Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint

By Bharath K. Sriperumbudur, Kenji Fukumizu, Gert R. G. Lanckriet. 2011

34 citations recorded by the source corpus.

Subfields: 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 Statistical Learning Theory

1. Kenji Fukumizu
2. Arthur Gretton
3. Bharath K. Sriperumbudur
4. Bernhard Schölkopf
5. Krikamol Muandet
6. Alex Smola
7. Le Song
8. Karsten M. Borgwardt

Citations in the corpus

• Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “This survey will deal mostly with the embedding of distributions in a Hilbert space, while extensions to Banach spaces are investigated in Zhang et al. (2009) and Sriperumbudur et al. (2011b).”
• On the optimal estimation of probability measures in weak and strong topologies (2013, neutral): “Suppose ∗ ∈ K ∗ ∈ X ∀ ∈ X [35, Lemma 4.28] (also see [32, Theorem 5]), it follows that the inclusion id : H ∗ → C 0 ( ) is well-defined and continuous.”
• A Kernel Two-Sample Test (2012, background): “Sriperumbudur et al. (2011b) provide expressions for the maximum mean discrepancy in terms of mean embeddings in reproducing kernel Banach spaces.”