PAC-Bayesian Analysis of Co-clustering and Beyond
By Yevgeny Seldin, Naftali Tishby. 2010
90 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Molecular Biology, 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
- Pierre Alquier
- François Laviolette
- Yevgeny Seldin
- John Shawe-Taylor
- Pascal Germain
- Nicolò Cesa-Bianchi
- Christoph H. Lampert
- Jean-Francis Roy
In Molecular Biology
- François Laviolette
- Pierre Alquier
- John Shawe-Taylor
- Peter Auer
- Pascal Germain
- Nicolò Cesa-Bianchi
- Robert C. Williamson
- Ulrike von Luxburg
In Statistical Learning Theory
- Pierre Alquier
- François Laviolette
- Yevgeny Seldin
- Pascal Germain
- Jean-Francis Roy
- Nicolò Cesa-Bianchi
- John Shawe-Taylor
- Peter Auer
Citations in the corpus
- User-friendly introduction to PAC-Bayes bounds (2021, background): “• unsupervised learning, including clustering [177, 13], representation learning [149, 148], variational autoencoders [54].”
- PAC-Bayes-Bernstein Inequality for Martingales and its Application to Multiarmed Bandits (2011, methodology): “In fact, the only additional domain known to us is density estimation (Seldin and Tishby, 2010; Higgs and Shawe-Taylor, 2010).”
- Clustering: Science or Art? (2009, background): “Directly optimizing the “usefulness”? Often “one does not learn structure for its own sake, but rather to facilitate solving some higher level task” (Seldin and Tishby, 2010).”
- PAC-Bayesian Bounds based on the Rényi Divergence (2016, background): “A key step of most PAC-Bayesian proofs is summarized by the following change of measure inequality [Seldin and Tishby, 2010, McAllester, 2013, Ger-main et al., 2015].”
- Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm (2015, background): “We present here the same proof as in Seldin and Tishby (2010) and McAllester (2013).”