On Some Extensions of Bernstein's Inequality for Self-adjoint Operators

Lower bounds for the Helmholtz function

S. Golden · 1965

Earlier work this paper cites.

Inequality with applications in statistical mechanics

C. J. Thompson · 1965

Earlier work this paper cites.

Convex trace functions and the Wigner-Yanase-Dyson conjecture

E. H. Lieb · 1973

Earlier work this paper cites.

On tail probabilities for martingales

David A Freedman · 1975

Earlier work this paper cites.

Inégalités de Khintchine dans C p {C}_{p} ( 1 < p < ∞ 1<p<\infty )

Françoise Lust-Piquard · 1986

Earlier work this paper cites.

Non commutative khintchine and paley inequalities

Françoise Lust-Piquard and Gilles Pisier · 1991

Earlier work this paper cites.

Probability in Banach spaces

M. Ledoux and M. Talagrand · 1991

Cited alongside, same era.

Matrix analysis

R. Bhatia · 1997

Cited alongside, same era.

Random vectors in the isotropic position

Mark Rudelson · 1999

Cited alongside, same era.

Strong converse for identification via quantum channels

R. Ahlswede and A. Winter · 2002

Cited alongside, same era.

Trace inequalities and quantum entropy: an introductory course

E. Carlen · 2010

Cited alongside, same era.

Concentration of the adjacency matrix and of the Laplacian in random graphs with independent edges

Original

R. I. Oliveira · 2010

Cited alongside, same era.

Freedman’s inequality for matrix martingales

J. A. Tropp · 2011

Closest in time.

Tail inequalities for sums of random matrices that depend on the intrinsic dimension

D. Hsu, S. M. Kakade, and T. Zhang · 2012

Closest in time.

Convergence of stochastic processes

David Pollard · 2012

Closest in time.

User-friendly tail bounds for sums of random matrices

J. A. Tropp · 2012

Closest in time.

Probability inequalities for bounded random vectors

I. A. Ahmad and M. Amezziane · 2013

Closest in time.

Asymptotics and concentration bounds for bilinear forms of spectral projectors of sample covariance

Vladimir Koltchinskii, Karim Lounici, et al · 2016

Closest in time.