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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. For problems 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 limit on further progress.\par
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. \par
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, signal processing, and ML. Speakers are briefed
to provide specific highlevel examples of interest to ML and to point out
accessible software. We will initiate discussions about how to best bridge gaps
between ML requirements and NM interfaces and terminology. \par
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.
\par\noindent Target audience: \par
Our workshop is targeted towards practitioners from NIPS, but is of interest
to numerical linear algebra researchers as well.
Author Information
Matthias Seeger (Amazon)
Suvrit Sra (MIT)
Suvrit Sra is a faculty member within the EECS department at MIT, where he is also a core faculty member of IDSS, LIDS, MITML Group, as well as the statistics and data science center. His research spans topics in optimization, matrix theory, differential geometry, and probability theory, which he connects with machine learning  a key focus of his research is on the theme "Optimization for Machine Learning” (http://optml.org)
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