Learning Kernels with Random Features

By Aman Sinha, John C. Duchi. 2016

108 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Simone Scardapane
2. Dianhui Wang
3. Mohammad Mahdi Derakhshani
4. Laura Palagi
5. Le Zhang

Citations in the corpus

• Kernel Continual Learning (2021, methodology): “Kernel methods (Sch ¨ olkopf & Smola, 2002) provide a well-suited tool for this due to their non-parametric nature, and have proven to be a powerful technique in machine learning (Cristianini et al., 2000; Smola & Sch ¨ olkopf, 2004; Rahimi & Recht, 2007; Sinha & Duchi, 2016).”
• Randomness in neural networks: an overview (2017, methodology): “• Instead of first choosing a kernel and then deriving an approximation, recently Sinha and Duchi showed that we can work with a generic random feature representation to learn a suitable kernel function for a given task.(68) • Finally, RFFs have also been studied for solving the last two classes of ”
• Learning to Learn Kernels with Variational Random Features (2020, unknown): “Instead of modeling the spectral distribution with explicit density functions, other works focus on optimizing the random base sampling strategy (Yang et al., 2015; Sinha & Duchi, 2016).”