Concept

Variance (mathématiques)

Publications associées (224)

Distributionally Robust Optimization with Markovian Data

We study a stochastic program where the probability distribution of the uncertain problem parameters is unknown and only indirectly observed via finitely many correlated samples generated by an unknown Markov chain with d states. We propose a data-driven d ...

2021

Forward-reflected-backward method with variance reduction

Volkan Cevher, Ahmet Alacaoglu

We propose a variance reduced algorithm for solving monotone variational inequalities. Without assuming strong monotonicity, cocoercivity, or boundedness of the domain, we prove almost sure convergence of the iterates generated by the algorithm to a soluti ...

2021

Generalization Comparison of Deep Neural Networks via Output Sensitivity

Patrick Thiran, Farnood Salehi, Mahsa Forouzesh

Although recent works have brought some insights into the performance improvement of techniques used in state-of-the-art deep-learning models, more work is needed to understand their generalization properties. We shed light on this matter by linking the lo ...

IEEE2021

Towards bootstrapping RG flows: sine-Gordon in AdS

The boundary correlation functions for a Quantum Field Theory (QFT) in an Anti-de Sitter (AdS) background can stay conformally covariant even if the bulk theory undergoes a renormalization group (RG) flow. Studying such correlation functions with the numer ...

SPRINGER2021

Frequency domain theory for functional time series: Variance decomposition and an invariance principle

Neda Mohammadi Jouzdani

This paper is concerned with frequency domain theory for functional time series, which are temporally dependent sequences of functions in a Hilbert space. We consider a variance decomposition, which is more suitable for such a data structure than the varia ...

INT STATISTICAL INST2020

Normal approximation of the solution to the stochastic heat equation with Levy noise

Carsten Hao Ye Chong

Given a sequence L & x2d9;epsilon of Levy noises, we derive necessary and sufficient conditions in terms of their variances sigma 2(epsilon) such that the solution to the stochastic heat equation with noise sigma(epsilon)-1L & x2d9;epsilon converges in law ...

SPRINGER2020

Uncertainty propagation for the design study of the PETALE experimental programme in the CROCUS reactor

Andreas Pautz, Vincent Pierre Lamirand, Dimitri Rochman, Axel Guy Marie Laureau

The PETALE experimental programme in the CROCUS reactor intends to provide integral measurements to constrain stainless steel nuclear data. This article presents the tools and the methodology developed to design and optimize the experiments, and its operat ...

2020

Fast ion transport phenomena in turbulent toroidal plasmas

Fabian Manke

In the field of plasma physics, suprathermal ions are encountered e.g. in astrophysical jets, the solar wind, as well as fusion plasmas, where they originate from neutral beam injection or fusion reactions. One aspect of shared interest with astrophysical ...

EPFL2020

Nonperturbative Mellin amplitudes: existence, properties, applications

Alexander Zhiboedov, João Pedro Alves Da Silva

We argue that nonperturbative CFT correlation functions admit a Mellin amplitude representation. Perturbative Mellin representation readily follows. We discuss the main properties of nonperturbative CFT Mellin amplitudes: subtractions, analyticity, unitari ...

SPRINGER2020

A Covariance Formula For Topological Events Of Smooth Gaussian Fields

Alejandro Rivera

We derive a covariance formula for the class of 'topological events' of smooth Gaussian fields on manifolds; these are events that depend only on the topology of the level sets of the field, for example, (i) crossing events for level or excursion sets, (ii ...

INST MATHEMATICAL STATISTICS2020

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