Bayesian Nonparametric Kernel-Learning
By Junier B. Oliva, Kumar Avinava Dubey, Andrew Gordon Wilson, Barnab'as P'oczos, Jeff Schneider, Eric P. Xing. 2015
73 citations recorded by the source corpus.
Kernel methods are ubiquitous tools in machine learning. However, there is often little reason for the common practice of selecting a kernel a priori. Even if a universal approximating kernel is selected, the quality of the finite sample estimator may be greatly affected by the choice of kernel. Furthermore, when directly applying kernel methods, one typically needs to compute a $N \times N$ Gram matrix of pairwise kernel evaluations to work with a dataset of $N$ instances. The computation of this Gram matrix precludes the direct application of kernel methods on large datasets, and makes kernel learning especially difficult. In this paper we introduce Bayesian nonparmetric kernel-learning (BaNK), a generic, data-driven framework for scalable learning of kernels. BaNK places a nonparametric prior on the spectral distribution of random frequencies allowing it to both learn kernels and scale to large datasets. We show that this framework can be used for large scale regression and classification tasks. Furthermore, we show that BaNK outperforms several other scalable approaches for kernel learning on a variety of real world datasets.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Guodong Zhang
- Chaoqi Wang
- Roger Baker Grosse
- Hung Hai Bui
- Wenyuan Zeng
- Kalyan Veeramachaneni
- Trung Le
- Lei Xu
Citations in the corpus
- Synthesizing Tabular Data using Generative Adversarial Networks (2018, methodology): “There are several statistical ways to generate this kind of synthetic data, including classification and regression trees [41, 29] and Bayesian networks [12, 30, 43].”
- Large-scale Online Kernel Learning with Random Feature Reparameterization (2017, background): “, spectral mixture kernels [Wilson and Adams, 2013] with both parametric and nonparametric [Oliva et al., 2015] versions) can also be derived as long as an appropriate mapping can be specified.”
- Differentiable Compositional Kernel Learning for Gaussian Processes (2018, background): “Deep kernel learning (DKL) (Wilson et al., 2016) further boosted the expressiveness by transforming the inputs of spectral mixture base kernel with a deep neural network.”