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Modern Nonparametric Methods in Machine Learning
Arthur Gretton · Mladen Kolar · Samory Kpotufe · John Lafferty · Han Liu · Bernhard Schölkopf · Alexander Smola · Rob Nowak · Mikhail Belkin · Lorenzo Rosasco · peter bickel · Yue Zhao

Mon Dec 09 07:30 AM -- 06:30 PM (PST) @ Harvey's Zephyr
Event URL: https://sites.google.com/site/nips2013modernnonparametric »

Modern data acquisition routinely produces massive and complex datasets. Examples are data from high throughput genomic experiments, climate data from worldwide data centers, robotic control data collected overtime in adversarial settings, user-behavior data from social networks, user preferences on online markets, and so forth. Modern pattern recognition problems arising in such disciplines are characterized by large data sizes, large number of observed variables, and increased pattern complexity. Therefore, nonparametric methods which can handle generally complex patterns are ever more relevant for modern data analysis. However, the larger data sizes and number of variables constitute new challenges for nonparametric methods in general. The aim of this workshop is to bring together both theoretical and applied researchers to discuss these modern challenges in detail, share insight on existing solutions, and lay out some of the important future directions.

Through a number of invited and contributed talks and a focused panel discussion, we plan to emphasize the importance of nonparametric methods and present challenges for modern nonparametric methods. In particular, we focus on the following aspect of nonparametric methods:

A. General motivations for nonparametric methods:

* the abundance of modern applications where little is known about data generating mechanisms (e.g., robotics, biology, social networks, recommendation systems)

* the ability of nonparametric analysis to capture general aspects of learning such as bias-variance tradeoffs, and thus yielding general insight on the inherent complexity of various learning tasks.

B. Modern challenges for nonparametric methods:

* handling big data: while large data sizes are a blessing w.r.t. generalization performance, they also present a modern challenge for nonparametric learning w.r.t. time-efficiency. In this context, we need to characterize trade-off between time and accuracy, create online or stream-based solutions, and develop approximation methods.

* larger problem complexity: large data is often paired with (1) large data dimension (number of observed variables), and (2) more complex target model spaces (e.g. less smooth regression function). To handle large data dimensions, likely solutions are methods that perform nonlinear dimension reduction, nonparametric variable selection, or adapt to the intrinsic dimension of the data. To handle the increased complexity of target model spaces, we require modern model selection procedures that can efficiently scale to modern data sizes while adapting to the complexity of the problem at hand.

Author Information

Arthur Gretton (Gatsby Unit, UCL)

Arthur Gretton is a Professor with the Gatsby Computational Neuroscience Unit at UCL. He received degrees in Physics and Systems Engineering from the Australian National University, and a PhD with Microsoft Research and the Signal Processing and Communications Laboratory at the University of Cambridge. He previously worked at the MPI for Biological Cybernetics, and at the Machine Learning Department, Carnegie Mellon University. Arthur's recent research interests in machine learning include the design and training of generative models, both implicit (e.g. GANs) and explicit (high/infinite dimensional exponential family models), nonparametric hypothesis testing, and kernel methods. He has been an associate editor at IEEE Transactions on Pattern Analysis and Machine Intelligence from 2009 to 2013, an Action Editor for JMLR since April 2013, an Area Chair for NeurIPS in 2008 and 2009, a Senior Area Chair for NeurIPS in 2018, an Area Chair for ICML in 2011 and 2012, and a member of the COLT Program Committee in 2013. Arthur was program chair for AISTATS in 2016 (with Christian Robert), tutorials chair for ICML 2018 (with Ruslan Salakhutdinov), workshops chair for ICML 2019 (with Honglak Lee), program chair for the Dali workshop in 2019 (with Krikamol Muandet and Shakir Mohammed), and co-organsier of the Machine Learning Summer School 2019 in London (with Marc Deisenroth).

Mladen Kolar (University of Chicago)
Samory Kpotufe (Princeton University)
John Lafferty (Yale University)
Han Liu (Tencent AI Lab)
Bernhard Schölkopf (MPI for Intelligent Systems, Tübingen)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

Alexander Smola (Amazon)

**AWS Machine Learning**

Rob Nowak (Wisconsin)
Mikhail Belkin (Ohio State University)
Lorenzo Rosasco (University of Genova- MIT - IIT)
peter bickel (UC Berkeley)
Yue Zhao (Cornell University)

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