Connectionists: CFP: D-7 reminder; Learning on Distributions, Functions, Graphs and Groups workshop @ NIPS-2017

[Zoltan Szabo]

Dear All,

This is a gentle reminder; the submission to the 'Learning on 
Distributions, Functions, Graphs and Groups workshop @ NIPS-2017' will 
close in 7 days.

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CALL FOR PAPERS:

Learning on Distributions, Functions, Graphs and Groups workshop @ NIPS-2017
December 8th, 2017
Long Beach, CA, U.S.
https://sites.google.com/site/nips2017learningon/

Important dates:
- Submission deadline: Oct. 10, 2017 (5pm, Pacific Time).
- Notification of acceptance: Oct. 20, 2017 (5pm, Pacific Time).

Confirmed speakers:
- Kenji Fukumizu (Institute for Statistical Mathematics, Tokyo)
- Hachem Kadri (Aix-Marseille University)
- Risi Kondor (University of Chicago)
- Simon Lacoste-Julien (University of Montreal)
- Barnabás Póczos (Carnegie Mellon University)

Description:
The increased variability of acquired data has recently pushed the field 
of machine learning to extend its scope to non-standard data including 
for example functional, distributional, graph, or topological data. 
Successful applications span across a wide range of disciplines such as 
healthcare, action recognition from iPod/iPhone accelerometer data, 
causal inference, bioinformatics, cosmology, acoustic-to-articulatory 
speech inversion, network inference, climate research, and ecological 
inference.

Leveraging the underlying structure of these non-standard data types 
often leads to significant boost in prediction accuracy and inference 
performance. In order to achieve these compelling improvements, however, 
numerous challenges and questions have to be addressed:
- choosing an adequate representation of the data,
- constructing appropriate similarity measures (inner product, norm or 
metric) on these representations,
- efficiently exploiting their intrinsic structure such as multi-scale 
nature or invariances,
- designing affordable computational schemes (relying e.g., on surrogate 
losses),
- understanding the computational-statistical tradeoffs of the resulting 
algorithms, and
- exploring novel application domains.

The goal of this workshop is
- to discuss new theoretical considerations and applications related to 
learning with non-standard data,
- to explore future research directions by bringing together 
practitioners with various domain expertise and algorithmic tools, and 
theoreticians interested in providing sound methodology,
- to accelerate the advances of this recent area and application arsenal.

We encourage submissions on a variety of topics, including but not 
limited to:
- Novel applications for learning on non-standard objects
- Learning theory/algorithms on distributions
- Topological and geometric data analysis
- Functional data analysis
- Multi-task learning, structured output prediction, and surrogate losses
- Vector-valued learning (e.g., operator-valued kernel)
- Gaussian processes
- Learning on graphs and networks
- Group theoretic methods and invariances in learning
- Learning with non-standard input/output data
- Large-scale approximations (e.g. sketching, random Fourier features, 
hashing, Nyström method, inducing point methods), and 
statistical-computational efficiency tradeoffs.

Organizers:
- Florence d'Alché-Buc (Télécom ParisTech, Paris-Saclay University)
- Krikamol Muandet (Mahidol University, MPI Tübingen)
- Bharath K. Sriperumbudur (Pennsylvania State University)
- Zoltán Szabó (École Polytechnique)

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Best,
Workshop Organizers