Most machine learning (ML) algorithms rely fundamentally on concepts of numerical mathematics. Standard reductions to black-box computational primitives do not usually meet real-world demands and have to be modified at all levels. The increasing complexity of ML problems requires layered approaches, where algorithms are components rather than stand-alone tools fitted individually with much human effort. In this modern context, predictable run-time and numerical stability behavior of algorithms become fundamental. Unfortunately, these aspects are widely ignored today by ML researchers, which limits the applicability of ML algorithms to complex problems.

Our workshop aims to address these shortcomings, by trying to distill a compromise between inadequate black-box reductions and highly involved complete numerical analyses. We will invite speakers with interest in *both* numerical methodology *and* real problems in applications close to machine learning.

While numerical software packages of ML interest will be pointed out, our focus will rather be on how to best bridge the gaps between ML requirements and these computational libraries. A subordinate goal will be to address the role of parallel numerical computation in ML.

Examples of machine learning founded on numerical methods include low level computer vision and image processing, non-Gaussian approximate inference, Gaussian filtering / smoothing, state space models, approximations to kernel methods, and many more.

The workshop will comprise a panel discussion, in which the invited speakers are urged to address the problems stated above, and offer individual views and suggestions for improvement. We highly recommend active or passive attendance at this event. Potential participants are encouraged to contact the organizers beforehand, concerning points they feel should be addressed in this event.

Programme Committee

  • Matthias W. Seeger MPI Informatics / Saarland University, Saarbruecken
  • Suvrit Sra MPI Biological Cybernetics, Tuebingen
  • John P. Cunningham Stanford University (EE), Palo Alto