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Kernel Learning: Automatic Selection of Optimal Kernels
Corinna Cortes · Arthur Gretton · Gert Lanckriet · Mehryar Mohri · Afshin Rostamizadeh

Sat Dec 13 07:30 AM -- 06:30 PM (PST) @ Westin: Nordic
Event URL: http://www.cs.nyu.edu/learning_kernels »

Kernel methods are widely used to address a variety of learning tasks including classification, regression, ranking, clustering, and dimensionality reduction. The appropriate choice of a kernel is often left to the user. But, poor selections may lead to sub-optimal performance. Furthermore, searching for an appropriate kernel manually may be a time-consuming and imperfect art. Instead, the kernel selection process can be included as part of the overall learning problem. In this way, better performance guarantees can be given and the kernel selection process can be made automatic. In this workshop, we will be concerned with using sampled data to select or learn a kernel function or kernel matrix appropriate for the specific task at hand. We will discuss several scenarios, including classification, regression, and ranking, where the use of kernels is ubiquitous, and different settings including inductive, transductive, or semi-supervised learning. We also invite discussions on the closely related fields of features selection and extraction, and are interested in exploring further the connection with these topics. The goal is to cover all questions related to the problem of learning kernels: different problem formulations, the computational efficiency and accuracy of the algorithms that address these problems and their different strengths and weaknesses, and the theoretical guarantees provided. What is the computational complexity? Does it work in practice? The formulation of some other learning problems, e.g. multi-task learning problems, is often very similar. These problems and their solutions will also be discussed in this workshop.

Author Information

Corinna Cortes (Google Research)
Arthur Gretton (Google Deepmind / 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).

Gert Lanckriet (U.C. San Diego)
Mehryar Mohri (Google Research & Courant Institute of Mathematical Sciences)
Afshin Rostamizadeh (Google Research)

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