 
NUMML 2010 : Numerical Mathematics Challenges in Machine Learning (NIPS 2010 Workshop)  
Link: http://numml.kyb.tuebingen.mpg.de  
 
Call For Papers  
Dear colleagues,
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@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 highquality 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@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 eigensolvers. As long as problems are of moderate size, NM routines can be invoked in a blackbox fashion. However, for a growing number of realworld ML applications, this separation is insufficient and turns out to be a severe limit on further progress. The increasing complexity of realworld ML problems must be met with layered approaches, where algorithms are longrunning and reliable components rather than standalone 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 realworld 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 highlevel 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 realworld 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 realworld 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 NMML 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 lowrank updates. For very large problems, randomized or online algorithms become attractive, as do multilevel 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 hugescale 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 (consumerpreference prediction) * Randomized algorithms for lowrank 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 * Multigrid / multilevel methods * Exact solvers for very sparse matrices Application examples are:  Linear models / Gaussian MRF (mean computations),  Nonlinear optimization methods (trustregion, Newton steps, IRLS) C) Numerical linear algebra packages relevant to ML * LAPACK, BLAS, GotoBLAS, MKL, UMFPACK, PETSc, MPI D) Exploiting matrix/model structure, fast matrixvector multiplication * Matrix decompositions/approximations * Multipole 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 crossfertilization 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  
