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Workshop
Nonparametric Methods for Large Scale Representation Learning
Andrew G Wilson · Alexander Smola · Eric Xing

Fri Dec 11 05:30 AM -- 03:30 PM (PST) @ 511 c
Event URL: http://www.cs.cmu.edu/~andrewgw/scalable.html »

In 2015, every minute of the day, users share hundreds of thousands of pictures, videos, tweets, reviews, and blog posts. More than ever before, we have access to massive datasets in almost every area of science and engineering, including genomics, robotics, and climate science. This wealth of information provides an unprecedented opportunity to automatically learn rich representations of data, which allows us to greatly improve performance in predictive tasks, but also provides a mechanism for scientific discovery. That is, by automatically learning expressive representations of data, versus carefully hand crafting features, we can obtain a new theoretical understanding of our modelling problems. Recently, deep learning architectures have had success for such representation learning, particularly in computer vision and natural language processing.

Expressive non-parametric methods also have great potential for large-scale structure discovery; indeed, these methods can be highly flexible, and have an information capacity that grows with the amount of available data. However, there are practical challenges involved in developing non-parametric methods for large scale representation learning.

Consider, for example, kernel methods. A kernel controls the generalisation properties of these methods. A well chosen kernel leads to impressive empirical performances. Difficulties arise when the kernel is a priori unknown and the number of datapoints is large. One must develop an expressive kernel learning approach, and scaling such an approach poses different challenges than scaling a standard kernel method. One faces additional computational constraints, and the need to retain significant model structure for expressing the rich information available in a large dataset. However, the need for expressive kernel learning on large datasets is especially great, since such datasets often provide more information to automatically learn an appropriate statistical representation.

This 1 day workshop is about non-parametric methods for large scale structure learning, including automatic pattern discovery, extrapolation, manifold learning, kernel learning, metric learning, data compression, feature extraction, trend filtering, and dimensionality reduction. Non-parametric methods include, for example, Gaussian processes, Dirichlet processes, Indian buffet processes, and support vector machines. We are particularly interested in developing scalable and expressive methods to derive new scientific insights from large datasets. A poster session, coffee breaks, and a panel guided discussion will encourage interaction between attendees. This workshop aims to bring together researchers wishing to explore alternatives to neural networks for learning rich non-linear function classes, with an emphasis on nonparametric methods, representation learning and scalability. We wish to carefully review and enumerate modern approaches to these challenges, share insights into the underlying properties of these methods, and discuss future directions.

Author Information

Andrew G Wilson (Carnegie Mellon University)
Alexander Smola (Carnegie Mellon University)

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Eric Xing (Carnegie Mellon University)

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