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Some deviation inequalities
TL;DR: In this article, a concentration property called Property~($\scriptstyle{R^n}$ was introduced for probability measures, which has an interesting stability under products and contractions.
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Abstract: We introduce a concentration property for probability measures on $\scriptstyle{R^n}$, which we call Property~($\scriptstyle\tau$); we show that this property has an interesting stability under products and contractions (Lemmas 1,~2,~3). Using property~($\scriptstyle\tau$), we give a short proof for a recent deviation inequality due to Talagrand. In a third section, we also recover known concentration results for Gaussian measures using our approach.}
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Optimal Transport: Old and New
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References
•Book
Optimal Transport: Old and New
Cédric Villani
- 02 Jan 2013
TL;DR: In this paper, the authors provide a detailed description of the basic properties of optimal transport, including cyclical monotonicity and Kantorovich duality, and three examples of coupling techniques.
7.4K
Generalization of an Inequality by Talagrand and Links with the Logarithmic Sobolev Inequality
Felix Otto,Cédric Villani +1 more
TL;DR: In this paper, it was shown that transport inequalities, similar to the one derived by M. Talagrand (1996, Geom. Funct. Anal. 6, 587-600) for the Gaussian measure, are implied by logarithmic Sobolev inequalities.
1.3K
•Book
Asymptotic theory of finite dimensional normed spaces
Vitali Milman,Gideon Schechtman +1 more
- 01 Jan 1986
TL;DR: The concentration of measure phenomenons in the theory of Normed spaces was discussed in this paper, where the Rademacher projection was applied to the case of finite Dimensional Normed Spaces.
1.2K
•Book
The volume of convex bodies and Banach space geometry
Gilles Pisier
- 01 Jan 1989
TL;DR: In this paper, the authors present a proof of the QS theorem for weak Hilbert spaces and weak cotype for weak type 2... and weak Hilbert space for weak Cotype.
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•Book
High-Dimensional Statistics: A Non-Asymptotic Viewpoint
Martin J. Wainwright
- 11 Apr 2019
TL;DR: This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level, and includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices.
1K