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Deep generative models such as GANs, normalizing flows, and diffusion models are powerful regularizers for inverse problems. They exhibit great potential for helping reduce ill-posedness and attain high-quality results. However, the latent…

Computer Vision and Pattern Recognition · Computer Science 2024-07-30 Dongzhuo Li

The purpose of this paper is to generalize our earlier work on the logarithm of the Riemann zeta-function to linear combinations of logarithms of primitive Dirichlet $L$-functions with constant real coefficients. Under the assumption of…

Number Theory · Mathematics 2022-01-13 Fatma Çiçek

Gaussian random number generators attract a widespread interest due to their applications in several fields. Important requirements include easy implementation, tail accuracy, and, finally, a flat spectrum. In this work, we study the…

Information Theory · Computer Science 2024-04-04 Francisco-Javier Soto , Ana I. Gómez , Domingo Gómez-Pérez

We consider the disordered monomer-dimer model on general finite graphs with bounded degrees. Under the finite fourth moment assumption on the weight distributions, we prove a Gaussian central limit theorem for the free energy of the…

Probability · Mathematics 2024-06-26 Wai-Kit Lam , Arnab Sen

We present a polynomial-time reduction of the discrete logarithm problem in any periodic (a.k.a. torsion) semigroup (SGDLP) to the same problem in a subgroup of the same semigroup. It follows that SGDLP can be solved in polynomial time by…

Cryptography and Security · Computer Science 2016-07-26 Matan Banin , Boaz Tsaban

In this paper, we propose distributed algorithms that solve a system of Boolean equations over a network, where each node in the network possesses only one Boolean equation from the system. The Boolean equation assigned at any particular…

Optimization and Control · Mathematics 2021-03-04 Hongsheng Qi , Bo Li , Rui-Juan Jing , Lei Wang , Alexandre Proutiere , Guodong Shi

We introduce a general framework allowing to apply the theory of regularity structures to discretisations of stochastic PDEs. The approach pursued in this article is that we do not focus on any one specific discretisation procedure.…

Probability · Mathematics 2024-04-15 Dirk Erhard , Martin Hairer

We present here an elementary example, for every fixed positive integer $k,$ of a strictly stationary nongaussian stochastic process in discrete time, all of whose $k$-marginals are gaussian.

Probability · Mathematics 2012-10-30 K. R. Parthasarathy

Generating data from discrete distributions is important for a number of application domains including text, tabular data, and genomic data. Several groups have recently used random $k$-satisfiability ($k$-SAT) as a synthetic benchmark for…

Machine Learning · Computer Science 2026-03-24 Alankrita Bhatt , Mukur Gupta , Germain Kolossov , Andrea Montanari

We introduce methods to bound the mean of a discrete distribution (or finite population) based on sample data, for random variables with a known set of possible values. In particular, the methods can be applied to categorical data with…

Statistics Theory · Mathematics 2021-11-16 Eric Bax , Frédéric Ouimet

We show that the distribution of symmetry of a naturally reductive nilpotent Lie group coincides with the invariant distribution induced by the set of fixed vectors of the isotropy. This extends a known result on compact naturally reductive…

Differential Geometry · Mathematics 2017-10-16 Silvio Reggiani

We show how the renormalization group approach can be used to prove quantitative central limit theorems (CLTs) in the setting of free, Boolean, bi-free and bi-Boolean independence under finite third moment assumptions. The proofs rely on…

Probability · Mathematics 2026-03-30 Jad Hamdan

We show how to efficiently count and generate uniformly at random finitely generated subgroups of the modular group $\textsf{PSL}(2,\mathbb{Z})$ of a given isomorphism type. The method to achieve these results relies on a natural map of…

Group Theory · Mathematics 2024-12-10 Frédérique Bassino , Cyril Nicaud , Pascal Weil

We show that the zeros of random sequences of Gaussian systems of polynomials of increasing degree almost surely converge to the expected limit distribution under very general hypotheses. In particular, the normalized distribution of zeros…

Complex Variables · Mathematics 2015-05-13 Bernard Shiffman

Reparameterizable densities are an important way to learn probability distributions in a deep learning setting. For many distributions it is possible to create low-variance gradient estimators by utilizing a `reparameterization trick'. Due…

Machine Learning · Statistics 2019-03-13 Luca Falorsi , Pim de Haan , Tim R. Davidson , Patrick Forré

Deep generative models provide a systematic way to learn nonlinear data distributions, through a set of latent variables and a nonlinear "generator" function that maps latent points into the input space. The nonlinearity of the generator…

Machine Learning · Statistics 2021-12-14 Georgios Arvanitidis , Lars Kai Hansen , Søren Hauberg

We show that the expected value of the mean width of a random polytope generated by $N$ random vectors ($n\leq N\leq e^{\sqrt n}$) uniformly distributed in an isotropic convex body in $\R^n$ is of the order $\sqrt{\log N} L_K$. This…

Functional Analysis · Mathematics 2012-05-29 David Alonso-Gutierrez , Joscha Prochno

This paper demonstrates that basic statistics (mean, variance) of the logarithm of the variate itself can be used in the calculation of differential entropy among random variables known to be multiples and powers of a common underlying…

Information Theory · Computer Science 2009-01-26 Thomas M. Eccardt

Gaussian elimination is used in special linear groups to solve the word problem. In this paper, we extend Gaussian elimination to unitary groups. These algorithms have an application in building a public-key cryptosystem, we demonstrate…

Group Theory · Mathematics 2015-11-05 Ayan Mahalanobis , Anupam Singh

We provide improved differentially private algorithms for identity testing of high-dimensional distributions. Specifically, for $d$-dimensional Gaussian distributions with known covariance $\Sigma$, we can test whether the distribution…

Data Structures and Algorithms · Computer Science 2022-07-26 Shyam Narayanan