MLSB 2012: Call for Contributions

Basel, Switzerland, September 8 and 9, 2012
http://mlsb.cc

Submission Deadline: June 7, 2012
Author Notification: June 28, 2012

MLSB12, the Sixth International Workshop on Machine Learning in Systems Biology will be held in Basel, Switzerland on September 8 and 9, 2012. The Workshop is organized as “Satellite Meeting” of the 11th European Conference on Computational Biology (ECCB).

The aim of this workshop is to contribute to the cross-fertilization between the research in machine learning methods and their applications to systems biology (i.e., complex biological and medical questions) by bringing together method developers and experimentalists. We encourage submissions bringing forward methods for discovering complex structures (e.g. interaction networks, molecule structures) and methods supporting genome-wide data analysis.

Submissions
The deadline for submissions of extended abstracts is June 7, 2012 (23:59 in the time zone of your choice).

We invite you to submit an extended abstract of up to 2 pages describing new or recently published (2012) results, formatted according to the NIPS style format (MLSB is not double-blind this year). Each extended abstract must be submitted online via the Easychair submission system: http://www.easychair.org/conferences/?conf=mlsb2012.

The extended abstracts will be reviewed by the scientific programme committee. They will be selected for oral or poster presentation according to their originality and relevance to the workshop topics. Electronic versions of the extended abstracts will be accessible to the participants prior to the conference, distributed in hardcopy form to participants at the conference, and will be made publicly available on the conference web site after the conference. However, the book of abstracts will not be published and the extended abstracts will not constitute a formal publication.

Topics
We encourage submissions bringing forward methods for discovering complex structures (e.g. interaction networks, molecule structures) and methods supporting genome-wide data analysis. A non-exhaustive list of topics suitable for this workshop are:

Methods:
Machine Learning Algorithms
Bayesian Methods
Data integration/fusion
Feature/subspace selection
Clustering Metabolic pathway modeling
Biclustering/association rules
Kernel Methods
Probabilistic inference
Structured output prediction
Systems identification
Graph inference, completion, smoothing
Semi-supervised learning

Applications:
Sequence Annotation
Gene Expression and post-transcriptional regulation
Inference of gene regulation networks
Gene prediction and whole genome association studies
Signaling networks
Systems biology approaches to biomarker identification
Rational drug design methods
Metabolic reconstruction
Protein function and structure prediction
Protein-protein interaction networks
Synthetic biology

MLSB 2012 Chairs
Karsten Borgwardt, Max Planck Institutes and University of Tübingen
Gunnar Rätsch, Memorial Sloan-Kettering Cancer Center, New York

CFP: Big Data Mining (BigMine-12) Workshop at KDD12

CALL FOR PAPERS

Big Data Mining (BigMine-12)
1st International Workshop on Big Data, Streams and Heterogeneous
Source Mining: Algorithms, Systems, Programming Models and
Applications (BigMine-12) – a KDD2012 Workshop

KDD2012 Conference Dates: August 12-16, 2012
BigMine-12 Workshop Date: Aug 12, 2012
Beijing, China

http://www.big-data-mining.org

Key dates:
Papers due: May 9, 2012
Acceptance notification: May 23, 2012
Workshop Final Paper Due: June 8, 2012
Workshop Proceedings Due: June 15, 2012

Paper submission and reviewing will be handled electronically. Authors
should consult the submission site
(http://big-data-mining.org/submission/) for full details regarding
paper preparation and submission guidelines.

Papers submitted to BigMine-12 should be original work and
substantively different from papers that have been previously
published or are under review in a journal or another
conference/workshop.

Following KDD main conference tradition, reviews are not double-blind,
and author names and affiliations should be listed.

We invite submission of papers describing innovative research on all
aspects of big data mining.

Examples of topic of interest include

– Scalable, Distributed and Parallel Algorithms
– New Programming Model for Large Data beyond Hadoop/MapReduce,
STORM, streaming languages
– Mining Algorithms of Data in non-traditional formats
(unstructured, semi-structured)
– Applications: social media, Internet of Things, Smart Grid,
Smart Transportation Systems
– Streaming Data Processing
– Heterogeneous Sources and Format Mining
– Systems Issues related to large datasets: clouds, streaming
system, architecture, and issues beyond cloud and streams.
– Interfaces to database systems and analytics.
– Evaluation Technologies
– Visualization for Big Data
– Applications: Large scale recommendation systems, social media
systems, social network systems, scientific data mining,
environmental, urban and other large data mining applications.

Papers emphasizing theoretical foundations, algorithms, systems,
applications, language issues, data storage and access, architecture
are particularly encouraged.

We welcome submissions by authors who are new to the data mining
research community.

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

Top-quality papers accepted and presented at the workshop after
careful revisions by the authors, reviewed by original PC members and
chairs will be recommended to ACM TIST, ACM TKDD, IEEE Intelligent
Systems or IEEE Computer for fast publication, depending on relevance
of the topic

Theoretical attention

Dear Colleagues,

I would appreciate if you could devote some of your attention
to explaining the shape of the learning curves seen in statistical
machine translation.

Here below is a link to an extensive experimental study.
These learning curves are different than those I expect from
statistical learning theory
and it would be great to have some model explaining them.
I can think of no better crowd than Pascal2 to answer this question.

regards

Nello Cristianini

Learning to Translate: A Statistical and Computational Analysis
Marco Turchi, Tijl De Bie,Cyril Goutte, and Nello Cristianini

Advances in Artificial Intelligence
Volume 2012 (2012), Article ID 484580, 15 pages
doi:10.1155/2012/484580

The article can be found here:
downloads.hindawi.com/journals/aai/2012/484580.pdf

PhD Scholarships on Machine Learning for Neuroscience

The Department of Information Engineering and Computer Science (DISI) at the University of Trento and its research partner Fondazione Bruno Kessler (FBK), invites applications for 2 open PhD positions in the area of Machine Learning for Neuroscience, both of them covered by scholarship.

The deadline for applications is May 15, 2012, before 13:00, CET.

The PhD research program aims at carrying out research activity on machine learning methodologies for neuroscientific data analysis. The main goal is the design and the deployment of machine learning algorithms for neuroimaging-based neuroscience investigations. The research focuses on three specific tasks: brain decoding, brain mapping and brain connectivity. The challenge is to design effective computational methods for multivariate pattern analysis.

The PhD research program will take place at NILab, the Neuroinformatics Laboratory raised as a joint initiative of the Bruno Kessler Foundation and the Center for Mind/Brain Sciences (CIMeC) of the University of Trento.

The grant amount is approximately 13.500 euro per year, before taxes. Students who win the PhD scholarship will also have the tuition fee waived. Additional support from the Province of Trento is available for accomodation, which amounts approximately 1250 euro per year.

Exceptional candidates may obtain an additional internship grant to join NILab earlier, i.e. between notification of acceptance, June 2012, and the beginning of the PhD Scholarship, November 2012.

Details on the PhD School and a link to the online application are provided below. For further information, please contact info.nilab@fbk.eu.

Links
– PhD School: http://ict.unitn.it/
– DISI: http://disi.unitn.it
– FBK: http://www.fbk.eu
– NILab: http://nilab.fbk.eu
– CIMeC: http://www.cimec.unitn.it

ICML 2012 student support (deadline 6 May)

ICML 2012, the 29th International Conference on Machine Learning,
is in Edinburgh, Scotland, from June 26 to July 1, 2012. http://icml.cc

There are two programs that provide financial support for students who
attend ICML. All students may apply. A student does NOT need to have
an accepted paper at the conference in order to participate in the
programs. Both programs have the same deadline.

Applications Due: 6 May 2012, midnight PST.
Absolutely no late applications will be accepted!

You can apply for both programs at once by applying for the travel
scholarship and answering yes to the question that asks about the
volunteer program.

Student Travel Scholarship

The student travel scholarship provides funding for students to
subsidize travel, conference registration, and housing expenses for
ICML 2012. To help students be integrated into the conference, each
sponsored student will have a poster on their research.

For details see http://icml.cc/2012/scholarship-details/

Volunteer Program

In the volunteer program, students receive free registration in exchange
for agreeing to help with the organization of the conference.

For details see http://icml.cc/2012/volunteers

IEEE SMC 2012: Extended Deadline

The 2012 IEEE International Conference on Systems, Man, and Cybernetics

October 14-17 Seoul, Korea

http://www.SMC2012.org

#####################################################################################################
Coupling Humans and Complex Systems in a Cyber World: Today’s Principles for Tomorrow’s Society

Steady technological progress in all areas of human activity now makes it possible for everyone to access almost limitless amounts of data and to interact in countless ways with their surroundings, as well as with each other. In order for this data to become information and for interactions to become meaningful, however, we need sound principles for the design of complex systems, and ways of how to couple them efficiently to humans. The SMC community has accumulated considerable expertise with such complex systems in all areas of Systems Science and Engineering, Human-Machine Systems, and Cybernetics, which we can now harness. A challenge in this endeavor will be to test the scalability of existing principles and theories to cope with the complexity of tomorrow’s cyber society.
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Important Dates

May 13, 2012 Deadline for submission of full-length papers for Regular and Special Sessions (Extended!)

May 15, 2012 Acceptance/Rejection notification for Tutorial/Workshop Sessions

June 15, 2012 Acceptance/Rejection notification for regular papers and special session papers

July 15, 2012 Deadline for submission of full-length papers for Workshop Sessions

July 31, 2012 Early registration deadline

July 31, 2012 Deadline for final camera-ready papers and tutorial/workshop material submission

Oct. 14-17, 2012 Conference dates

research positions on NLP/ML at dMetrics.com

dMetrics is working on creating the next generation of text-analysis technology.
Its team includes experts on both natural language processing and machine
learning. The company is currently hiring and has openings in both research and
engineering, see: http://dmetrics.com/careers/ for details.

To potential applicants: If you attend EACL-2012 and are interested in knowing
more about job opportunities at dMetrics please contact Ariadna Quattoni or Xavier
Carreras.

——————————————————————————-

RESEARCH OPENINGS

dMetrics develops machine learning and natural language processing techniques to
meet both high-precision and high-recall information needs. Staffed with six
research scientists, and supported by software engineers, we offer opportunities
for applied research on a variety of projects related to language understanding,
ranging from solving classification and structure prediction problems for
fine-grained information extraction to building robust natural language processing
(NLP) architectures integrating syntactic and semantic components for processing
very large noisy data sets to mining and validating information extracted from
natural language data to annotating and curating text data.

Successful candidates will initiate and execute applied research projects, develop
innovative algorithms to analyze data, coauthor patents, and stay current with the
academic state-of-the-art.

The candidates will have a strong background in machine learning, data mining,
and/or NLP. Experience in processing large data sets is desirable. A solid
research publication record in these areas is a plus.

POSITION DESCRIPTIONS

RESEARCH SCIENTIST

We offer research scientist and internship positions to address challenging
problems including but not limited to:

DISCOURSE PROCESSING
Our research targets complex semantic tasks spanning multiple sentences and
involving first-order as well as higher-order relations. Research work in
discourse processing will focus on enlarging the scope and improving the recall of
information extraction by modeling discourse constructs. The ideal candidate will
be an expert in structure prediction for relation extraction, have a working
knowledge of syntactic and shallow semantic parsing, and experience in weakly
supervised structure prediction.

DATA MINING
Validating pieces of information extracted from our data is business-critical.
Research work in data mining will focus on assessing both the predictive power and
the novelty of extracted information. In addition to validating extracted
information, the ideal candidate will provide feedback to the natural language
processing research team as to which pieces of information to extract in order to
gain further statistical insights.

DATA ANNOTATION AND CURATION
Achieving consistent annotation needed for training target statistical models is
one of our core research tasks. The ideal candidate will implement annotation
procedures, possibly relying on crowd or community sourcing. Additional tasks
include curating linguistic resources relevant to information extraction tasks. A
background in linguistic analysis of real-world text data is required. Working
knowledge of NLP tools is a plus.

SEMANTIC PARSING
Mapping textual data to representations of meaning suitable for data aggregation
is one of our core objectives. The ideal candidate will design broad-coverage
semantic representations deep enough for capturing linguistic phenomena relevant
to data aggregation, and build statistical models to learn these representations
given limited supervision.

ALGORITHMS ENGINEER
We take great pride in running our machine learning and NLP algorithms on real
world datasets at real world speeds. A successful candidate should be able to help
the researchers to implement and optimize their algorithms for near real-time
performance over terabytes of data, and integrate these algorithms into our
overall NLP pipeline. Candidates should be comfortable working heavily with our
application development team and coordinating research work with product
development. The ideal candidate will have a Masters in CS (or equivalent), with
experience in distributed systems and/or high-performance algorithms.

Note: If you are an NLP, ML, or data mining researcher interested in joining our
team and your profile does not fit the positions listed below, don’t hesitate to
contact us anyway. We have tons of extraordinary challenges! We look forward to
hearing from you!

Summer School – 11th-15th June 2012

School of Computing Science, University of Glasgow
11th-15th June 2012

—Overview—

This summer school will focus on the use of inference and dynamical
modelling in human-computer interaction. The combination of modern
statistical inference and real-time closed loop modelling offers rich
possibilities in building interactive systems, but there is a
significant gap between the techniques commonly used in HCI and the
mathematical tools available in other fields of computing science.
This school aims to illustrate how to bring these mathematical tools
to bear on interaction problems.

The opportunities for interaction with computer systems are rapidly
expanding beyond traditional input and output paradigms: full-body
motion sensors, brain-computer interfaces, 3D displays, touch panels
are now commonplace commercial items. The profusion of new sensing
devices for human input and the new display channels which are
becoming available offer the potential to create more involving,
expressive and efficient interactions in a much wider range of
contexts. Dealing with these complex sources of human intention
requires appropriate mathematical methods; modelling and analysis of
interactions requires sophisticated methods which can transform
streams of data from complex sensors into estimates of human
intention.

The programme will consist of a set of lectures delivered by experts
of international standing combined with hands-on practical sessions
for constructing and working with the techniques covered in the course
material.

—Audience—

This school will be suitable for PhD students from a range of fields,
especially machine learning, HCI, interaction design and inference.

Although the course will have substantial technical content, no
prerequisites are required beyond a background in computer science.

—Speakers—

The following speakers are confirmed for the summer school:

Simon Rogers (University of Glasgow)
John Williamson (University of Glasgow)
Thomas Hermann (Bielefeld University)
Per Ola Kristensson (University of St. Andrews)
Lars Kai Hansen (Technical University of Denmark)
Mirco Musolesi (University of Birmingham)

—Application/Registration—

Registration is £250, not including accommodation.

Registration *and* accommodation are free for computing science PhD
students from SICSA institutions.

To apply for a position at the Summer School, please go to:
http://idisummerschool.eventbrite.com/

CALL FOR CONTRIBUTIONS: Object, functional, structured data : towards next generation kernel-based methods

ICML 2012 Workshop, June 30, 2012, Edinburgh, UK.

https://sites.google.com/site/nextgenkernels/

Important dates

Submission due by May 7, 2012.

Author Notification, May 21, 2012.

Workshop, June 30, 2012.

Topic

This workshop concerns analysis and prediction of complex data such as objects, functions and structures. It aims to discuss various ways to extend machine learning and statistical inference to these data and especially to complex outputs prediction. A special attention will be paid to operator-valued kernels and tools for prediction in infinite dimensional space.

Context and motivation

Complex data occur in many fields such as bioinformatics, information retrieval, speech recognition, image reconstruction, econometrics, biomedical engineering. In this workshop, we will consider two kinds of data: functional data and object or structured data. Functional data refers to data collected under the form of sampled curves or surfaces (longitudinal studies, time series, images). Analysis of these data as samples of random functions rather that a collection of individual observations is called Functional Data Analysis (FDA). FDA involves statistics in infinite-dimensional spaces and is closely associated to operatorial statistics. Its main approaches include functional principal component analysis and functional regression. Many theoretical challenges remain open in FDA and attract an increasing number of researchers.

Object and structure data exhibit an explicit structure like trees, graphs or sequences. For instance, documents, molecules, social networks and again images can be easily encoded as objet structured data. For the two last decades, both machine learning and statistics communities have developed various approaches such as graphical probabilistic models as well as kernel methods to take into account the structure of the data. In the meantime, FDA has been extended to Object Data Analysis which deals with samples of object data.

However, most of the efforts have been concentrated so far on dealing with complex inputs. In this workshop, we would like to emphasize the problem of complex outputs prediction which is involved for instance in multi-task learning, structured classification and regression, and network inference. All these tasks share a common feature: they can be viewed as approximation of vector-valued functions instead of scalar-valued functions and in the most general case, the output space is an Hilbert space. A promising direction first developed in (Micchelli and Pontil, 2005) consists in working with Reproducing Kernel Hilbert Spaces with operator-valued kernels in order to get an appropriate framework for regularization. There is thus a strong link between recent works in machine learning about prediction of multiple or complex outputs and functional and operatorial statistics.

This workshop aims at bringing together researchers from both communities to 1) provide an overview of existing concepts and methods, 2) identify theoretical challenges and (3) discuss practical applications and new tasks.

Invited speakers

Yasemin Altun (Google)

Frédéric Ferraty (University of Toulouse, France)

Arthur Gretton (Gatsby Unit, UCL MPI for Intelligent Systems, UK)

Neil Lawrence (University of Sheffield, UK)

Steve Marron (University of North Carolina, USA)

Charles Micchelli (University of Albany, USA)

Call for contributions

We invite short, high-quality submissions on the following topics:

* complex output learning
* structured output prediction
* functional data analysis

* object data analysis
* operator-valued kernels

* operator-based statistics

* joint-kernel maps
* statistical dynamics
* applications (non exhaustive list) : signal and image processing, bioinformatics, natural language processing, time series modeling …

Submission guidelines

Submissions should be written as extended abstracts, no longer than 4 pages in the ICML latex style. ICML style files and formatting instructions can be found at The submissions should include the authors’ name and affiliation since the review process will not be double blind. The extended abstract may be accompanied by an unlimited appendix and other supplementary material, with the understanding that anything beyond 4 pages may be ignored by the program committee. Please send your submission by email to nextgenkernelicml2012@gmail.com before May 7, 2102 at midnight PDT. Recently-published work is allowed.

We expect to select contributions for the spotlight and poster sessions. Authors will receive a notification by May 21, 2012.

Organizers

Florence d’Alché-Buc (University of Evry & INRIA-Saclay, France)
Hachem Kadri (INRIA-Lille, France)
Massimiliano Pontil (University College London, UK)
Alain Rakotomamonjy (University of Rouen, France)

Website admin: Céline Brouard (University of Evry, France)
Contact: nextgenkernelicml2012@gmail.com

European Workshop on RL 2012 – Extended Deadline: April 18

Key Facts:

EWRL 2012: The 10th European Workshop on Reinforcement Learning
2-days ICML Workshop
Location: Edinburgh, Scotland

Dates:
EWRL 2012: June 30-July 1
Submission Deadline: April 18, 2012 (23:59 American Samoa Time)
Notification Due: May 15, 2012

Proceedings published in JMLR W&C, Vol. 24

URL: http://ewrl.wordpress.com/ewrl10-2012/

Organizers: Marc Deisenroth, Csaba Szepesvari, Jan Peters
***************************************************************

EWRL 2012 aims to serve as a forum to discuss the current
state-of-the-art and future research directions in the continuously
growing field of reinforcement learning. We intend to make this an
exciting event not only for the European RL community but also
international researchers from related areas with many opportunities to
share new knowledge and encourage collaborative work.

The main question of this workshop is to discuss, how other statistical
learning techniques may be used to developed new RL approaches in order
to achieve properties including higher numerical robustness, easier use
in terms of open parameters, probabilistic and Bayesian interpretations,
better scalability, the inclusions of prior knowledge, etc.

We are calling for papers from the entire reinforcement learning
spectrum, with the option of either 2 page short papers or longer 8 page
JMLR W&C Proceedings format research papers. We encourage a range of
submissions to encourage broad discussion. We will publish selected
papers in the prestigious JMLR W&C Proceedings, Vol. 24.

Double submissions are allowed but must be clearly indicated. However in
the event that an EWRL
paper is accepted to another conference proceedings or journal, it will
not be reprinted in the official EWRL proceedings. The paper would still
be considered, however, for acceptance and presentation at EWRL.
Double submissions must be clearly labelled as such (e.g., add a
footnote on the first page). In case your ICML submission exceeds EWRL’s
page limit, don’t worry too much about it: submit the ICML paper.

We encourage submissions from a range of sub-topics including
(but not limited to):

– Reinforcement Learning Theory
– Function Approximation in Reinforcement Learning
– Current Progress in Bandit Regret Bounds
– MDPs, POMDPs
– Exploration vs Exploration Tradeoff
– Multi-Agent Reinforcement Learning
– Policy Search
– Actor-Critic Methods
– Bayesian Control Approaches
– RL Benchmark Problems
– Real-world Applications
– Robot RL

Keynote Speakers:

Richard Sutton (University of Alberta)
Shie Mannor (Technion)
Martin Riedmiller (University of Freiburg)
Drew Bagnell (CMU) (tentative)

Submission deadline: April 18, 2012 (23:59 American Samoa Time)
Page limit: 2 pages for short papers and 8 pages for regular
papers.
Paper format: JMLR W&C, Vol. 24
Style file:
http://www.tex.ac.uk/tex-archive/help/Catalogue/entries/jmlr.html

For more information, see http://ewrl.wordpress.com/ewrl10-2012/