PostDoc position in Computer Vision at Xerox Research

Starting Date: January 2013 or later.
The Textual and Visual Pattern Analysis group of the Xerox Research Centre Europe, is looking for a PostDoc researcher on the topic of Fine-Grained Visual Categorization (FGVC).
FGVC consists in classifying an image into a possibly large set of categories which are visually similar and semantically related e.g. animals, plants or vehicles. This is a challenging problem as the difference between two categories might rely on subtle cues which are difficult to detect, even with state-of-the-art algorithms. Although some systems have been successfully developed for various special-purpose applications such as the recognition of leaves or birds, one of the major challenges is to develop algorithms which can be applied to new problems or new domains with as little customization as possible. This project is carried out in collaboration with the TEXMEX group at INRIA Rennes (and especially Hervé Jégou) in the framework of a project sponsored by the French Government.
Requirements:
• Ph.D. degree in computer vision and / or machine learning.
• Excellent publication record in major computer vision / machine learning / multimedia conferences and journals.
• Strong development skills in C/C++ and Matlab.
• Fluent in written and oral English.
• Highly motivated.
The Textual and Visual Pattern Analysis group specializes in understanding, organizing, retrieving and enhancing visual and hybrid content. We have extensive experience and state-of-the-art systems in image categorization, image retrieval, image enhancement, quality / aesthetic assessment and document image processing. Our technology has won numerous awards in public competitions such as the PASCAL Visual Object Challenge, ImageCLEF or the ImageNet Large Scale Visual Recognition Challenge.
The Xerox Research Centre Europe is located in Grenoble, in the heart of the French Alps, close to both the Italian and Swiss borders. Based in France, the centre is part of the global Xerox Innovation Group made up of 650 researchers and Engineers in five world renowned research and technology centers.

To submit an application, please send your CV, cover letter as well as the name of at least one reference to both xrce-candidates@xrce.xerox.com and florent.perronnin@xrce.xerox.com.

ECML PKDD 2013 will have a continuous journal submission track in addition to the regular conference submission. The journal submission track is now open.

ECML PKDD 2013
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Prague, Czech Republic, September 23 to 27.

First Call for Papers
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) provides an international forum for the discussion of the latest high-quality research results in all areas related to machine learning and knowledge discovery in databases, as well as their application in innovative application domains.

The 2013 edition of ECML PKDD will have, next to the usual proceedings track, a new journal track in collaboration with Machine Learning and Data Mining and Knowledge Discovery. Articles can be submitted to the journal track all year long, while the proceedings track will have one deadline in April.

Submissions to the journal track should meet the standards of the journals, and, additionally, be concise and lend themselves to oral presentation at a conference. Articles focusing on consolidation of earlier work are less suitable for this track. Submissions will benefit from a streamlined reviewing process that allows for notification within 8 weeks. Resubmission of revised versions is possible. Upon acceptance, submissions automatically earn a presentation slot at the conference, and an abstract of the article will be included in the proceedings. More information about this new submission model is available at www.ecmlpkdd2013.org.

Submissions are invited on all aspects of machine learning, knowledge discovery and data mining, including real-world applications. Journal submissions should present work that is novel, timely, and constitutes a clearly delineated piece of research that can be considered finished. Submissions to the proceedings track ideally present innovative ideas that are inspiring, provoke discussion, and/or are demonstrated to have a large potential.

Important criteria for all submissions are their:

* potential to inspire the research community by introducing new and relevant problems, concepts, solution strategies, and ideas
* contribution to solving a problem widely recognized as both challenging and important
* capability to address a novel area of impact of machine learning and data mining
* scientific rigor, correctness, reproducibility of experiments
* presentation quality: preciseness and clarity is required

IMPORTANT DATES :

Journal track:

* bi-weekly batch deadlines on Sundays (GMT). The 2012 deadlines are 12.8., 26.8., 9.9., 23.9., 7.10., 21.10., 4.11., 18.11., 2.12., and 16.12.
* currently open to submissions
* notification within 8 weeks (for submissions within the page restrictions)

Proceedings track:

* abstract submission: Thursday, April 18, 2013
* paper submission: Monday, April 22, 2013
* notification: Friday, June 14, 2013
* camera ready copy: Friday, June 28, 2013

Conference: September 23-27, 2013

Master Class: Particle Filtering and Smoothing for State-Space Models by Arnaud Doucet

3rd October 2012, 1pm, UCL

To book please go to: http://csmlmasterclassdoucet.eventbrite.com/

State-space models are a popular class of time series models which are ubiquitous in econometrics, ecology, robotics, signal processing, statistics etc. Beyond finite state-space and linear Gaussian models, approximate inference in state-space models relies either on analytical or numerical approximations of the posterior distributions of interest. Particle methods are a class of sequential Monte Carlo methods which are flexible, easily parallelizable and provide consistent estimates. In this talk, I will review standard and advanced particle filtering and smoothing techniques. I will also discuss theoretical results which shed light on the performance of these approaches.

This is the first of three talks. There is no need to sign up for the talks on 4 and 5 October (same time and location). For more information please see: http://www.csml.ucl.ac.uk/events/series/10

Lunch will follow each lecture, in the Roberts Building Foyer (G02)

Arnaud will be available for individual of group meetings during the day from Monday 1 to Friday 5 October 2012. Please email Victoria Nicholl v.nicholl@ucl.ac.uk

Distinguished Lecture: Maximum Likelihood Particle Parameter Estimation for State-Space Models
Thursday 4 October 2012
Talk: Roberts G06 Sir Ambrose Fleming LT – 13:00-14:00
Lunch: Roberts Foyer G02 – 14:00-15:00

Distinguished Lecture: Bayesian Parameter Inference in State-Space Models using Particle Markov chain Monte Car
Friday 5 October 2012
Talk: Roberts G06 Sir Ambrose Fleming LT – 13:00-14:00
Lunch: Roberts Foyer G02 – 14:00-15:00

Sponsored by DeepMind Technologies: an ambitious London-based startup building general-pupose learning algorithms, with initial product applications in mobile social gaming.

CFP: Two NIPS workshops

NIPS 2012 Workshop on the Confluence between Kernel Methods and Graphical Models https://sites.google.com/site/kernelgraphical
================================================================
This workshop addresses two main research questions: first, how may kernel methods be used to address difficult learning problems for graphical models, such as inference for multi-modal continuous distributions on many variables, and dealing with non-conjugate priors? And second, how might kernel methods be advanced by bringing in concepts from graphical models, for instance by incorporating sophisticated conditional independence structures, latent variables, and prior information?

Submissions due: Sep 26
Notification: Oct 14

NIPS 2012 Workshop on Modern Nonparametric Methods in Machine Learning https://sites.google.com/site/nips2012modernnonparametric
=========================================================

Statistical analysis of big, high-dimensional data has become frequent in many scientific fields ranging from biology, genomics and health sciences to astronomy, economics and machine learning. The aim of this workshop is to bring together practitioners, who work on specialized applications, and theoreticians that are interested in providing sound methodology. We hope to advertise recent successes of nonparametric methods in a number of domains, involving large scale high-dimensional problems, and to dismiss the common belief that nonparametric methods are not suitable for dealing with challenges arising from big data.

Submissions due: Sep 16
Notification: Oct 7

Postdoc and PhD student (with scholarship)

One post doctoral researcher and one PhD student (with scholarship) is needed for a research project in the field of high-dimensional machine learning, which involves tools from machine learning, high-dimensional statistics, optimization and probability, led by Prof. Huan Xu, Prof. Chongjin Ong and Prof. Chenlei Leng from the National University of Singapore, starting as early as Jan 2013. The PhD position is within the department of Mechanical Engineering and will be under the supervision of Prof. Xu and Prof Ong, and the post doctoral researcher is expected to be working closely with all three faculty members. Interested applicants please email to Prof. Xu at mpexuh@nus.edu.sg.

Ideally, an applicant to the PhD position should have relevant research exposure in machine learning, operations research or statistics, and have solid mathematical ability. Good publication record would be a big plus. For the post doctoral researcher position, we expect an applicant to have strong research ability demonstrated from publication in high profile venue in relevant fields. For both positions, the applicants must be highly motivated and self-driven, eager to learn new tools and new methodologies.

About the PI (Huan Xu): I am an assistant Professor in the department of mechanical engineering in the National University of Singapore. My current research interest focus on learning and decision-making in large-scale complex systems. Specifically, I have done work in machine learning, high-dimensional statistics (e.g., Lasso, robust PCA etc), robust and adaptable optimization, Markov decision processes, and application in E-commerce, social network analysis and electricity power system. My researcjhas been published in venues including Operations Research, Math. Oper. Res., IEEE Info. Theory, JMLR, ICML/NIPS/COLT etc. More details of my research can be found in my websitehttp://guppy.mpe.nus.edu.sg/~mpexuh/

The information of the co-PIs can be found in the following websites.
http://serve.me.nus.edu.sg/mpeongcj/
http://www.stat.nus.edu.sg/~stalc/
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Huan XU

Assistant Professor :: Department of Mechanical Engineering ::: National University of Singapore
Office Room: E2-05-13
DID: 65-65164094
Email: mpexuh@nus.edu.sg
Website: http://guppy.mpe.nus.edu.sg/mpexuh

Reminder: Open PhD position in ML at Heudiasyc lab in Compiègne, France

Open PhD Position : Learning Representations of large-scale Multi-relational Data. Application to link prediction in Knowledge Bases.

Supervision : Antoine Bordes and Yves Grandvalet, CNRS – Université de Technologie de Compiègne.

Dates : position open from November 1st, 2012 to January 1st, 2013.
(earlier or later start dates can be negotiable)

Context :

A PhD studentship is available as part of the French ANR funded project EVEREST on “lEarning high-leVEl REpresentations of large Sparse Tensors”
being undertaken by Heudiasyc laboratory in Université de Technologie de Compiègne, with a partnership of Xerox Research Center Europe (Grenoble, France). See https://www.hds.utc.fr/everest for more details on the project.

The student will be based in the Heudiasyc laboratory in Compiègne
(France) and join the DI team headed by Yves Grandvalet. He/she will be supervised by Antoine Bordes (https://www.hds.utc.fr/~bordesan) and Yves Grandvalet (https://www.hds.utc.fr/~grandval). Heudiasyc is a joint laboratory with the Université de Technologie de Compiègne (UTC) and the French governmental agency for research (CNRS). In 2011, it was rated A+ (the highest rate) by the French Research evaluation agency (AERES).
Heudiasyc fosters interdisciplinary research on information science and technology including machine learning, uncertain reasoning, operations research, robotics and knowledge management. In 2011 Heudiasyc was awarded with an excellence project (LabEx) on the « Control of Technological Systems of Systems ». The project will also include a collaboration with Xerox Research Center Europe, through interactions with Guillaume Bouchard (http://www.xrce.xerox.com/).

The studentship is funded by an ANR project and will start between November 1st, 2012 and January 1st, 2013. The studentship is funded for 3 years (currently 1850€ per month — gross salary).

Requirements :

The PhD candidate should have or expect to obtain a MSc or equivalent in computer science or mathematics. The following qualities are desirable :
strong interests in machine learning or statistics ; excellent record of academic and/or professional achievement ; strong mathematical skills ; strong programming skills ; good written and spoken communication skills in French or English. The ideal candidate should be able to conduct theoretical research, but also implement and test models on very large datasets.

Project description :

Huge amounts of structured and relational data are available in many domains of engineering, industry or research ranging from the Semantic Web, or bioinformatics to recommender systems. As a result, knowledge bases (KBs), such as Freebase, WordNet or GeneOntology, became essential tools for storing, manipulating and accessing information, but they are also incomplete, imprecise and far too large to be used as efficiently and broadly as they could. Hence, there is need for methods able to summarize, complete or merge these large databases. This is the motivation of the project.

The data of these KBs is naturally represented as a so called multi-relational graph consisting of nodes associated with entities and of different types of edges between nodes corresponding to the different types of relations. The first phase of the project will consist in developing and evaluating an approach based on energy-based learning [4] for deriving high-level representations of such multi-relational graphs.
By high-level, we mean that these representations should enable to condense the original databases, to complete them by filling in missing values, and to ease their matching and merging. Energy-based models could provide a new direction to deal with multi-relational data, which will be compared with traditional low-rank methods [5] or Bayesian approaches [2,6]. They have already shown some promising preliminary results [1]. A goal of the thesis will also be to bridge energy-based learning in this context and tensor factorization [3].

In a second phase, the qualities of these new representations will be applied to link prediction, i.e. uncover relationships in a multi-relational graph that probably exist but have not been observed, on benchmark data and on real-world data provided by Xerox.

References :

[1] Bordes, A., J. Weston, R. Collobert, and Y. Bengio. “Learning Structured Embeddings of Knowledge Bases.” Proceedings of the International Conference on Artificial Intelligence (AAAI). AAAI Press, 2011.
[2] Kemp, C., J.B. Tenenbaum, T.L. Griffiths, T. Yamada, and N. Ueda.
“Learning Systems of Concepts with an Infinite Relational Model.”
Proceedings of the International Conference on Artificial Intelligence (AAAI). AAAI Press, 2006.
[3] Koida, T.G., and B.W. Bader. “Tensor Decompositions and Applications.”
SIAM Review, 2008.
[4] Lecun Y, Chopra S, Hadsell R, marc’aurelio R, Huang f (2006) A tutorial on Energy-Based learning. In: Bakir G, Hofman T, sch ̈olkopf B, Smola A, Taskar B (eds) Predicting Structured Data, MIT Press [5] Nickel, M., V. Tresp, and H.-P. Kriegel. “A Three-Way Model for Collective Learning on Multi-Relational Data.” Proceedinsg of the International Conference on Machine Learning (ICML). Bellevue, WA:
Omnipress, 2011.
[6] Sutskever, I., R. Salakhutdinov, and J.B. Tenenbaum. “Modelling Relational Data using Bayesian Clustered Tensor Factorization.” Avances in Neural Information Processing Systems (NIPS). Vancouver, BC, 2010.

Contact and application :

Applicants should send (preferably as a single PDF file):
* a CV
* a brief statement of research interests
* references (with email and phone number)
* their academic transcript
* a sample of strongest publications or course work (e.g. Master thesis)

Applications and inquiries should be directed to:
* Antoine Bordes – antoine.bordes@hds.utc.fr
* Yves Grandvalet – yves.grandvalet@hds.utc.fr

OPT 2012 – NIPS Workshop on Optimization for Machine Learning

—————————————————————————————————-
OPT 2012
NIPS Workshop on Optimization for Machine Learning
(Dec., 7-8th, 2012)
Visit: http://opt.kyb.tuebingen.mpg.de/index.html
Submit: http://www.easychair.org/conferences/?conf=opt2012
—————————————————————————————————-

We invite participation in the 5th International Workshop on
“Optimization for
Machine Learning”, to be held as a part of the NIPS 2012 conference.

Join us for an exciting program that includes plenary talks by:

* Pablo Parrilo (MIT)
* Francis Bach (INRIA)

Research contributions from the community form an integral part of our
program
and we invite papers for oral and poster presentation in the workshop. We
encourage authors to not only submit finished pieces of work, but also works
currently in progress that you would like to announce and get feedback
on. Accepted submissions will have the option to be considered for a JMLR
special issue for the workshop proceedings.

To encourage cutting-edge participation, the workshop will offer a “best
presentation” award as recognition. We also encourage submissions describing
practical systems and softwares that have been implemented to address
various
optimization problems that arise in machine learning.

The main topics are, including, but not limited to:

* Stochastic, Parallel and Online Optimization,
– Large-scale learning, massive data sets
– Distributed algorithms
– Optimization on massively parallel architectures
– Optimization using GPUs, Streaming algorithms
– Decomposition for large-scale, message-passing and online learning
– Stochastic approximation
– Randomized algorithms

* Nonconvex Optimization
– Efficient nonsmooth global optimization
– Nonsmooth, nonconvex optimization
– Nonconvex quadratic programming, including binary QPs
– Convex Concave Decompositions, D.C. Programming, EM
– Training of deep architectures and large hidden variable models
– Approximation Algorithms

* Algorithms and Techniques (application oriented)
– Global and Lipschitz optimization
– Algorithms for nonsmooth optimization
– Linear and higher-order relaxations
– Polyhedral combinatorics applications to ML problems

* Combinatorial Optimization
– Optimization in Graphical Models
– Structure learning
– MAP estimation in continuous and discrete random fields
– Clustering and graph-partitioning
– Semi-supervised and multiple-instance learning

* Practical techniques
– Optimization software and toolboxes
– GPU, Multicore, Distributed implementations

* Applications close to machine learning
– Sparse learning, compressed sensing, signal processing
– Computational Statistics
– Large scale scientific computing

Important Dates
————————–

* Deadline for submission of papers: 28th September 2012
* Notification of acceptance: 25th October 2012
* Final version of submission: 5th November 2012

Please note that at least one author of each accepted paper must be
available to present the paper at the workshop. Further details
regarding the submission process are available at the workshop
homepage.

Organizers
—————–

* Suvrit Sra, Max Planck Institute for Intelligent Systems
* Alekh Agarwal, Microsoft Research New York
* Senior Advisor: Stephen Wright, University of Wisconsin, Madison

Call for participation – small workshop on the interface of statistical mechanics and control theory

On september 12-16 2012, we are organizing a unique small workshop on the interface of statistical mechanics and control theory. In this meeting, we explore links between robotics, stochastic optimal control, non-equilibrium systems, large deviations and stochastic descriptions of quantum mechanics.

The workshop will take place in the beautiful Albaycin area of Granada, which is on the Unesco world heritage list. Due to the very interdiscipinary nature of the meeting, tutorial style talks will give introductions into different topics in the morning and we have reserved time for informal discussions in the afternoon. The program and other details can be found on the conference website http://www.snn.ru.nl/cyberstat_granada/.

The meeting is open to a limited number of interested researchers. For registration details see the conference website.

Looking forward to meeting you in Granada.

The organizers:

Bert Kappen
Misha Chertkov
Riccardo Zecchina
Frank Redig

[NIPS workshop, call for papers] Cross-Lingual Technologies (xLiTe) – NIPS 2012 workshop

JSI is co-organising Cross-Lingual Technologies Workshop at NIPS as a Pascal event

Consider submitting related topics to the workshop.

Marko

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Subject: [NIPS workshop, call for papers] Cross-Lingual Technologies (xLiTe) – NIPS 2012 workshop

xLiTe: The workshop on ‘Cross-Lingual Technologies’ will be held in conjunction with NIPS 2012. December 7, 2012. Lake Tahoe, Nevada, USA.

http://km.aifb.kit.edu/ws/xlite/

==================
Objectives
==================

Automatic text understanding has been an unsolved research problem for many years. This partially results from the dynamic and diverging nature of human languages, which ultimately results in many different varieties of natural language. This variations range from the individual level, to regional and social dialects, and up to seemingly separate languages and language families.

However, in recent years there have been considerable achievements in data driven approaches to computational linguistics exploiting the redundancy in the encoded information and the structures used. Those approaches are mostly not language specific or can even exploit redundancies across languages.

This progress in cross-lingual technologies is largely due to the increased availability of multilingual data in the form of static repositories or streams of documents. In addition parallel and comparable corpora like Wikipedia are easily available and constantly updated. Finally, cross-lingual knowledge bases like DBpedia can be used as an Interlingua to connect structured information across languages. This helps at scaling the traditionally monolingual tasks, such as information retrieval and intelligent information access, to multilingual and cross-lingual applications.

From the application side, there is a clear need for such cross-lingual technology and services. Available systems on the market are typically focused on multilingual tasks, such as machine translation, and don’t deal with cross-linguality. A good example is one of the most popular news aggregators, namely Google News that collects news isolated per individual language. The ability to cross the border of a particular language would help many users to consume the breadth of news reporting by joining information in their mother tongue with information from the rest of the world.

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

‣ Early Submission: Sept 16, 2012
‣ Early Notification: Oct 7, 2012
‣ Late/Re- Submission: Oct 21, 2012
‣ Late Notification: Oct 28, 2012
‣ Workshop Day: Dec 7, 2012

==================
Call for Papers
==================

The workshop on cross-Lingual Technologies (xLiTe) offers a platform for discussing algorithms and applications for statistical analysis of language resources covering many languages.

The xLiTe workshop is aimed at techniques, which strive for flexibility making them applicable across languages and language varieties with less manual effort and manual labeled training data. Such approaches might also be beneficial for solving the pressing task of analyzing the continuously evolving natural language varieties that are not well formed. Such data typically originates from social media, like text messages, forum posts or tweets and often is highly domain dependent.

Ideal contributions cover one or more of the topics listed below:
‣ Unsupervised and weakly supervised learning methods for cross-lingual technologies
‣ Cross-lingual technologies beyond statistical machine translation
‣ Cross-lingual representations of linguistic structure

And cover cross-lingual tasks, such as:
‣ Information diffusion across the languages
‣ Cross-lingual document linking and comparison
‣ Cross-lingual topic modeling
‣ Cross-lingual information extraction
‣ Cross-lingual semantic distances
‣ Cross-lingual semantic parsing
‣ Cross-lingual disambiguation
‣ Cross-lingual semantic annotation
‣ Cross-lingual language resources and knowledge bases

For submission instructions see
http://km.aifb.kit.edu/ws/xlite/

==================
Confirmed Speakers
==================

‣ Ryan McDonald – Google Research
‣ Bill Dolan – Microsoft Research
‣ Evan Sandhaus – New York Times
‣ Ivan Titov – Saarland University

==================
Organizers
==================

‣ Achim Rettinger – Karlsruhe Institute of Technology
‣ Xavier Carreras – Technical University of Catalunya
‣ Marko Grobelnik – Jozef Stefan Institute
‣ Juanzi Li – Tsinghua University
‣ Blaz Fortuna – Jozef Stefan Institute

xLiTe-NIPS-2012-CfP.txt
Subject: [NIPS workshop, call for papers] Cross-Lingual Technologies (xLiTe) – NIPS 2012 workshop

xLiTe: The workshop on ‘Cross-Lingual Technologies’ will be held in conjunction with NIPS 2012. December 7, 2012. Lake Tahoe, Nevada, USA.

http://km.aifb.kit.edu/ws/xlite/

==================
Objectives
==================

Automatic text understanding has been an unsolved research problem for many years. This partially results from the dynamic and diverging nature of human languages, which ultimately results in many different varieties of natural language. This variations range from the individual level, to regional and social dialects, and up to seemingly separate languages and language families.

However, in recent years there have been considerable achievements in data driven approaches to computational linguistics exploiting the redundancy in the encoded information and the structures used. Those approaches are mostly not language specific or can even exploit redundancies across languages.

This progress in cross-lingual technologies is largely due to the increased availability of multilingual data in the form of static repositories or streams of documents. In addition parallel and comparable corpora like Wikipedia are easily available and constantly updated. Finally, cross-lingual knowledge bases like DBpedia can be used as an Interlingua to connect structured information across languages. This helps at scaling the traditionally monolingual tasks, such as information retrieval and intelligent information access, to multilingual and cross-lingual applications.

From the application side, there is a clear need for such cross-lingual technology and services. Available systems on the market are typically focused on multilingual tasks, such as machine translation, and don’t deal with cross-linguality. A good example is one of the most popular news aggregators, namely Google News that collects news isolated per individual language. The ability to cross the border of a particular language would help many users to consume the breadth of news reporting by joining information in their mother tongue with information from the rest of the world.

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

‣ Early Submission: Sept 16, 2012
‣ Early Notification: Oct 7, 2012
‣ Late/Re- Submission: Oct 21, 2012
‣ Late Notification: Oct 28, 2012
‣ Workshop Day: Dec 7, 2012

==================
Call for Papers
==================

The workshop on cross-Lingual Technologies (xLiTe) offers a platform for discussing algorithms and applications for statistical analysis of language resources covering many languages.

The xLiTe workshop is aimed at techniques, which strive for flexibility making them applicable across languages and language varieties with less manual effort and manual labeled training data. Such approaches might also be beneficial for solving the pressing task of analyzing the continuously evolving natural language varieties that are not well formed. Such data typically originates from social media, like text messages, forum posts or tweets and often is highly domain dependent.

Ideal contributions cover one or more of the topics listed below:
‣ Unsupervised and weakly supervised learning methods for cross-lingual technologies
‣ Cross-lingual technologies beyond statistical machine translation
‣ Cross-lingual representations of linguistic structure

And cover cross-lingual tasks, such as:
‣ Information diffusion across the languages
‣ Cross-lingual document linking and comparison
‣ Cross-lingual topic modeling
‣ Cross-lingual information extraction
‣ Cross-lingual semantic distances
‣ Cross-lingual semantic parsing
‣ Cross-lingual disambiguation
‣ Cross-lingual semantic annotation
‣ Cross-lingual language resources and knowledge bases

For submission instructions see
http://km.aifb.kit.edu/ws/xlite/

==================
Confirmed Speakers
==================

‣ Ryan McDonald – Google Research
‣ Bill Dolan – Microsoft Research
‣ Evan Sandhaus – New York Times
‣ Ivan Titov – Saarland University

==================
Organizers
==================

‣ Achim Rettinger – Karlsruhe Institute of Technology
‣ Xavier Carreras – Technical University of Catalunya
‣ Marko Grobelnik – Jozef Stefan Institute
‣ Juanzi Li – Tsinghua University
‣ Blaz Fortuna – Jozef Stefan Institute

CFP: Data Mining School in Maastricht, The Netherlands: October 22 – October 24, 2012

*********************************************************************
** **
** 10th SCHOOL ON DATA MINING, Maastricht University, **
** Maastricht, The Netherlands **
** http://www.unimaas.nl/datamining/ **
** **
** Apologies if you receive multiple copies of this announcement **
** Please forward to anyone who might be interested **
** **
*********************************************************************

School on Data Mining

An intensive 3-day introduction to methods and applications

Department of Knowledge Engineering, Maastricht University,
Maastricht, The Netherlands
October 22 – October 24, 2012

Introduction
Most business organizations collect terabytes of data about business
processes and resources. Usually these data provide just “facts and
figures”, not knowledge that can be used to understand and eventually
re-engineer business processes and resources. Scientific community in
academia and business have addressed this problem in the last 20 years
by developing a new applied field of study known as data mining.
In practice data mining is a process of extracting implicit,
previously unknown, and potentially useful knowledge from data. It
employs techniques from statistics, artificial intelligence, and
computer science. Data mining has been successfully applied for
acquiring new knowledge in many domains (like Business, Medicine,
Biology, Economics, Military, etc.). As a result most business
organizations need urgently data-mining specialists, and this is
the point where this school comes to help.

Description
Our school on data mining tries to find a balance between theory and practice.
Each lecture is accompanied by a lab in which participants experiment
with the techniques introduced in the lecture. The lab tool is Weka, one
of the most advanced data-mining environments. A number of real data
sets will be analysed and discussed. In the end of the school
participants develop their own ability to apply data-mining techniques
for business and research purposes.

Content
The school will cover the topics listed below.
– The Knowledge Discovery Process
– Data Preparation
– Basic Techniques for Data Mining:
+ Decision-Tree Induction
+ Rule Induction
+ Instance-Based Learning
+ Bayesian Learning
+ Support Vector Machines
+ Regression Techniques
+ Clustering Techniques
+ Association Rules
– Tools for Data Mining
– How to Interpret and Evaluate Data-Mining Results

Intended Audience
This school is intended for four groups of data-mining beginners:
students, scientists, engineers, and experts in specific fields who need
to apply data-mining techniques to their scientific research, business
management, or other related applications.

Prerequisites
The school does not require any background in databases, statistics,
artificial intelligence, or machine learning. A general background in
science is sufficient as is a high degree of enthusiasm for new
scientific approaches.

Certificate
Upon request a certificate of full participation will be provided after
the school.

Registration
To register for the school please send an email to:

smirnov@maastrichtuniversity.nl

In the e-mail please specify:
– Name
– University / Organisation
– Address
– Phone
– E-Mail

Registration Deadline: October 15, 2012

Registration fees
Academic fee 600 Euros
Non-academic fee 850 Euros

Coffee breaks are included in the price. The local
cafeteria will be available for lunch (not included).

Registartion e-mail: smirnov@maastrichtuniversity.nl

Regular mail should be sent to:

Evgueni Smirnov
Department of Knowledge Engineering
Faculty of Humanities and Sciences
Maastricht University
P.O.Box 616
6200 MD Maastricht
The Netherlands
Phone: +31 (0) 43 38 82023
Fax: +31 (0) 43 38 84897
E-mail: smirnov@maastrichtuniversity.nl