REPRODUCING KERNEL HILBERT SPACES AND RANDOM MEASURES

By Charles Suquet. 2009

5 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Graphics and Computer-Aided Design

How researchers see this paper

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

In Artificial Intelligence

1. Bharath K. Sriperumbudur
2. Arthur Gretton
3. Kenji Fukumizu
4. Bernhard Schölkopf
5. Krikamol Muandet
6. Dino Sejdinovic
7. Gert R. G. Lanckriet
8. Le Song

In Computer Graphics and Computer-Aided Design

1. Nishant A. Mehta
2. Kenji Fukumizu
3. Alexander G. Gray
4. Gert R. G. Lanckriet
5. Bharath K. Sriperumbudur

Citations in the corpus

• Generative and Latent Mean Map Kernels (2010, background): “Guilbart [7] and later Suquet [24] (also see [25] for a related English-language paper) previously extended reproducing kernels to kernels on bounded signed measures.”
• Universality, Characteristic Kernels and RKHS Embedding of Measures (2010, background): “The authors thank anonymous reviewers for their constructive comments that greatly improved the manuscript and also for pointing out to Suquet (2009). B.”
• On the empirical estimation of integral probability metrics (2012, background): “(d) Kernel distance: γF is called the kernel distance or maximum mean discrepancy [6, 20, 21, 46] when F = {f : ‖f‖H ≤ 1}, where H represents a reproducing kernel Hilbert space (RKHS) [2] with k as its reproducing kernel (r.”