Results 41 to 50 of about 10,290,926 (277)

Tight concentration inequalities for quantum adversarial setups exploiting permutation symmetry [PDF]

open access: yesQuantum
We developed new concentration inequalities for a quantum state on an N-qudit system or measurement outcomes on it that apply to an adversarial setup, where an adversary prepares the quantum state.
Takaya Matsuura   +4 more
semanticscholar   +1 more source

Combinatorial anti-concentration inequalities, with applications [PDF]

open access: yesMathematical Proceedings of the Cambridge Philosophical Society, 2019
We prove several different anti-concentration inequalities for functions of independent Bernoulli-distributed random variables. First, motivated by a conjecture of Alon, Hefetz, Krivelevich and Tyomkyn, we prove some “Poisson-type” anti-concentration ...
Jacob Fox, Matthew Kwan, Lisa Sauermann
semanticscholar   +1 more source

Inequalities in the utilisation of maternal health Care in Rural India: Evidences from National Family Health Survey III & IV

open access: yesBMC Public Health, 2020
Background Since the implementation of National Rural Health Mission (NRHM) in 2005, Maternal Mortality Ratio has significantly declined in India through a noticeable improvement in maternal health care services. However, India did not succeed to achieve
Balhasan Ali, Shekhar Chauhan
doaj   +1 more source

Concentration inequalities for random matrix products [PDF]

open access: yesLinear Algebra and its Applications, 2019
Suppose $\{ X_k \}_{k \in \mathbb{Z}}$ is a sequence of bounded independent random matrices with common dimension $d\times d$ and common expectation $\mathbb{E}[ X_k ]= X$. Under these general assumptions, the normalized random matrix product $$Z_n = (I +
Amelia Henriksen, Rachel A. Ward
semanticscholar   +1 more source

Concentration inequalities for Stochastic Differential Equations with additive fractional noise [PDF]

open access: yesElectronic Journal of Probability, 2019
In this paper, we establish concentration inequalities both for functionals of the whole solution on an interval [0, T ] of an additive SDE driven by a fractional Brownian motion with Hurst parameter H $\in$ (0, 1) and for functionals of discrete-time ...
Maylis Varvenne
semanticscholar   +1 more source

Weighted Poincaré inequalities, concentration inequalities and tail bounds related to Stein kernels in dimension one [PDF]

open access: yesBernoulli, 2018
We investigate links between the so-called Stein's density approach in dimension one and some functional and concentration inequalities. We show that measures having a finite first moment and a density with connected support satisfy a weighted Poincare ...
Adrien Saumard
semanticscholar   +1 more source

Concentration Inequalities for Randomly Permuted Sums [PDF]

open access: yesProgress in probability, 2018
Initially motivated by the study of the non-asymptotic properties of non-parametric tests based on permutation methods, concentration inequalities for uniformly permuted sums have been largely studied in the literature. Recently, Delyon et al.
Mélisande Albert
semanticscholar   +1 more source

Statistical agnostic mapping: a framework in neuroimaging based on concentration inequalities [PDF]

open access: yesbioRxiv, 2019
In the 70s a novel branch of statistics emerged focusing its effort in selecting a function in the pattern recognition problem, which fulfils a definite relationship between the quality of the approximation and its complexity.
J. M. Górriz   +13 more
semanticscholar   +1 more source

A concentration inequality for the facility location problem [PDF]

open access: yesOperations Research Letters, 2022
We give a concentration inequality for a stochastic version of the facility location problem. We show the objective $C_n = \min_{F \subseteq [0,1]^2}|F|+\sum_{x\in X}\min_{f\in F}\|x-f\|$ is concentrated in an interval of length $O(n^{1/6})$ and $\E[C_n]=Θ(n^{2/3})$ if the input $X$ consists of i.i.d. uniform points in the unit square. Our main tool is
openaire   +3 more sources

Selective Reviews of Bandit Problems in AI via a Statistical View

open access: yesMathematics
Reinforcement Learning (RL) is a widely researched area in artificial intelligence that focuses on teaching agents decision-making through interactions with their environment.
Pengjie Zhou, Haoyu Wei, Huiming Zhang
doaj   +1 more source

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