Publications associées à Fractional extreme distributions

Digital Transformation and Business Model Innovation in the Film Industry: The Case of Movieday.it

Digital technologies are enabling the transformation of the established market boundaries and business models of the cinema industry, bringing opportunities for a reconfiguration of the conceptual framework of movie content distribution through movie theat ...

Palgrave Macmillan2020

A Local Law for Singular Values from Diophantine Equations

Marius Christopher Lemm

We introduce the

N\times N

random matrices

X_{j,k}=\exp\left(2\pi i \sum_{q=1}^d\ \omega_{j,q} k^q\right) \quad \text{with } \{\omega_{j,q}\}_{\substack{1\leq j\leq N\\ 1\leq q\leq d}} \text{ i.i.d. random variables},

and

d

a fixed integer. We pr ...

2020

Bayesian Differential Privacy for Machine Learning

Boi Faltings, Aleksei Triastcyn

%0 Conference Paper %T Bayesian Differential Privacy for Machine Learning %A Aleksei Triastcyn %A Boi Faltings %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E A ...

2020

Max-infinitely divisible models and inference for spatial extremes

Emeric Rolland Georges Thibaud, Raphaël Huser

For many environmental processes, recent studies have shown that the dependence strength is decreasing when quantile levels increase. This implies that the popular max-stable models are inadequate to capture the rate of joint tail decay, and to estimate jo ...

WILEY2020

Redundant features can hurt robustness to distributions shift

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Guillermo Ortiz Jimenez, Apostolos Modas

In this work, we borrow tools from the field of adversarial robustness, and propose a new framework that permits to relate dataset features to the distance of samples to the decision boundary. Using this framework we identify the subspace of features used ...

2020

Redundant features can hurt robustness to distribution shift

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Guillermo Ortiz Jimenez, Apostolos Modas

In this work, we borrow tools from the field of adversarial robustness, and propose a new framework that permits to relate dataset features to the distance of samples to the decision boundary. Using this framework we identify the subspace of features used ...

2020

Fat navigators and Moire phase tracking comparison for motion estimation and retrospective correction

Daniel Gallichan, Frédéric Gretsch

Purpose To compare motion tracking by 2 modern methods (fat navigators [FatNavs] and Moir' phase tracking [MPT]) as well as their performance for retrospective correction of very high resolution acquisitions. Methods A direct comparison of FatNavs and MPT ...

2020

Non-perturbative probability distribution function for cosmological counts in cells

Sergey Sibiryakov, Mikhail Ivanov

We present a non-perturbative calculation of the 1-point probability distribution function (PDF) for the spherically-averaged matter density field. The PDF is represented as a path integral and is evaluated using the saddle-point method. It factorizes into ...

IOP PUBLISHING LTD2019

Statistical Aspects of Wasserstein Distances

Victor Panaretos, Yoav Zemel

Wasserstein distances are metrics on probability distributions inspired by the problem of optimal mass transportation. Roughly speaking, they measure the minimal effort required to reconfigure the probability mass of one distribution in order to recover th ...

ANNUAL REVIEWS2019

Global eigenvalue distribution of matrices defined by the skew-shift

Marius Christopher Lemm

We consider large Hermitian matrices whose entries are defined by evaluating the exponential function along orbits of the skew-shift

\binom{j}{2} \omega+jy+x \mod 1

for irrational

\omega

. We prove that the eigenvalue distribution of these matrices conv ...

2019

Fractional extreme distributions

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Digital Transformation and Business Model Innovation in the Film Industry: The Case of Movieday.it

A Local Law for Singular Values from Diophantine Equations

Bayesian Differential Privacy for Machine Learning

Max-infinitely divisible models and inference for spatial extremes

Redundant features can hurt robustness to distributions shift

Redundant features can hurt robustness to distribution shift

Fat navigators and Moire phase tracking comparison for motion estimation and retrospective correction

Non-perturbative probability distribution function for cosmological counts in cells

Statistical Aspects of Wasserstein Distances

Global eigenvalue distribution of matrices defined by the skew-shift

Digital Transformation and Business Model Innovation in the Film Industry: The Case of Movieday.it

Statistical Aspects of Wasserstein Distances

Redundant features can hurt robustness to distributions shift

Non-perturbative probability distribution function for cosmological counts in cells

A Local Law for Singular Values from Diophantine Equations

Bayesian Differential Privacy for Machine Learning

Max-infinitely divisible models and inference for spatial extremes

Global eigenvalue distribution of matrices defined by the skew-shift

Fat navigators and Moire phase tracking comparison for motion estimation and retrospective correction

Redundant features can hurt robustness to distribution shift