opera”.

Open Postdoc position in Machine Learning

Open Postdoc position in Machine Learning

Lab: Imagine, Université Paris Est, France
Contact: Jean-Yves Audibert (audibert at imagine.enpc.fr)

Numerous problems linked to the exploration-exploitation trade-off remain open (even in the well-known *multi-armed bandit* problem). We want to address them, i.e. propose algorithms solving them and study their theoretical properties. In spirit, this is related to (i) the recent works on policies based on upper confidence bounds and (ii) the nice book: Prediction, learning, and games by Nicolò Cesa-Bianchi and Gábor Lugosi. The work is mainly theoretical but, as far as possible, we also intend to address some of the energy minimization problems that arise in Computer Vision, in particular combinatorial problems of 3D reconstruction, by using the proposed algorithms.

The position requires strong theoretical skills in probability and statistics. An ability to implement algorithms working on real-world data is also desirable.
To apply, please email CV and a brief description of research interests to Jean-Yves Audibert (audibert at imagine.enpc.fr) before May 15th, 2010.

The position is funded by a grant from the French National Research Agency (ANR). The successful candidate will have no mandatory teaching or administrative duties.

ICCLA 2011 – Call for Participation

International Conference on Computational Learning for Aerospace 2011 (ICCLA’11)
10-14 January 2011 Biopolis, Singapore

http://c2inet.sce.ntu.edu.sg/conference
http://c2inet.sce.ntu.edu.sg/conference/CFP_ICCLA.pdf

The conference addresses development of novel computational learning techniques for the analysis/application in aerospace and related industries. The goal is to identify prominent areas within the domain of aerospace where developments in computational learning can improve on current techniques and/or answer new interesting questions. The conference will bring together researchers from machine learning, data mining, optimization, and other computational learning topics, as well as researchers and engineers from industry to discuss problems in aerospace where computational learning may provide an edge over existing approaches. The conference also aims to promote discussion on recent progresses and challenges as well as on methodological issues and applied research problems. The em- phasis will be on practical problem solving involving novel algorithmic approaches.

Call For Papers:
Prospective authors are cordially invited to submit high-quality papers to ICCLA 2011. Accepted papers will be published in the conference proceedings. The conference topics include, but not limited to:

 Optimization Methods
 Autonomous Systems
 Intelligent Agents
 Reinforcement Learning
 Supervised and Semi-supervised Learning
 Bayesianand Generative Modeling
 Evolutionary Computing
 Memetic Computing
 Recurrentand State Space Models
 Kernel Methods
 Support Vector Machines
 Neural Networks
 SwarmIntelligence
 Boosting
 Multi-agent Simulation
 Multi-view/Multi-task Learning
 Online Methods
 Information Retrieval
 Multi-objective Design
 Robotics
 Radar
 Diagnostics
 Automation
 Surface Reconstruction

Paper Submission:
Manuscripts should be prepared according to the standard format of LNCS papers and be restricted to a maximum of 8 pages. All accepted papers will appear in the conference proceedings by Springer publisher. Please follow instruction on the submission page at the conference’s website http://c2inet.sce.ntu.edu.sg/conference .

Important Dates
Paper Submission: Jul 15, 2010
Decision Notification: Sep 15, 2010
Camera-ready Submission: Oct 15, 2010
Special Session/Tutorial/ Workshop Proposals: July 15, 2010

General Chair:
Dr. David R. Hardoon

Conference chairs:
Dr. Ong Yew Soon
Dr. Lim Meng Hiot
Dr. Ivor Tsang Wai Hung
Dr. Daisuke Sasaki
Dr. Emilio Parrado-Hernandez

Program Co-Chairs:
Dr. Song Wenbin
Dr. Partha S. Dutta

Finance Chair
Dr. Sintiani Dewi Teddy

Publication & Publicity Chairs
Dr. Dudy Lim
Dr. Daisuke Sasaki

Tutorial Chair
Dr. Li Xiaoli

Registration & Competition Chair
Dr. Zhang Jie

Local Chair
Dr. Li Xiang

Sponsors:
Nanyang Technological University
Boeing Phantom Works
PASCAL Network of Excellence
Global COE program at Institute of Fluid Science, Tohoku University

UAI 2010 Approximate Inference Evaluation – Preliminary Call for Participation

=========================================
PRELIMINARY CALL FOR PARTICIPATION IN THE
2010 UAI APPROXIMATE INFERENCE EVALUATION
=========================================

We are pleased to announce an upcoming evaluation of probabilistic
inference algorithms, as part of the UAI conference this July.
All researchers working on inference in graphical models are
encouraged to participate.

The evaluation will focus on the following computational tasks: calculating
MAP/MPE assignments, calculating partition functions (i.e., probability of
evidence), and calculating marginals. Submitted algorithms can compete in
some or all of these tasks.

NEW FEATURES in this year’s competition:

* There will be separate tracks for approximate algorithms that
provide upper or lower bound guarantees and those that do not.
Exact algorithms are of course invited to participate in any
of these tracks.

* A special track will assess the merit of the submitted inference
algorithms when used as a black box for parameter learning.

* Participants will receive automated performance reports
and will be able to submit improvements during the
entire competition period.

We look forward to your participation,

The organizers

DATES
====
May 3 – Dataset and submission format announced.
May 10 – Submissions accepted on website.
June 25 – Final deadline for submissions.
July 1 – Final results and winners announced.
July 8-11 – Results reported at the UAI conference.

ORGANIZERS
========
Gal Elidan – Hebrew University
Amir Globerson – Hebrew University

PROGRAM COMMITTEE
==============
Jeff Bilmes – Univ. of Washington
Rina Dechter – UC Irvine
Peter Grunwald – CWI
Isabelle Guyon – Clopinet
Peter Spirtes – CMU

Funded PhD position in Data Mining and e-health — University of Saint-Etienne, France (+ Univ of Tokyo)

The University of Saint-Etienne (France) invites applications for a *fully funded 3-year PhD position* at the Hubert-Curien lab.

Main points of the studentship:
*Data mining on stress and emotion information and e-health communities
*Starting date is about October, 1st 2010.
*In cooperation with the University of Tokyo (Living Environment Laboratory)

This project concerns the design of new data mining algorithms to exploit the symbolic data resulting from stress and emotion data transformation, in order to make discoveries (abnormal evolution signs, regular patterns, etc.). This knowledge will be used in e-health community services to implement scenarios such as user support, group debriefing, emergency processes.

The selected candidate will join the machine learning group composed of about 20 researchers working at the crossroads of data mining, machine learning, information retrieval and social networks.

Candidates must have demonstrable interest and expertise in data mining as well as in knowledge modeling and e-health. A background in psychology is highly desirable but not essential. A good level in English is also essential. Some interest in the Japanese culture is also desirable. Applicants should have or be in the process of getting a *Master’s degree in Computer Science*.

Applications with a CV (including grades of the last two academic years) should be sent to:
pierre.maret@univ-st-etienne.fr and fabrice.muhlenbach@univ-st-etienne.fr
by April, 18th 2010 at the latest.

*Some links:*
-Research team : http://labh-curien.univ-st-etienne.fr/MachineLearning/index.php
-Some facts about Saint-Etienne can be found here: http://en.wikipedia.org/wiki/Saint-Étienne.
-Saint Etienne is a medium size city near Lyon, France.
-The University of Saint Etienne is a member of the University of Lyon consortium.
-The closest airport is Lyon Saint Exupéry (http://www.lyon.aeroport.fr).
-The city is surrounded by the Pilat Regional Parc in which almost any outdoor activity can be practiced (http://www.parc-naturel-pilat.fr/eng/).
-From a cultural point of view, the art museum in Saint-Etienne holds the second national contemporary art collection, and classical music concerts, dance shows, and operas are performed at the Saint-Etienne “Massenet Opera” (http://www.decouvrez-le-votre.com/).

ECML PKDD 2010 INDUSTRIAL SESSION – Call For Participation

ECML PKDD 2010 INDUSTRIAL SESSION – Call For Participation

The European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases

September 24, 2010
Barcelona, Spain
http://www.ecmlpkdd2010.org/

ECML PKDD is among the premier scientific forums in machine learning
and data mining: we invite you to participate in the
ECML PKDD 2010 Industrial Session, consisting of invited presentations
on selected topics in machine learning and data mining from industry
perspective, and a panel on the future research challenges and
opportunities in data mining and machine learning from leading
experts in industry.

Invited Speakers
—————-
Rakesh Agrawal (Microsoft Search Labs)
Mayank Bawa (Aster Data)
Ignasi Belda (Intelligent Pharma)
Michael Berthold (KNIME)
Thore Graepel (Microsoft Research)
Alejandro Jaimes (Yahoo! Research)

Chairs
——
Taneli Mielikainen (Nokia)
Hugo Zaragoza (Yahoo! Research)

Follow ECML PKDD news on Twitter:
http://twitter.com/ecmlpkdd10

2 Job offers at the Berlin BCI group

The Machine Learning Dept. (headed by Klaus-Robert Müller) at TU Berlin offers
two research positions in a EU-funded project (“TOBI”) centered around topics of
Brain-Computer Interfaces. The positions are for PhD students or a Postdoc
researcher.

Key words: Berlin Brain-Computer Interface, Machine Learning, EEG Analysis,
Mental State Monitoring, Gaming.

From: now
To: October 2012

Please find more detailed information under

* (PhD or Postdoc)

* (PhD)

PASCAL Bootcamp – First call for participation

Pascal2 Machine Learning Bootcamp – Marseille France
First call for participation

The LIF-Marseille organises the second machine learning bootcamp of the European Network of Excellence PASCAL (Pattern Analysis, Statistical modelling and ComputAtional Learning) . This school will be held during summer 2010, from July 5th (afternoon) to July 13th (morning), on the campus of Saint Charles in Marseille.

Objective and Targeted Audience
——————————-
The bootcamp is an international event which consists in a week of course (lectures, practical and lab sessions) designed for Master students or students just about to start a PhD. It aims at training good-level students from various scientific fields to major thematics of PASCAL network, particularly machine learning.

Preliminary Program
————————-
* Introduction to Machine Learning
* Probability and Mathematical Needs
* Graphical Models
* Kernel Methods
* Machine Learning for Natural Language Processing
* Clustering
* On-Line Learning

Accommodation
————
We have booked 10 shared rooms with 4 beds each in an hotel in the centre of Marseilles (close to the port), the hotel is totally reserved for the bootcamp participants during one week. The hotel, Breakfasts and lunches (except the lunch of Sunday 11) are included and covered by our sponsors, only the dinners are at your own expense.

Inscription
————
Due to the limited number of available rooms, we may select the candidates, see the website for more details.
Application deadline: 15th May 2010.

Website
——-
http://bootcamp.lif.univ-mrs.fr

Previous Bootcamp
—————–
Barcelona during summer 2007.
http://lsi.epsevg.upc.es/~bootcamp07

Program Committee
—————————
* John Shawe-Taylor, University College London
* Colin de la Higuera, Université de Nantes
* François Denis, Université de Provence
* José Luis Balcázar, Universitat Politècnica de Catalunya
* Alexis Nasr, Université de la Méditerranée
* Rémi Eyraud, Université de Provence

Sponsors
——–
PASCAL2 Network of Excellence
Laboratoire d’Informatique Fondamentale de Marseille
Université de la Méditerannée
Université de Provence