Connectionists: NIPS 2010 workshop announcements
Brian J Mingus
brian.mingus at Colorado.EDU
Mon Sep 20 16:24:50 EDT 2010
Dear colleagues,
Below you can find a compilation of the NIPS 2010 announcements that have
been sent to the Connectionists mailing list. If more NIPS-related
announcements arrive I will send another compilation on October 15th, so if
you are planning an announcement please send it to the list before that
time.
The following announcements are included here. You may wish to paste the
title into the Ctrl+f find function of your browser.
- NIPS 2010 Registration Open
- Deep Learning and Unsupervised Feature Learning
- Discrete Optimization in Machine Learning -- Structures, Algorithms and
Applications (DISCML)
- Optimization for Machine Learning
- Numerical Mathematics Challenges in Machine Learning
- Machine Learning for Assistive Technologies
- Challenges of Data Visualization
- Tensors, Kernels, and Machine Learning
- Transfer Learning Via Rich Generative Models
- Coarse-to-Fine Learning and Inference
- New Directions in Multiple Kernel Learning
- Predictive Models in Personalized Medicine
- New Problems and Methods in Computational Biology
Brian Mingus
Connectionists moderator
Graduate student
Computational Cognitive Neuroscience Lab
University of Colorado at Boulder
---------- Forwarded message ----------
From: Chris Hiestand <chiestand at salk.edu>
To: connectionists at cs.cmu.edu
Date: Wed, 8 Sep 2010 14:22:43 -0700
Subject: NIPS 2010 Registration Open
NIPS 2010 Registration for the Tutorials and Conference Sessions in
Vancouver and the Workshops in Whistler is now open:
https://nips.cc/Register/
Please note: early registration pricing ends after November 6.
For planning purposes, we'd tremendously appreciate if you participate in
a survey about the 2011 NIPS conference in Spain:
https://nips.cc/Surveys/survey.php?id=8
For students or Post Docs seeking financial support, both travel support
applications and volunteer applications are now open:
http://nips.cc/ConferenceInformation/TravelSupport
http://nips.cc/ConferenceInformation/Volunteering
The demonstrations proposal deadline is approaching:
September 20, 2010 23:59 PDT
http://nips.cc/Conferences/2010/CallForDemonstrations
We look forward to seeing you in Vancouver and Whistler!
---------- Forwarded message ----------
From: Honglak Lee <honglak at eecs.umich.edu>
To: connectionists at cs.cmu.edu
Date: Thu, 16 Sep 2010 22:23:31 -0400
Subject: CFP: NIPS 2010 Workshop on Deep Learning and Unsupervised Feature
Learning
Dear colleagues,
This is a call for participation in the:
Deep Learning and Unsupervised Feature Learning Workshop
in conjunction with
24th Annual Conference on Neural Information Processing Systems (NIPS 2010)
December 10 or 11, 2010 Whistler, BC, Canada
(This is a one-day workshop, and the date will be determined soon.)
http://deeplearningworkshopnips2010.wordpress.com/
Overview
------------------------------------
In recent years, there has been a lot of interest in algorithms that
learn feature hierarchies from unlabeled data. Deep learning
methods such as deep belief networks, sparse coding-based methods,
convolutional networks, and deep Boltzmann machines, have shown
promise and have already been successfully applied to a variety of
tasks in computer vision, audio processing, natural language
processing, information retrieval, and robotics. In this workshop, we
will bring together researchers who are interested in deep learning
and unsupervised feature learning, review the recent technical
progress, discuss the challenges, and identify promising future
research directions.
The workshop invites paper submissions that will be either presented
as oral or in poster format. Through invited talks, panel discussions
and presentations by the participants, this workshop attempts to
address some of the more controversial topics in deep learning today,
such as whether hierarchical systems are more powerful, and what
principles should guide the design of objective functions used to
train these models. Panel discussions will be led by the members of
the organizing committee as well as by prominent representatives of
the vision and neuro-science communities.
The goal of this workshop is two-fold. First, we want to identify the
next big challenges and propose research directions for the deep
learning community. Second, we want to bridge the gap between
researchers working on different (but related) fields, to leverage
their expertise, and to encourage the exchange of ideas with all the
other members of the NIPS community.
Dates
------------------------------------
- Submission deadline: October 15, 2010
- Acceptance notification: November 5, 2010
- Workshop date: December 10 or 11, 2010 (This is a one-day workshop,
and the date will be determined soon.)
A tentative schedule is available at:
http://deeplearningworkshopnips2010.wordpress.com/schedule.
Submissions
------------------------------------
We solicit submissions of unpublished research papers. Papers must
have at most 8 pages and must satisfy the formatting instructions of
the NIPS 2010 call for papers.
Style files are available at http://nips.cc/PaperInformation/StyleFiles.
Please note that the reviewing is double blind, and make sure to
submit your papers anonymously.
Papers should be submitted through
https://cmt.research.microsoft.com/DLUFL2010/
no later than 23:59 EST, Friday, October 15, 2010.
We encourage submissions on the following and related topics:
* unsupervised feature learning algorithms
* deep learning algorithms
* semi-supervised and transfer learning algorithms
* inference and optimization
* theoretical foundations of unsupervised learning
* theoretical foundations of deep learning
* applications of deep learning and unsupervised feature learning
The best papers will be awarded by an oral presentation, all other
papers will have a poster presentation accompanied by a short
spotlight presentation.
Organizers
------------------------------------
* Honglak Lee – University of Michigan
* Marc’Aurelio Ranzato – University of Toronto
* Yoshua Bengio – University of Montreal
* Geoff Hinton – University of Toronto
* Yann LeCun – New York University
* Andrew Y. Ng – Stanford University
---------- Forwarded message ----------
From: Andreas Krause <krausea at caltech.edu>
To: connectionists at cs.cmu.edu
Date: Sun, 29 Aug 2010 14:07:54 -0700
Subject: CFP: NIPS 2010 Workshop on Discrete Optimization in Machine
Learning -- Structures, Algorithms and Applications (DISCML)
===============================================
Call for Papers
Discrete Optimization in Machine Learning
Structures, Algorithms and Applications
Workshop at the
24th Annual Conference on Neural Information Processing Systems
(NIPS 2010)
http://www.discml.cc
Submission Deadline: Friday October 29, 2010
===============================================
- We apologize for multiple postings -
Solving optimization problems with ultimately discretely solutions is
becoming increasingly important in machine learning: At the core of
statistical machine learning is to infer conclusions from data, and
when the variables underlying the data are discrete, both the tasks of
inferring the model from data, as well as performing predictions using
the estimated model are discrete optimization problems. This workshop
aims at exploring discrete structures relevant to machine learning and
techniques relevant to solving discrete learning problems. In addition to
studying discrete structures and algorithms, this year's workshop will
put a particular emphasis on novel applications of discrete optimization
in machine learning.
We would like to encourage high quality submissions of short papers
relevant to the workshop topics. Accepted papers will be presented as
spotlight talks and posters. Of particular interest are new
algorithms with theoretical guarantees, as well as applications of
discrete optimization to machine learning problems in areas such as
the following:
Combinatorial algorithms
- Submodular & supermodular optimization
- Discrete convex analysis
- Pseudo-boolean optimization
- Randomized / approximation algorithms
Continuous relaxations
- Sparse approximation & compressive sensing
- Regularization techniques
- Structured sparsity models
Applications
- Graphical model inference & structure learning
- Clustering
- Feature selection, active learning & experimental design
- Structured prediction
- Novel discrete optimization problems in ML
Submission deadline: October 29, 2010
Length & Format: max. 6 pages NIPS 2010 format
Time & Location: December 11 2010, Whistler, Canada
Submission instructions: Email to submit at discml.cc
Organizers: Andreas Krause (California Institute of Technology),
Pradeep Ravikumar (University of Texas, Austin), Jeff A. Bilmes
(University of Washington), Stefanie Jegelka (Max Planck Institute
for Biological Cybernetics in Tuebingen, Germany)
---------- Forwarded message ----------
From: Suvrit Sra <suvrit at gmail.com>
To: connectionists at cs.cmu.edu
Date: Mon, 13 Sep 2010 14:24:26 +0200
Subject: CFP: OPT 2010, 3rd International (NIPS) Workshop on Optimization
for Machine Learning
*** Sorry if you have already received this call for participation **
Dear colleagues,
This is a call for participation in OPT 2010,
The 3rd International Workshop on Optimization for Machine Learning,
a Neural Information Processing Systems (NIPS 2010) Workshop.
Date: Dec. 10, 2010.
Location: Whistler, Canada
http://opt.kyb.tuebingen.mpg.de
*Deadline for submissions: 24th Oct., 2010
*Submission URL: http://www.easychair.org/conferences/?conf=opt2010
The detailed CFP follows.
------------------------------------------------------------------------------
OPT 2010
3rd International Workshop on Optimization for Machine Learning
NIPS*2010 Workshop
December 10th, 2010, Whistler, Canada
URL: http://opt.kyb.tuebingen.mpg.de/
------------------------------------------------------------------------------
Abstract
--------
Optimization is a well-established, mature discipline. But the way we use
this
discipline is undergoing a rapid transformation: the advent of modern data
intensive applications in statistics, scientific computing, or data mining
and
machine learning, is forcing us to drop theoretically powerful methods in
favor of simpler but more scalable ones. This changeover exhibits itself
most
starkly in machine learning, where we have to often process massive
datasets;
this necessitates not only reliance on large-scale optimization techniques,
but also the need to develop methods "tuned" to the specific needs of
machine
learning problems.
Background and Objectives
-------------------------
We build on OPT*2008 and 2009, the forerunners to this workshop that
happened
as a part of NIPS workshops. Beyond that significant precedent, there have
been several other related workshops such as the "Mathematical Programming
in
Machine Learning / Data Mining" series (2005 to 2007) and the BigML NIPS
2007
workshop.
Our workshop has the following major aims:
* Provide a platform for increasing the interaction between researchers
from
optimization, operations research, statistics, scientific computing, and
machine learning;
* Identify key problems and challenges that lie at the intersection of
optimization and ML;
* Narrow the gap between optimization and ML, to help reduce rediscovery,
and thereby accelerate new advances.
Call for Participation
----------------------
This year we invite two types of submissions to the workshop:
(i) contributed talks and/or posters
(ii) open problems
For the latter, we request the authors to prepare a few slides that clearly
present, motivate, and explain an important open problem --- the main aim
here
is to foster active discussion. The topics of interest for the open
problem session are the same as those for regular submissions; please see
below for details.
In addition to open problems, we invite high quality submissions for
presentation as talks or poster presentations during the workshop. We are
especially interested in participants who can contribute theory /
algorithms,
applications, or implementations with a machine learning focus on the
following topics:
Topics
------
* 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
* Algorithms and Techniques (application oriented)
- Global and Lipschitz optimization
- Algorithms for non-smooth optimization
- Linear and higher-order relaxations
- Polyhedral combinatorics applications to ML problems
* Non-Convex Optimization,
- Non-convex quadratic programming, including binary QPs
- Convex Concave Decompositions, D.C. Programming, EM
- Training of deep architectures and large hidden variable models
- Approximation Algorithms
* Optimization with Sparsity constraints
- Combinatorial methods for L0 norm minimization
- L1, Lasso, Group Lasso, sparse PCA, sparse Gaussians
- Rank minimization methods
- Feature and subspace selection
* 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
Important Dates
---------------
* Deadline for submission of papers: 24st October 2010
* Notification of acceptance: 12th November 2010
* Final version of submission: 20th November 2010
* Workshop date: 10th December 2010
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 (style files, page
limits, etc.)
Workshop
--------
The workshop will be a one-day event with a morning and afternoon session.
In
addition to a lunch break, long coffee breaks will be offered both in the
morning and afternoon.
A new session on open problems is proposed for spurring active discussion
and
interaction amongst the participants. A key aim of this session will be on
establishing areas and problems of interest to the community.
Invited Speakers
----------------
* Yurii Nesterov -- Catholic University of Louvain
* Laurent El Ghaoui -- University of California, Berkeley
* Mark Schmidt -- University of British Columbia
Workshop Organizers
-------------------
* Suvrit Sra, Max Planck Institute for Biological Cybernetics
* Sebastian Nowozin, Microsoft Research, Cambridge, UK
* Stephen Wright, University of Wisconsin, Madison
------------------------------------------------------------------------------
---------- Forwarded message ----------
From: Suvrit Sra <suvrit at gmail.com>
To: connectionists at cs.cmu.edu
Date: Mon, 13 Sep 2010 14:23:25 +0200
Subject: CFP: NUMML 2010, NIPS Workshop on Numerical Mathematics Challenges
in Machine Learning
Dear colleagues,
*** Sorry if you have already received this call for participation **
This is a call for participation in the:
Neural Information Processing Systems (NIPS 2010)
Workshop on Numerical Mathematical Challenges in Machine Learning
Dec. 11, 2010. Whistler, Canada
http://numml.kyb.tuebingen.mpg.de
*Deadline for submissions: 21st Oct., 2010
*Submit by email to: suvadmin at googlemail.com
The detailed CFP follows.
--------------------------------------------------------------------------------------------------
NUMML 2010
Numerical Mathematical Challenges in Machine Learning
NIPS*2010 Workshop
December 11th, 2010, Whistler, Canada
URL: http://numml.kyb.tuebingen.mpg.de/
--------------------------------------------------------------------------------------------------
Call for Contributions
------------------------------
We invite high-quality submissions for presentation as posters at the
workshop. The poster session will be designed along the lines of the poster
session for the main NIPS conference. There will probably be a spotlight
session (2 min./poster), although this depends on scheduling details not
finalized yet. In any case, authors are encouraged (and should be motivated)
to use the poster session as a means to obtain valuable feedback from
experts
present at the workshop (see "Invited Speakers" below).
Submissions should be in the form of an extended abstract, paper (limited to
8
pages), or poster. Work must be original, not published or in submission
elsewhere (a possible exception are publications at venues unknown to
machine
learning researchers, please state such details with your submission).
Authors should make an effort to motivate why the work fits the goals of the
workshop (see below) and should be of interest to the audience. Merely
resubmitting a submission rejected at the main conference, without adding
such
motivation, is strongly discouraged.
Important Dates
------------------------
* Deadline for submission: 21st October 2010
* Notification of acceptance: 27th October 2010
* Workshop date: 11th December 2010
Submission:
-----------------
Please email your submissions to: suvadmin at googlemail.com
NOTE:
---------
At least one author of each accepted submission must attend to present the
poster/potential spotlight at the workshop. Further details regarding the
submission process are available from the workshop homepage.
What follows is a synopsis about workshop goals, invited speakers, expected
audience. This information can also be obtained from the workshop homepage.
-----------------------------------------------------------------------------------------------------------------
Abstract
------------
Most machine learning (ML) methods are based on numerical mathematics (NM)
concepts, from differential equation solvers over dense matrix
factorizations
to iterative linear system and eigen-solvers. As long as problems are of
moderate size, NM routines can be invoked in a black-box fashion. However,
for
a growing number of real-world ML applications, this separation is
insufficient
and turns out to be a severe limit on further progress.
The increasing complexity of real-world ML problems must be met with layered
approaches, where algorithms are long-running and reliable components rather
than stand-alone tools tuned individually to each task at hand. Constructing
and justifying dependable reductions requires at least some awareness about
NM
issues. With more and more basic learning problems being solved sufficiently
well on the level of prototypes, to advance towards real-world practice the
following key properties must be ensured: scalability, reliability, and
numerical robustness. Unfortunately, these points are widely ignored by many
ML researchers, preventing applicability of ML algorithms and code to
complex
problems and limiting the practical scope of ML as a whole.
Goals, Potential Impact
----------------------------------
Our workshop addresses the abovementioned concerns and limitations. By
inviting numerical mathematics researchers with interest in *both* numerical
methodology *and* real problems in applications close to machine learning,
we
will probe realistic routes out of the prototyping sandbox. Our aim is to
strengthen dialog between NM and ML. While speakers will be encouraged to
provide specific high-level examples of interest to ML and to point out
accessible software, we will also initiate discussions about how to best
bridge gaps between ML requirements and NM interfaces and terminology; the
ultimate goal would be to figure out how at least some of NM's high
standards
of reliability might be transferred to ML problems.
The workshop will reinforce the community's awakening attention towards
critical issues of numerical scalability and robustness in algorithm design
and implementation. Further progress on most real-world ML problems is
conditional on good numerical practices, understanding basic robustness and
reliability issues, and a wider, more informed integration of good numerical
software. As most real-world applications come with reliability and
scalability
requirements that are by and large ignored by most current ML methodology,
the
impact of pointing out tractable ways for improvement is substantial.
General Topics of Interest
-------------------------------------
A basic example for the NM-ML interface is the linear model (or
Gaussian Markov random field), a major building block behind sparse
estimation,
Kalman smoothing, Gaussian process methods, variational approximate
inference,
classification, ranking, and point process estimation. Linear model
computations
reduce to solving large linear systems, eigenvector approximations, and
matrix
factorizations with low-rank updates. For very large problems, randomized or
online algorithms become attractive, as do multi-level strategies.
Additional
examples include analyzing global properties of very large graphs arising in
social, biological, or information transmissing networks, or robust
filtering
as a backbone for adaptive exploration and control.
We welcome and seek contributions on the following subtopics (although we do
not limit ourselves to these):
A) Large to huge-scale numerical algorithms for ML applications
* Eigenvector approximations: Specialized variants of the Lanczos
algorithm,
randomized algorithms. Application examples are:
- The linear model (covariance estimation);
- Spectral clustering, graph Laplacian methods,
- PCA, scalable graph analysis (social networks),
- Matrix completion (consumer-preference prediction)
* Randomized algorithms for low-rank matrix approximations
* Parallel and distributed algorithms
* Online and streaming numerical algorithms
B) Solving large linear systems:
* Iterative solvers
* Preconditioners, especially those based on model/problems structure which
arise in ML applications
* Multi-grid / multi-level methods
* Exact solvers for very sparse matrices
Application examples are:
- Linear models / Gaussian MRF (mean computations),
- Nonlinear optimization methods (trust-region, Newton steps, IRLS)
C) Numerical linear algebra packages relevant to ML
* LAPACK, BLAS, GotoBLAS, MKL, UMFPACK, PETSc, MPI
D) Exploiting matrix/model structure, fast matrix-vector multiplication
* Matrix decompositions/approximations
* Multi-pole methods
* Nonuniform FFT, local convolutions
E) How can numerical methods be improved using ML technology?
* Reordering strategies for sparse decompositions
* Preconditioning based on model structure
* Distributed parallel computing
Target audience:
Our workshop is targeted towards practitioners from NIPS, but is of interest
to numerical linear algebra researchers as well.
Workshop
--------------
The workshop will feature talks (tutorial style, as well as technical) on
topics relevant to the workshop. Because the explicit purpose of our
workshop
is to foster cross-fertilization between the NM and ML communities, we also
plan to hold a discussion session, which we will help to structure by
raising
concrete questions based on the topics and concerns outlined above.
To further bolster active participation, we will set aside time for poster
and
spotlight presentations, which will offer participants a chance to get
feedback about their work.
Invited Speakers
------------------------
Inderjit Dhillon University of Texas, Austin
Dan Kushnir Yale University
Michael Mahoney Stanford University
Richard Szeliski Microsoft Research
Alan Willsky Massachusetts Institute of Technology
Workshop URL
---------------------
http://numml.kyb.tuebingen.mpg.de
Workshop Organizers
------------------------------
Suvrit Sra
Max Planck Institute for Biological Cybernetics, Tuebingen
Matthias W. Seeger
Max Planck Institute for Informatics and Saarland University, Saarbruecken
Inderjit Dhillon
University of Texas at Austin, Austin, TX
------------------------------------------------------------------------------
---------- Forwarded message ----------
From: jesse hoey <jhoey at cs.uwaterloo.ca>
To: connectionists <connectionists at cs.cmu.edu>
Date: Wed, 8 Sep 2010 11:14:12 -0400
Subject: CFP: NIPS 2010 Workshop on Machine Learning for Assistive
Technologies
######################################################################
FIRST CALL FOR CONTRIBUTIONS
Machine Learning for Assistive Technologies
a workshop in conjunction with
24th Annual Conference on Neural Information Processing Systems
(NIPS 2010)
December 10 2010 Whistler, BC, Canada
http://www.cs.uwaterloo.ca/~jhoey/mlat-nips2010<http://www.cs.uwaterloo.ca/%7Ejhoey/mlat-nips2010>
Deadline for Submissions: Wednesday, October 20, 2010
Notification of Decision: Wednesday, November 3, 2010
#####################################################################
Overview:
This workshop will expose the research area of assistive technology to
machine learning specialists, will provide a forum for machine
learning researchers and medical/industrial practitioners to
brainstorm about the main challenges, and will lead to developments of
new research ideas and directions in which machine learning approaches
are applied to complex assistive technology problems. The workshop
will discuss important open questions aimed at the next five years of
research in a number of key areas, for example
1) What are the main bottlenecks that are currently holding back
complex assistive technologies from being widely deployed/used? The
argument to be presented and discussed at the workshop is that the
application of adaptivity and machine learning is one of these
bottlenecks. However, other viewpoints will be presented and
discussed.
2) Do assistive technologies need some new type of machine learning?
Are there any new machine learning problems or is it mostly a matter
of adapting existing machine learning techniques to assistive
technologies? A key challenge for assistive technologies is the
detection of novel or changing patterns of behavior. Are existing
novelty detection, feature selection and unsupervised learning
techniques sufficient to handle this challenge?
3) What are the bottlenecks for the scaling of machine learning
techniques for the assistive technology domain? More precisely, how
can ML algorithms scale to large domains both in terms of state,
action and observation spaces, and in terms of temporal extent?
Unsupervised learning, feature selection, distributivity, and
hierarchy are obvious choices. However, user adaptability and
customizability, the appropriate integration of prior knowledge, and
the rapid and inexpensive deployment of large sensor networks
(including cameras) also play a significant role.
Workshop Format
---------------
Participants will be machine learning specialists with an interest in
expanding their research profile into the area of assistive
technology, existing researchers in AT, practitioners in occupational
therapy with an interest in machine learning, and technology
developers with an interest in further developing their application
area into this novel field of research. The main focus of the workshop
will be on discussions and brainstorming sessions of breakout groups
with the explicit goal of identifying demands from the field of AT,
and ML related research topics that will help to overcome current
bottlenecks for successful AT approaches.
The workshop will consist of invited talks from two perspectives
(medical/industrial and academic/research) to be given by experts from
the field. Participants of the workshop will be asked to submit short
or long papers. Accepted papers will briefly be presented orally in
short (spotlight) sessions. Accompanying posters will be displayed
throughout the whole workshop. The workshop will then define breakout
discussion topics, and will allocate participants to groups for
brainstorming sessions, closing with presentations and discussions.
Significant time will be allocated to these breakout discussions and
the presentations of their findings.
Invited Talks
-------------
Prasad Tadepalli, Oregon State University will speak from the machine
learning perspective
Other invited speakers to be confirmed soon.
Submissions:
-------------------
We welcome the following types of papers:
1. 6-8 page research papers that describe research in machine learning
as applied to assistive technology
2. 6-8 page research papers that describe studies of assistive
technology, emphasising the role (or potential role) of learning.
3. 2 page position statements or research abstacts from academia or
industry describing particular approaches or research techniques and
tools
Accepted papers will be presented as posters. Exceptional work will
be considered for oral presentation.
All submissions should adhere to NIPS format
(http://nips.cc/PaperInformation/StyleFiles). Please email your
submissions to: mlat.nips2010 at gmail.com
Deadline for Submissions: Wednesday, October 20, 2010
Notification of Decision: Wednesday, November 3, 2010
Organizers:
-----------
Jesse Hoey, University of Waterloo, jhoey at cs.uwaterloo.ca
Pascal Poupart, University of Waterloo, ppoupart at cs.uwaterloo.ca
Thomas Plotz, Newcastle University, t.ploetz at ncl.ac.uk
We look forward to receiving your submissions!
Jesse, Pascal and Thomas
--
Jesse Hoey
David R. Cheriton School of Computer Science
University of Waterloo
200 University Avenue West
Waterloo, Ontario
N2L 3G1 CANADA
tel: +15198884567x37744
email: jhoey at cs.uwaterloo.ca
---------- Forwarded message ----------
From: Laurens van der Maaten <lvdmaaten at gmail.com>
To:
Date: Tue, 31 Aug 2010 11:00:49 -0700
Subject: NIPS 2010 Workshop on Challenges of Data Visualization
-- Apologies if you receive multiple copies of this announcement --
-- Please forward to anyone who might be interested --
######################################################################
CALL FOR PAPERS
Challenges of Data Visualization
a workshop in conjunction with
24th Annual Conference on Neural Information Processing Systems (NIPS 2010)
December 10 or 11, 2010 Whistler, BC, Canada
http://cseweb.ucsd.edu/~lvdmaaten/workshops/nips2010<http://cseweb.ucsd.edu/%7Elvdmaaten/workshops/nips2010>
Submission deadline: October 22, 2010
Acceptance notification: November 5, 2010
#####################################################################
Overview:
---------------------------------
The increasing amount and complexity of electronic data sets turns
visualization into a key technology to provide an intuitive interface to the
information. Unsupervised learning has developed powerful techniques for,
e.g., manifold learning, dimensionality reduction, collaborative filtering,
and topic modeling. However, the field has so far not fully appreciated the
problems that data analysts seeking to apply unsupervised learning to
information visualization are facing such as heterogeneous and context
dependent objectives or streaming and distributed data with different
credibility. Moreover, the unsupervised learning field has hitherto failed
to develop human-in-the-loop approaches to data visualization, even though
such approaches including, e.g., user relevance feedback are necessary to
arrive at valid and interesting results.
As a consequence, a number of challenges arise in the context of data
visualization which cannot be solved by classical methods in the field:
- Methods have to deal with modern data formats and data sets: How can the
technologies be adapted to deal with streaming and probably non i.i.d. data
sets? How can specific data formats be visualized appropriately such as
spatio-temporal data, spectral data, data characterized by a general
probably non-metric dissimilarity measure, etc.? How can we deal with
heterogeneous data and different credibility? How can the dissimilarity
measure be adapted to emphasize the aspects which are relevant for
visualization?
- Available techniques for specific tasks should be combined in a canonic
way: How can unsupervised learning techniques be combined to construct good
visualizations? For instance, how can we effectively combine techniques for
clustering, collaborative filtering, and topic modeling with dimensionality
reduction to construct scatter plots that reveal the similarity between
groups of data, movies, or documents? How can we arrive at context dependent
visualization?
- Visualization techniques should be accompanied by theoretical guarantees:
What are reasonable mathematical specifications of data visualization to
shape this inherently ill-posed problem? Can this be controlled by the user
in an efficient way? How can visualization be evaluated? What are reasonable
benchmarks? What are reasonable evaluation measures?
- Visualization techniques should be ready to use for users outside the
field: Which methods are suited to users outside the field? How can the
necessity be avoided to set specific technical parameters by hand or choose
from different possible mathematical algorithms by hand? Can this necessity
be substituted by intuitive interactive mechanisms which can be used by
non-experts?
The goal of the workshop is to identify the state-of-the-art with respect to
these challenges and to discuss possibilities to meet these demands with
modern techniques. The workshop will consist of invited tutorial talks,
presentations of new research in a poster session, and panel discussions to
identify the current state-of-the-art and future perspectives. Registration
will be open to all NIPS 2010 Workshop attendees.
Submissions:
---------------------------------
We solicit submissions for an oral or poster presentation that report new
(unpublished) research results or ongoing research. Submissions can be up to
4 pages long. It is allowed to use additional pages for visualizations
(i.e., it is acceptable to have additional pages with images). Papers should
be formatted in NIPS 2010 format (LaTeX style files are available on the
conference website). Papers must be in English and must be submitted as PDF
files. If accepted, submissions will be published on the workshop website.
Papers should be submitted electronically no later than 23:59 Pacific
Standard time, Friday, October 22, 2010. The submission website will be
announced soon.
At least one author of each accepted submission will be expected to attend
and present their findings at the workshop.
We encourage submissions connected to the following non-exhaustive list of
topics:
- Visualization methods for streaming data sets
- Visualization of structures and heterogeneous objects
- Visualization of multiple modalities and non-metric data
- Back-projection methods
- Parameterless models for data visualization
- Evaluation measures of data visualization
- Innovative combination of different machine learning tools for data
visualization
- Novel benchmarks for data visualization
Dates:
---------------------------------
- Submission deadline: October 22, 2010
- Acceptance notification: November 5, 2010
- Workshop date: December 10 or 11, 2010
Organizers:
---------------------------------
- Barbara Hammer, TU Clausthal
- Laurens van der Maaten, UC San Diego / Delft University of Technology
- Fei Sha, University of Southern California
- Alex Smola, Yahoo! Research / Australian National University
---------- Forwarded message ----------
From: Marco Signoretto <marco.signoretto at esat.kuleuven.be>
To: connectionists at cs.cmu.edu
Date: Mon, 13 Sep 2010 22:20:31 +0200
Subject: NIPS 2010 Workshop: Tensors, Kernels, and Machine Learning - Call
for Contributions
=======================================================================
NIPS2010 Workshop
Tensors, Kernels, and Machine Learning
Friday December 10th, Whistler, BC
http://csmr.ca.sandia.gov/~dfgleic/tkml2010<http://csmr.ca.sandia.gov/%7Edfgleic/tkml2010>
-----------------------------------------------------------------------
Submission deadline: September 30th, 2010
Notification deadline: October 11th, 2010
-----------------------------------------------------------------------
Tensors are a generalization of vectors and matrices to high
dimensions.
The goal of this workshop is to explore the links between tensors,
kernel methods, and machine learning. We expect that many problems in,
for example, machine learning and kernel methods can benefit from
being
expressing as tensor problems; conversely, the tensor community may
learn from the estimation techniques commonly used in information
processing and from some of the kernel extensions to nonlinear models.
Moreover, some of the techniques in kernel methods might enable
kernel based multi-linear models of tensors.
This workshop is appropriate for anyone who wishes to learn more about
tensor methods and/or share their machine learning or kernel
techniques
with the tensor community; conversely, we invite contributions from
tensor experts seeking to use tensors for problems in machine learning
and information processing.
Please see http://csmr.ca.sandia.gov/~dfgleic/tkml2010<http://csmr.ca.sandia.gov/%7Edfgleic/tkml2010>for
more
information about submissions.
ORGANIZERS
Andreas Argyriou (Toyota Institute of Technology),
David F. Gleich (Sandia), Tamara G. Kolda (Sandia),
Vicente Malave (UC San Diego), Marco Signoretto (KU Leuven),
Johan Suykens (KU Leuven)
=======================================================================
--
Marco Signoretto,
Office Nr. 04.11
ESAT - SCD - SISTA,
Katholieke Universiteit Leuven,
Kasteelpark Arenberg 10, B-3001 LEUVEN - HEVERLEE (BELGIUM)
Email : Marco.Signoretto at esat.kuleuven.be
Phone: +32 16 328657
---------- Forwarded message ----------
From: Ruslan Salakhutdinov <rsalakhu at cs.toronto.edu>
To: connectionists at cs.cmu.edu
Date: Wed, 25 Aug 2010 17:04:23 -0400 (EDT)
Subject: NIPS 2010 workshop on Transfer Learning Via Rich Generative Models
-- Apologies if you receive multiple copies of this announcement --
------------------------------------------------------------
CALL FOR CONTRIBUTIONS
NIPS 2010 workshop on Transfer Learning Via
Rich Generative Models.
Whistler, BC, Canada, December, 2010
http://www.mit.edu/~rsalakhu/workshop_nips2010/index.html<http://www.mit.edu/%7Ersalakhu/workshop_nips2010/index.html>
Important Dates:
----------------
Deadline for submissions: October 20, 2009
Notification of acceptance: October 27, 2009
Overview:
----------------
Intelligent systems must be capable of transferring previously-learned
abstract knowledge to new concepts, given only a small or noisy set of
examples. This transfer of higher order information to new learning tasks
lies at the core of many problems in the fields of computer vision,
cognitive science, machine learning, speech perception and natural language
processing.
Over the last decade, there has been considerable progress in developing
cross-task transfer (e.g., multi-task learning and semi-supervised learning)
using both discriminative and generative approaches. However, many existing
learning systems today can not cope with new tasks for which they have not
been specifically trained. Even when applied to related tasks, trained
systems often display unstable behavior.
More recently, researchers have begun developing new approaches to building
rich generative models that are capable of extracting useful, high-level
structured representations from high-dimensional input. The learned
representations have been shown to give promising results for solving a
multitude of novel learning tasks, even though these tasks may be unknown
when the generative model is being trained.
Although there has been recent progress, existing computational models are
still far from being able to represent, identify and learn the wide variety
of possible patterns and structure in real-world data. The goal of this
workshop is to catalyze the growing community of researchers working on
learning rich generative models, assess the current state of the field,
discuss key challenges, and identify future promising directions of
investigation.
(More detailed background information is available at the workshop website,
http://www.mit.edu/~rsalakhu/workshop_nips2010/index.html<http://www.mit.edu/%7Ersalakhu/workshop_nips2010/index.html>
)
Submission Instructions:
------------------------
We invite submission of extended abstracts to the workshop. Extended
abstracts should be 2-4 pages and adhere to the NIPS style (
http://nips.cc/PaperInformation/StyleFiles). Submissions should include the
title, authors' names, institutions and email addresses and should be sent
in PDF or PS file format by email to gentrans-nips2010 at cs.toronto.edu
Submissions will be reviewed by the organizing committee on the basis of
relevance, significance, technical quality, and clarity. Selected
submissions may be accepted either as an oral presentation or as a poster
presentation: there will be a limited number of oral presentations.
We encourage submissions with a particular emphasis on:
1. Learning structured representations: How can machines extract invariant
representations from a large supply of high-dimensional highly-structured
unlabeled data? How can these representations be used to learn many
different concepts (e.g., visual object categories) and expand on them
without disrupting previously-learned concepts? How can these
representations be used in multiple applications?
2. Transfer Learning: How can previously-learned representations help
learning new tasks so that less labeled supervision is needed? How can this
facilitate knowledge representation for transfer learning tasks?
3. One-shot learning: Can we develop rich generative models that are capable
of efficiently leveraging background knowledge in order to learn novel
categories based on a single or a few training example? Are there models
suitable for deep transfer, or generalizing across domains, when presented
with few examples?
4. Deep learning: Recently, there has been notable progress in learning deep
probabilistic generative models, including Deep Belief Networks, Deep
Boltzmann Machines, deep nonparametric Bayesian models, that contain many
layers of hidden variables. Can these models be extended to transfer
learning tasks as well as learning new concepts with only one or few
examples? Can we use representations learned by the deep models as an input
to more structured hierarchical Bayesian models?
5. Scalability and success in real-world applications: How well do existing
transfer learning models scale to large-scale problems including problems in
computer vision, natural language processing, and speech perception? How
well do these algorithms perform when applied to modeling high-dimensional
real-world distributions (e.g. the distribution of natural images)?
Organizers
----------
Ruslan Salakhutdinov, MIT
Ryan Adams, University of Toronto
Josh Tenenbaum, MIT
Zoubin Ghahramani, University of Cambridge
Tom Griffiths, University of California, Berkeley.
---------- Forwarded message ----------
From: Ben Taskar <taskar at cis.upenn.edu>
To: connectionists at cs.cmu.edu
Date: Thu, 26 Aug 2010 07:30:59 -0400
Subject: Call for papers: NIPS 2010 Workshop on Coarse-to-Fine Learning and
Inference
Apologies for multiple copies of this announcement.
######################################################################
CALL FOR PAPERS
Coarse-to-Fine Learning and Inference
a workshop in conjunction with
24th Annual Conference on Neural Information Processing Systems (NIPS 2010)
December 10 or 11, 2010 Whistler, BC, Canada
http://learning.cis.upenn.edu/coarse2fine/
Deadline for Submissions: Friday, October 29, 2010
Notification of Decision: Monday, November 8, 2010
#####################################################################
Overview
The bottleneck in many complex prediction problems is the prohibitive
cost of inference or search at test time. Examples include structured
problems such as object detection and segmentation, natural language
parsing and translation, as well as standard classification with
kernelized or costly features or a very large number of classes. These
problems present a fundamental trade-off between approximation error
(bias) and inference or search error due to computational constraints
as we consider models of increasing complexity. This trade-off is much
less understood than the traditional approximation/estimation
(bias/variance) trade-off but is constantly encountered in machine
learning applications. The primary aim of this workshop is to formally
explore this trade-off and to unify a variety of recent approaches,
which can be broadly described as "coarse-to-fine" methods, that
explicitly learn to control this trade-off. Unlike approximate
inference algorithms, coarse-to-fine methods typically involve exact
inference in a coarsened or reduced output space that is then
iteratively refined. They have been used with great success in
specific applications in computer vision (e.g., face detection) and
natural language processing (e.g., parsing, machine translation).
However, coarse-to-fine methods have not been studied and formalized
as a general machine learning problem. Thus many natural theoretical
and empirical questions have remained un-posed; e.g., when will such
methods succeed, what is the fundamental theory linking these
applications, and what formal guarantees can be found?
A significant portion of the workshop will be given over to
discussion, in the form of two organized panel discussions and a small
poster session. We have taken care to invite speakers who come from
each of the research areas mentioned above, and we intend to similarly
ensure that the panels are comprised of speakers from multiple
communities. We anticipate that this workshop will lead to new
research directions in the analysis and development of coarse-to-fine
and other methods that address the bias/computation trade-off,
including the establishment of several benchmark problems to allow
easier entry by researchers who are not domain experts into this area.
Call for Participation
We invite submission of workshop papers that discuss ongoing or
completed work in machine learning, computer vision, and natural
language processing and addressing large-scale prediction problems
where inference cost is a major bottleneck. Furthermore, because the
"coarse-to-fine" label is broadly interpreted across many different
fields, we also invite any submission that involves learning to
address the bias/computation trade-off or that provides new
theoretical insight into this problem. A workshop paper should be no
more than six pages in the standard NIPS format. Authorship should not
be blind. Please submit a paper by emailing it in Postscript or PDF
format to coarse2fineNIPS2010 at gmail.com. We anticipate accepting six
such papers for poster presentations, some of which will also receive
an oral presentation. Please only submit an article if at least one of
the authors will be able to attend the workshop and present the work.
* Please use NIPS template and style files. No more than 6 pages,
authorship not blind.
* Submit to coarse2fineNIPS2010 at gmail.com by October 29.
Important Dates:
* Friday, October 29 -- Paper submission deadline
* Monday, November 8 -- Notification of acceptance
Organizers:
Ben Taskar taskar at cis.upenn.edu University of Pennsylvania
David Weiss djweiss at cis.upenn.edu University of Pennsylvania
Ben Sapp bensapp at cis.upenn.edu University of Pennsylvania
Slav Petrov petrov at cs.berkeley.edu Google Research, New York
---------- Forwarded message ----------
From: Marius Kloft <mkloft at eecs.berkeley.edu>
To: connectionists at cs.cmu.edu
Date: Wed, 01 Sep 2010 23:18:39 -0700
Subject: NIPS 2010 Workshop: New Directions in Multiple Kernel Learning -
Call for Contributions
=========================================================================
CALL FOR PAPERS
New Directions in Multiple Kernel Learning
NIPS 2010 Workshop, Whistler, British Columbia, Canada
http://doc.ml.tu-berlin.de/mkl_workshop
-- Submission Deadline: October 18, 2010 --
=========================================================================
Research on Multiple Kernel Learning (MKL) has matured to the point
where efficient systems can be applied out of the box to various
application domains. In contrast to last year's workshop, which
evaluated the achievements of MKL in the past decade, this workshop
looks beyond the standard setting and investigates new directions for
MKL.
In particular, we focus on two topics:
1. There are three research areas, which are closely related, but have
traditionally been treated separately: learning the kernel, learning
distance metrics, and learning the covariance function of a Gaussian
process. We therefore would like to bring together researchers from
these areas to find a unifying view, explore connections, and
exchange ideas.
2. We ask for novel contributions that take new directions, propose
innovative approaches, and take unconventional views. This includes
research, which goes beyond the limited classical sum-of-kernels
setup, finds new ways of combining kernels, or applies MKL in more
complex settings.
The workshop will include:
* A brief introduction talk
* 4 invited keynote talks on new views and directions in MKL
* 4 talks by authors of contributed papers
* A poster session of contributed papers, and a poster-spotlight
session
* A discussion panel
The organizing committee is seeking short research papers for
presentation at the workshop. The committee will select several
submitted papers for 15-minute talks and poster presentations. The
accepted papers will be published on the workshop web site.
We plan to publish proceedings of this workshop in a special issue of an
appropriate journal. We will submit a proposal for such an issue to the
Journal of Machine Learning Research.
Amongst others, we encourage submissions in the following areas:
* New views on MKL, e.g., from the perspectives of metric learning,
Gaussian processes, learning with similarity functions, etc.
* New approaches to MKL, in particular, kernel parameterizations
different than convex combinations and new objective functions
* Sparse vs. non-sparse regularization in similarity learning
* Use of MKL in unsupervised, semi-supervised, multi-task, and
transfer learning
* MKL with structured input/output
* Innovative applications
SUBMISSION GUIDELINES
Submissions should be written as extended abstracts, no longer than 4
pages in the NIPS latex style. Style files and formatting instructions
can be found at http://nips.cc/PaperInformation/StyleFiles. 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
ml-newtrendsinmkl at lists.tu-berlin.de
before October 18. Notifications will be given on Nov 2. Topics that
were recently published or presented elsewhere are allowed, provided
that the extended abstract mentions this explicitly.
ORGANIZERS:
Marius Kloft (UC Berkeley), Ulrich Rueckert (UC Berkeley),
Cheng Soon Ong (ETH Zuerich), Alain Rakotomamonjy (University of
Rouen), Soeren Sonnenburg (TU Berlin/Max Planck FML), Francis Bach
(ENS/INRIA)
WORKSHOP HOMEPAGE:
http://doc.ml.tu-berlin.de/mkl_workshop
---------- Forwarded message ----------
From: "Yu, Shipeng (H USA)" <shipeng.yu at siemens.com>
To: <connectionists at cs.cmu.edu>
Date: Thu, 26 Aug 2010 15:43:48 -0400
Subject: NIPS-2010 workshop on Predictive Models in Personalized Medicine
-- Apologies if you receive multiple copies of this announcement --
------------------------------------------------------------
CALL FOR CONTRIBUTIONS
NIPS 2010 workshop on
Predictive Models in Personalized Medicine
Whistler, BC, Canada, December, 2010
http://sites.google.com/site/personalmedmodels/
Important Dates:
----------------
Deadline for submissions: October 8, 2010
Notification of acceptance: November 12, 2010
Background:
----------------
Recently there has been a paradigm shift from evidence based medicine to
personalized medicine. Earlier optimal therapy selection based on
populations e.g. If a patient belonged to a homogenous category such as T2
stage, node negative, non-metastatic, non-small cell lung cancer, the best
treatment was selected on clinical trials for the various medications on the
same population. Historically, treatment is identical for all members of
this patient cohort. While this approach was developed to utilize the
statistical power of significantly large sample of a relatively homogeneous
group of patients, it ignores the heterogeneity of the individuals within
the cohort. This is slowly being replaced by personalized predictive models
utilize all available information from each patient (exams, demographics,
imaging, lab, genomic etc.) to identify optimal therapy in an individualized
manner. This approach improves outcomes because it exploits more detailed
patient information to reduce uncertainty in predicting patient outcomes as
a function of treatment.
This finds applications in preventive care, diagnosis, therapy selection and
monitoring. For example, a) predicting patients at risk of developing
hypertension and preventing manifestation ahead of time with appropriate
intervention (medications, diet, lifestyle changes etc.); b) improving the
early detection of cancer in asymptomatic patient; c) selecting the optimal
chemotherapy/radiation dosage or other therapy parameters based on patient
characteristics. Chemotherapy is expensive with terrible side effects and
often only works for less than 50% of the patients treated with it.
Identifying the right subset of patients that can benefit from it reduces
the costs and improves efficacy of the treatment. d) predicting patient
response to a given medication or/and treatment: Often the outcomes of
therapy manifest too late e.g. outcomes of chemo-radiation therapy in
patients with non-small cell lung cancer may take many months to manifest.
By monitoring surrogate markers, one may be able to predict poor outcomes
early on and modify the therapy plan. Also by predicting patient response
and adequate dosage for a given medication , undesirable possible drugs
adverse side effects can be avoided. A good example of this is the recent
work from the International Warfarin Pharmacogenetics Consortium (see
references) on estimation of the Warfarin Dose with Clinical and
Pharmacogenetic Data.
Goal:
----------------
The purpose of this cross-discipline workshop is to bring together machine
learning and healthcare researchers interested in problems and applications
of predictive models in the field of personalized medicine. The goal of the
workshop will be to bridge the gap between the theory of predictive models
and the applications and needs of the healthcare community. There will be
exchange of ideas, identification of important and challenging applications
and discovery of possible synergies. Ideally this will spur discussion and
collaboration between the two disciplines and result in collaborative grant
submissions. The emphasis will be on the mathematical and engineering
aspects of predictive models and how it relates to practical medical
problems.
Although, predictive modeling for healthcare has been explored by
biostatisticians for several decades, this workshop focuses on substantially
different needs and problems that are better addressed by modern machine
learning technologies. For example, how should we organize clinical trials
to validate the clinical utility of predictive models for personalized
therapy selection? This workshop does not focus on issues of basic science;
rather, we focus on predictive models that combine all available patient
data (including imaging, pathology, lab, genomics etc.) to impact point of
care decision making.
Topics of Interest:
----------------
We would like to encourage submissions on any of (but not limited to) the
following topics:
** Applications
- Personalized Medicine (individualized or sub-population based)
- Preventive Medicine
- Therapy Selection
- Precision Diagnostics (Disease Sub-typing, Precise Diagnosis)
- Companion Diagnostics and Therapeutics
- Patient Risk Assessment (for incidence of disease)
- Integrated Diagnostics combining modalities like imaging, genomics and
in-vitro diagnostics.
** Algorithms/Theory
- Dealing with missing data (e.g. data not missing at random)
- Inductive transfer for reducing sample sizes
- Feature Selection
- Classification
- Survival Analysis
- Data Challenges (Noise, other pre-processing)
- Statistical Methods for validating personalized predictive models (e.g.
Clinical Trials)
Submission Instructions:
------------------------
We call for paper contribution of up to 8 pages to the workshop using NIPS
style. Accepted papers will be either presented as a talk or poster (with
poster spotlight). They will also be available in an online proceedings that
will be made available prior to the workshop. Extended versions of some
accepted papers will also be invited for inclusion in an edited book on the
same topic as the workshop.
Papers should be emailed to the organizers at
personalmedmodels.nips10 at gmail.com. Please indicate your preference for oral
or poster presentation.
Organizers
----------
Faisal Farooq, Siemens Medical Solutions USA, Inc.
Balaji Krishnapuram, Siemens Medical Solutions USA, Inc.
Romer Rosales, Siemens Medical Solutions USA, Inc.
Glenn Fung, Siemens Medical Solutions USA, Inc.
Shipeng Yu, Siemens Medical Solutions USA, Inc.
Jude Shavlik, University of Wisconsin at Madison
----------------------------------------------------------------------------
This message and any included attachments are from Siemens Medical Solutions
and are intended only for the addressee(s).
The information contained herein may include trade secrets or privileged or
otherwise confidential information. Unauthorized review, forwarding, printing,
copying, distributing, or using such information is strictly prohibited and may
be unlawful. If you received this message in error, or have reason to believe
you are not authorized to receive it, please promptly delete this message and
notify the sender by e-mail with a copy to Central.SecurityOffice at siemens.com
Thank you
---------- Forwarded message ----------
From: Yanjun Qi <qiyanjun07 at gmail.com>
To: connectionists at cs.cmu.edu, Connectionists-Request at cs.cmu.edu
Date: Fri, 17 Sep 2010 17:48:54 -0400
Subject: [NIPS 2010 MLCB workshop ] Call for contributions - New Problems
and Methods in Computational Biology
----------
Call for contributions
New Problems and Methods in Computational Biology
http://www.mlcb.org
A workshop at the Twenty-Third Annual Conference on
Neural Information Processing Systems (NIPS 2010)
Whistler, BC, Canada, December 10 or 11, 2010.
Deadline for submission of extended abstracts: Oct 25, 2010,
WORKSHOP DESCRIPTION
The field of computational biology has seen dramatic growth over
the past few years, in terms of newly available data, new
scientific questions and new challenges for learning and
inference. In particular, biological data is often relationally
structured and highly diverse, and thus requires combining multiple
weak evidence from heterogeneous sources. These sources include
sequenced genomes of a variety of organisms, gene expression data
from multiple technologies, protein sequence and 3D structural
data, protein interaction data, gene ontology and pathway
databases, genetic variation data (such as SNPs), high-content
phenotypic screening data, and an enormous
amount of text data in the biological and medical literature. These
new types of scientific and clinical problems require novel
supervised and unsupervised learning approaches that can use these
growing resources.
The workshop will host presentations of emerging problems and
machine learning techniques in computational biology. We encourage
contributions describing either progress on new bioinformatics
problems or work on established problems using methods that are
substantially different from standard approaches. Kernel methods,
graphical models, semi-supervised approaches, feature selection
and other techniques applied to relevant bioinformatics problems
would all be appropriate for the workshop.
SUBMISSION INSTRUCTIONS
Researchers interested in contributing should upload an extended
abstract of 4 pages in PDF format to the MLCB submission web site
http://www.easychair.org/conferences/?conf=mlcb2010
by Oct 25, 2010, 11:59pm (Samoa time).
No special style is required. Authors may use the NIPS style file, but
are also free to use other styles as long as they use standard font
size (11 pt) and margins (1 in).
All submissions will be anonymously peer reviewed and will be
evaluated on the basis of their technical content. A strong
submission to the workshop typically presents a new learning method
that yields new biological insights, or applies an existing learning
method to a new biological problem. However, submissions that improve
upon existing methods for solving previously studied problems will
also be considered. Examples of research presented in previous years
can be found online at http://www.mlcb.org/nipscompbio/previous/.
Please note that accepted abstracts will be posted online at
www.mlcb.org. Authors may submit two versions of their abstract, a
longer version for review and a shorter version for posting to the web
page. In addition, we intent to make presentations be video taped and
published online as part of the videolectures.net website supported by
Pascal.
The workshop allows submissions of papers that are under review or
have been recently published in a conference or a journal. This is
done to encourage presentation of mature research projects that are
interesting to the community. The authors should clearly state any
overlapping published work at time of submission. Authors of
accepted abstracts will be invited to submit full length versions
of their contributions for publication in a special issue of BMC
Bioinformatics.
ORGANIZERS
Gunnar Rätsch,
Friedrich Miescher Laboratory of the Max Planck Society
Tomer Hertz,
Fred Hutchinson Cancer Research Center
Yanjun Qi,
Machine Learning Department, NEC Research
Jean-Philippe Vert,
Mines ParisTech, Institut Curie
PROGRAM COMMITTEE
Mathieu Blanchette, McGill University
Gal Chechik, Google Research
Florence d'Alche-Buc, Université d'Evry-Val d'Essonne, Genopole,
Eleazar Eskin, UC Los Angeles,
Brendan Frey (University of Toronto)
Alexander Hartemink (Duke University)
David Heckerman, Microsoft Research ,
Michael I. Jordan, UC Berkeley ,
Christina Leslie, Memorial Sloan-Kettering Cancer Research Center,
Michal Linial, The Hebrew University of Jerusalem ,
Quaid Morris, University of Toronto,
Klaus-Robert Müller, Fraunhofer FIRST ,
William Stafford Noble, Department of Genome Sciences, University of
Washington
Dana Pe'er, Columbia University ,
Uwe Ohler, Duke University ,
Alexander Schliep, Rutgers University,
Koji Tsuda, Computational Biology Research Center
Alexander Zien, LIFE Biosystems
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