Algebraic and Geometric Methods in Statistics: Exponential manifold by reproducing kernel Hilbert spaces

By Kenji Fukumizu. 2009

8 citations recorded by the source corpus.

Subfields: Applied Mathematics, Artificial Intelligence, Statistics and Probability

How researchers see this paper

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

In Applied Mathematics

1. Heiko Strathmann
2. Arthur Gretton
3. Le Song
4. Wenliang Li
5. Danica J. Sutherland
6. Alex Smola

In Artificial Intelligence

1. Arthur Gretton
2. Byron Boots
3. Le Song
4. Bernhard Schölkopf
5. Kenji Fukumizu
6. Sergey Levine
7. Alex Smola
8. Geoffrey J. Gordon

In Statistics and Probability

1. Arthur Gretton
2. Le Song
3. Bernhard Schölkopf
4. Kenji Fukumizu
5. Bharath K. Sriperumbudur
6. Alex Smola
7. Danica J. Sutherland
8. Heiko Strathmann

Citations in the corpus

• Hilbert Space Embeddings of Hidden Markov Models (2010, background): “Many problems, such as a practical consistent estimator, remain unsolved however (Fukumizu, 2009).”
• Learning deep kernels for exponential family densities (2018, background): “One promising approach for incorporating this necessary structure is the kernel exponential family (Canu and Smola 2006; Fukumizu 2009; Sriperumbudur et al. 2017).”