Concentration inequalities using the entropy method
By Stéphane Boucheron, Gábor Lugosi, Pascal Massart. 2003
91 citations recorded by the source corpus.
Subfields: Applied Mathematics, Discrete Mathematics and Combinatorics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/116483ad6ffa250a6035103cfe4b7d8052d43a9f
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
- Gábor Lugosi
- Stéphane Boucheron
- Holger Rauhut
- Tong Zhang
- R. Meir
- Pascal Massart
- Radosław Adamczak
- Tino Ullrich
In Discrete Mathematics and Combinatorics
In Statistics and Probability
- Stéphane Boucheron
- Gábor Lugosi
- Francis R. Bach
- Tong Zhang
- Holger Rauhut
- Pascal Massart
- R. Meir
- Shahar Mendelson
Citations in the corpus
- Generalization Error Bounds for Bayesian Mixture Algorithms (2003, methodology): “More recent techniques based on more refined concentration inequalities (e.g. Boucheron et al. 2003, Bartlett et al. 2002a, Mannor et al. 2003) are sometimes able to achieve faster rates of convergence under favorable circumstances.”
- Compressive Sensing with structured random matrices (2012, background): “Extensions of deviation and concentration inequalities can be found in [10, 9, 79].”
- On concentration of self-bounding functions (2009, methodology): “This inequality is at the basis of several concentration inequalities proved by the entropy method, see[2; 3 ; 17; 16; 4; 11; 13].”