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Due to the rapid growth of data and computational resources, distributed optimization has become an active research area in recent years. While first-order methods seem to dominate the field, second-order methods are nevertheless attractive…

Machine Learning · Computer Science 2018-06-21 Celestine Dünner , Aurelien Lucchi , Matilde Gargiani , An Bian , Thomas Hofmann , Martin Jaggi

Continuity, compactness, the spectrum and ergodic properties of the differentiation operator are investigated, when it acts in the Fr\'echet space of all Dirichlet series that are uniformly convergent in all half-planes $\{s \in \mathbb{C}…

Functional Analysis · Mathematics 2020-03-12 José Bonet

We study diffusion processes driven by a Brownian motion with regular drift in a finite dimension setting. The drift has two components on different time scales, a fast conservative component and a slow dissipative component. Using the…

Probability · Mathematics 2014-03-27 Florent Barret , Max-K. Von Renesse

We generalize the results of \cite{Capistran2016} on expected Bayes factors (BF) to control the numerical error in the posterior distribution to an infinite dimensional setting when considering Banach functional spaces and now in a prior…

We study diffusion processes and stochastic flows which are time-changed random perturbations of a deterministic flow on a manifold. Using non-symmetric Dirichlet forms and their convergence in a sense close to the Mosco-convergence, we…

Probability · Mathematics 2020-09-22 Florent Barret , Olivier Raimond

We propose a second order differential calculus to analyze the regularity and the stability properties of the distribution semigroup associated with McKean-Vlasov diffusions. This methodology provides second order Taylor type expansions…

Probability · Mathematics 2020-01-07 M Arnaudon , P del Moral

We present a comprehensive study of semiclassical phase-space propagation in the Wigner representation, emphasizing numerical applications, in particular as an initial-value representation. Two semiclassical approximation schemes are…

Chemical Physics · Physics 2010-07-01 Thomas Dittrich , Edgar A. Gomez , Leonardo A. Pachon

Parafermions of order two and three are shown to be the fundamental tool to construct superspaces related to cubic and quartic extensions of the Poincar\'e algebra. The corresponding superfields are constructed, and some of their main…

High Energy Physics - Theory · Physics 2011-08-17 R. Campoamor-Stursberg , M. Rausch de Traubenberg

Evolution formulas of the density operator, the photon number distribution, and the Wigner function are derived for the problem on the optical fields propagation in realistic environments. The method of deriving these formulas is novel and…

Quantum Physics · Physics 2017-01-04 Xue-xiang Xu

We study a flavour-violating four-fermion interaction in the Lifshitz context, in 3+1 dimensions and with a critical exponent z=3. This model is renormalizable, and features dynamical mass generation, as well as asymptotic freedom. At…

High Energy Physics - Phenomenology · Physics 2013-05-30 J. Alexandre , J. Brister , N. Houston

We consider differential operators between sections of arbitrary powers of the determinant line bundle over a contact manifold. We extend the standard notions of the Heisenberg calculus: noncommutative symbolic calculus, the principal…

Mathematical Physics · Physics 2019-01-01 Charles H. Conley , Valentin Ovsienko

We study diffusions, variational principles and associated boundary value problems on directed graphs with natural weightings. Using random walks and exit times, we associate to certain subgraphs (domains) a pair of sequences, each of which…

Spectral Theory · Mathematics 2007-05-23 Patrick McDonald , Robert Meyers

We propose Dirichlet Simplex Nest, a class of probabilistic models suitable for a variety of data types, and develop fast and provably accurate inference algorithms by accounting for the model's convex geometry and low dimensional…

Machine Learning · Statistics 2019-05-28 Mikhail Yurochkin , Aritra Guha , Yuekai Sun , XuanLong Nguyen

In the language of $L^\infty$-modules proposed by Gigli, we introduce a first order calculus on a topological Lusin measure space $(M,\mathfrak{m})$ carrying a quasi-regular, strongly local Dirichlet form $\mathscr{E}$. Furthermore, we…

Differential Geometry · Mathematics 2022-05-25 Mathias Braun

This paper considers computational methods that split a vector field into three components in the case when both the vector field and the split components might be unbounded. We first employ classical Taylor expansion which, after some…

Numerical Analysis · Mathematics 2024-03-25 Arieh Iserles , Karolina Kropielnicka

This paper introduces a new approach to the study of rates of convergence for posterior distributions. It is a natural extension of a recent approach to the study of Bayesian consistency. In particular, we improve on current rates of…

Statistics Theory · Mathematics 2007-08-22 Stephen G. Walker , Antonio Lijoi , Igor Prünster

When the historical data are limited, the conditional probabilities associated with the nodes of Bayesian networks are uncertain and can be empirically estimated. Second order estimation methods provide a framework for both estimating the…

Machine Learning · Statistics 2022-08-09 Conrad D. Hougen , Lance M. Kaplan , Federico Cerutti , Alfred O. Hero

We consider the Dirichlet problem for equation involving a general operator associated with a symmetric transient regular Dirichlet form and bounded Borel measure on the right-hand side of the equation. We introduce a new function space…

Analysis of PDEs · Mathematics 2024-03-08 Andrzej Rozkosz , Tomasz Klimsiak

The problem of diffraction by a Dirichlet quarter-plane (a flat cone) in a 3D space is studied. The Wiener-Hopf equation for this case is derived and involves two unknown (spectral) functions depending on two complex variables. The aim of…

Analysis of PDEs · Mathematics 2021-02-09 R. C. Assier , A. V. Shanin

Modern large-scale statistical models require to estimate thousands to millions of parameters. This is often accomplished by iterative algorithms such as gradient descent, projected gradient descent or their accelerated versions. What are…

Machine Learning · Statistics 2020-03-04 Michael Celentano , Andrea Montanari , Yuchen Wu