From Points to Probability Measures: Statistical Learning on Distributions with Kernel Mean Embedding

By Krikamol Muandet. 2015

4 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Representation & Self-Supervised Learning, Statistical Learning Theory

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. Bernhard Schölkopf
2. David Lopez-Paz
3. Bharath K. Sriperumbudur
4. Krikamol Muandet
5. Kenji Fukumizu
6. Arthur Gretton
7. Dino Sejdinovic
8. Dacheng Tao

In Representation & Self-Supervised Learning

1. Bharath K. Sriperumbudur
2. David Lopez-Paz
3. Bernhard Schölkopf
4. Kenji Fukumizu
5. Krikamol Muandet
6. Arthur Gretton
7. Kun Zhang
8. Dacheng Tao

In Statistical Learning Theory

1. Bharath K. Sriperumbudur
2. Kenji Fukumizu
3. Bernhard Schölkopf
4. Krikamol Muandet
5. David Lopez-Paz
6. Arthur Gretton
7. Dino Sejdinovic
8. Le Song

Citations in the corpus

• Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “Note that the works of Muandet et al. (2014a; 2016) differ from the Stein’s seminal work and those along this line. That is, the setting of Muandet et al. (2016) involves a non-linear feature map φ”
• From Dependence to Causation (2016, methodology): “To this end, we will use kernel mean embeddings (Smola et al., 2007; Muandet, 2015).”