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Relations
between
machine learning problems – an approach to
unify the field
NIPS 2011
Workshop,
December 16 or 17, 2011, Sierra
Nevada,Spain
The only
essential knowledge pertains to the
inter-relatedness
of things
--- Jorge Luis Borges
This workshop will focus on relations between machine learning
problems. The idea is that by better understanding how different
machine learning problems relate to each other, we will be able to
better understand the field as a whole.
The idea of a relation is quite general. In includes such notions as
reductions between learning problems, but is not restricted to that.
Our goal can be explained by an analogy with functional analysis -
rather than studying individual functions, functional analysis focusses
on the transformations between different functions. This high level of
abstraction led to enormous advances in mathematics.
The motivation for the
workshop is several-fold:
- End users typically
only care about solving their problem,
not the technique used.
Many machine learning techniques still require a detaile dunderstanding
of their operation in order to use them
- ML as a service Much modern software is evolving to being
delivered via the web as a service. What does it mean for Machine
Learning to be delivered as a service? One question that needs
resolving is how to describe what the service does (ideally in a
declarative manner). Understanding relations between machine learning
problems can be thus seen as analogous to the composition of (Machine
Learning) web services
- Reinvention Many
machine learning solutions are reinvented / rediscovered. This is
hardly surprising since the focus is often on techniques and not
problems. If you can not describe your problem in a manner that others
can easily understand and search, then it is hard to figure whether
solutions to seemingly new problems already exist.
- Modularity A
feature of mature engineering disciplines is modularity, which has
enormous design
and
economic advantages. Understanding relations between problems
seems important to achieve greater modularity.
- Conceptual simplicity
Finally, if one can understand the field using a smaller number of
primitives and combination operations, then this has an intrinsic
appeal (apply Occam's razor at the
meta-level!)
Here is a more detailed description.
We aim to prepare a position paper
by mid-September that better summarises our thinking and the possible
directions forward.
Deadlines
- Submissions
30 September, 2011
- Notifications
23 October, 2011
- Registration (See nips website)
Organisers
Bob Williamson
John Langford
Ulrike
von Luxburg
Mark Reid
Jennifer Wortman
Vaughan
Format
- Introductory overview linked
to the position paper (the organisers)
- We need a BIT more GUTS (=Grand Unified
Theory of Statistics) (Peter Grünwald)
- Machine
Learning Markets: Putting your money where your mouth is (Amos Storkey)
- Contributed talks
- Panel discussion: How to build a map of
all of machine learning?
Publication
We will seek funding to enable the talks to be
recorded and published by videolectures.net
We do not intend to publish
workshop papers (we leave it to individual authors to choose the
best venue for their final papers).
Contact
For enquiries and submissions email rml@anu.edu.au
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