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We present a "black box" proof of mean-field near-critical behaviour for a family of functions on $\mathbb Z^d$ (${d>2}$) satisfying a short list of assumptions. The functions represent two-point functions of a lattice statistical…

Probability · Mathematics 2026-05-22 Hugo Duminil-Copin , Aman Markar , Romain Panis , Gordon Slade

We investigate the bond percolation model on transient weighted graphs ${G}$ induced by the excursion sets of the Gaussian free field on the corresponding metric graph. We assume that balls in ${G}$ have polynomial volume growth with growth…

Probability · Mathematics 2025-11-03 Alexander Drewitz , Alexis Prévost , Pierre-François Rodriguez

In unconstrained optimisation on an Euclidean space, to prove convergence in Gradient Descent processes (GD) $x_{n+1}=x_n-\delta _n \nabla f(x_n)$ it usually is required that the learning rates $\delta _n$'s are bounded: $\delta _n\leq…

Optimization and Control · Mathematics 2020-01-09 Tuyen Trung Truong

We investigate the spatial search problem on the two-dimensional square lattice, using the Dirac evolution operator discretised according to the staggered lattice fermion formalism. $d=2$ is the critical dimension for the spatial search…

Quantum Physics · Physics 2011-02-28 Apoorva Patel , K. S. Raghunathan , Md. Aminoor Rahaman

We consider oriented percolation on Z^d times Z_+ whose bond-occupation probability is pD(...), where p is the percolation parameter and D is a probability distribution on Z^d. Suppose that D(x) decays as |x|^{-d-\alpha} for some \alpha>0.…

Probability · Mathematics 2007-08-21 Lung-Chi Chen , Akira Sakai

The exponential decay of lattice Green functions is one of the main technical ingredients of the Ba{\l}aban's approach to renormalization. We give here a self-contained proof, whose various ingredients were scattered in the literature. The…

Mathematical Physics · Physics 2023-03-21 Wojciech Dybalski , Alexander Stottmeister , Yoh Tanimoto

We prove decoupling inequalities for the Gaussian free field on $\mathbb{Z}^d$, $d\geq 3$. As an application, we obtain exponential decay (with logarithmic correction for $d=3$) of the connectivity function of excursion sets for large…

Probability · Mathematics 2016-02-24 Serguei Popov , Balazs Rath

We investigate the bond percolation model on transient weighted graphs ${G}$ induced by the excursion sets of the Gaussian free field on the corresponding metric graph. Under the sole assumption that its sign clusters do not percolate, we…

Probability · Mathematics 2024-05-28 Alexander Drewitz , Alexis Prévost , Pierre-François Rodriguez

Gradient descent (GD) on logistic regression has many fascinating properties. When the dataset is linearly separable, it is known that the iterates converge in direction to the maximum-margin separator regardless of how large the step size…

Machine Learning · Computer Science 2025-07-16 Si Yi Meng , Baptiste Goujaud , Antonio Orvieto , Christopher De Sa

The lattice Green function, i.e., the resolvent of the discrete Laplace operator, is fundamental in probability theory and mathematical physics. We derive its long-distance behaviour via a detailed analysis of an integral representation…

Probability · Mathematics 2022-06-09 Emmanuel Michta , Gordon Slade

We study the behaviour of the rescaled minimal subtree containing the origin and K random vertices selected from a random critical (sufficiently spread-out, and in dimensions d > 8) lattice tree conditioned to survive until time ns, in the…

Probability · Mathematics 2025-03-30 Manuel Cabezas , Alexander Fribergh , Mark Holmes , Edwin Perkins

This is a short review of the two papers on the $x$-space asymptotics of the critical two-point function $G_{p_c}(x)$ for the long-range models of self-avoiding walk, percolation and the Ising model on $\mathbb{Z}^d$, defined by the…

Mathematical Physics · Physics 2019-10-23 Akira Sakai

We study gradient descent (GD) with a constant stepsize for $\ell_2$-regularized logistic regression with linearly separable data. Classical theory suggests small stepsizes to ensure monotonic reduction of the optimization objective,…

Machine Learning · Statistics 2025-11-04 Jingfeng Wu , Pierre Marion , Peter Bartlett

We show that there exists a very natural, superstatistics-linked extension of the central limit theorem (CLT) to deformed exponentials (also called q-Gaussians): This generalization favorably compares with the one provided by S. Umarov and…

Statistical Mechanics · Physics 2007-06-04 C. Vignat , A. Plastino

We study the existence of $L^2$ normalized solutions for nonlinear Schr\"odinger equations and systems. Under new Palais-Smale type conditions we develop new deformation arguments for the constraint functional on $S_m=\{ u; \,…

Analysis of PDEs · Mathematics 2023-12-18 Norihisa Ikoma , Kazunaga Tanaka

Logarithmic finite-size scaling of the O($n$) universality class at the upper critical dimensionality ($d_c=4$) has a fundamental role in statistical and condensed-matter physics and important applications in various experimental systems.…

Statistical Mechanics · Physics 2021-04-13 Jian-Ping Lv , Wanwan Xu , Yanan Sun , Kun Chen , Youjin Deng

The behavior of the bulk two-point correlation function $G({\bf r};T|d)$ in $d$-dimensional system with van der Waals type interactions is investigated and its consequences on the finite-size scaling properties of the susceptibility in such…

Statistical Mechanics · Physics 2009-11-07 Daniel M. Dantchev

We consider the wave equation with a cubic convolution $\partial_t^2 u-\Delta u=(|x|^{-\gamma}*u^2)u$ in three space dimensions. Here, $0<\gamma<3$ and $*$ stands for the convolution in the space variables. It is well known that if initial…

Analysis of PDEs · Mathematics 2020-10-02 Tomoyuki Tanaka , Kyouhei Wakasa

We consider the level-sets of continuous Gaussian fields on $\mathbb{R}^d$ above a certain level $-\ell\in \mathbb{R}$, which defines a percolation model as $\ell$ varies. We assume that the covariance kernel satisfies certain regularity,…

Probability · Mathematics 2021-06-15 Franco Severo

We study gradient descent (GD) dynamics on logistic regression problems with large, constant step sizes. For linearly-separable data, it is known that GD converges to the minimizer with arbitrarily large step sizes, a property which no…

Machine Learning · Computer Science 2024-11-05 Si Yi Meng , Antonio Orvieto , Daniel Yiming Cao , Christopher De Sa