
Open access
Date
2021Type
- Journal Article
ETH Bibliography
yes
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Abstract
Kernel methods on discrete domains have shown great promise for many challenging data types, for instance, biological sequence data and molecular structure data. Scalable kernel methods like Support Vector Machines may offer good predictive performances but do not intrinsically provide uncertainty estimates. In contrast, probabilistic kernel methods like Gaussian Processes offer uncertainty estimates in addition to good predictive performance but fall short in terms of scalability. While the scalability of Gaussian processes can be improved using sparse inducing point approximations, the selection of these inducing points remains challenging. We explore different techniques for selecting inducing points on discrete domains, including greedy selection, determinantal point processes, and simulated annealing. We find that simulated annealing, which can select inducing points that are not in the training set, can perform competitively with support vector machines and full Gaussian processes on synthetic data, as well as on challenging real-world DNA sequence data. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000497930Publication status
publishedExternal links
Journal / series
IEEE AccessVolume
Pages / Article No.
Publisher
IEEESubject
Kernel; Gaussian processes; Uncertainty; Training; Optimization; Simulated annealing; DNA; machine learning; uncertainty quantification; discrete optimizationOrganisational unit
09568 - Rätsch, Gunnar / Rätsch, Gunnar
Related publications and datasets
Is new version of: http://hdl.handle.net/20.500.11850/316357
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ETH Bibliography
yes
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