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Score-based diffusion models have demonstrated outstanding empirical performance in machine learning and artificial intelligence, particularly in generating high-quality new samples from complex probability distributions. Improving the…

Machine Learning · Statistics 2025-05-30 Yuchen Jiao , Gen Li

We introduce two families of inequalities. Large ensemble decoupling is connected to the continuous restriction phenomenon. Tight decoupling is connected to the discrete Restriction conjecture for the sphere. Our investigation opens new…

Classical Analysis and ODEs · Mathematics 2024-02-08 Ciprian Demeter

In this paper, a derandomized algorithm for sampling decoding is proposed to achieve near-optimal performance in lattice decoding. By setting a probability threshold to sample candidates, the whole sampling procedure becomes deterministic,…

Information Theory · Computer Science 2016-11-17 Zheng Wang , Shuiyin Liu , Cong Ling

This paper introduces an upper bound on the absolute difference between: (a) the cumulative distribution function (CDF) of the sum of a finite number of independent and identically distributed random variables with finite absolute third…

Information Theory · Computer Science 2020-07-22 Dadja Anade , Jean-Marie Gorce , Philippe Mary , Samir Perlaza

Let $F$ be a class of functions on a probability space $(\Omega,\mu)$ and let $X_1,...,X_k$ be independent random variables distributed according to $\mu$. We establish high probability tail estimates of the form $\sup_{f \in F} |\{i :…

Probability · Mathematics 2007-05-23 Shahar Mendelson

In a celebrated work by Hoeffding [J. Amer. Statist. Assoc. 58 (1963) 13-30], several inequalities for tail probabilities of sums M_n=X_1+... +X_n of bounded independent random variables X_j were proved. These inequalities had a…

Probability · Mathematics 2007-05-23 Vidmantas Bentkus

We study boundary non-crossing probabilities $$ P_{f,u} := \mathrm P\big(\forall t\in \mathbb T\ X_t + f(t)\le u(t)\big) $$ for continuous centered Gaussian process $X$ indexed by some arbitrary compact separable metric space $\mathbb T$.…

Probability · Mathematics 2020-03-16 Enkelejd Hashorva , Yuliya Mishura , Georgiy Shevchenko

We consider the following oriented percolation model of $\mathbb {N} \times \mathbb{Z}^d$: we equip $\mathbb {N}\times \mathbb{Z}^d$ with the edge set $\{[(n,x),(n+1,y)] | n\in \mathbb {N}, x,y\in \mathbb{Z}^d\}$, and we say that each edge…

Probability · Mathematics 2012-02-08 Hubert Lacoin

In this article we prove a sprinkled decoupling inequality for the stationary Hammersley's interacting particle process. Inspired by the work of Baldasso and Texeira (2018), and Hil\'ario, Kious and Texeira (2020), we apply this inequality…

Probability · Mathematics 2025-06-25 Leandro P. R. Pimentel , Roberto Viveros

A family $\mathcal{F}$ of subsets of $\{1,2,\ldots,n\}$ is called a $t$-intersecting family if $|F\cap G| \geq t$ for any two members $F, G \in \mathcal{F}$ and for some positive integer $t$. If $t=1$, then we call the family $\mathcal{F}$…

Combinatorics · Mathematics 2022-11-23 Jagannath Bhanja , Sayan Goswami

A classical statistical inequality is used to show that the distance covariance of two bounded random vectors is bounded from above by a simple function of the dimensionality and the bounds of the random vectors. Two special cases that…

Probability · Mathematics 2023-06-30 John Çamkıran

We study, using functional renormalization (FRG), two copies of an elastic system pinned by mutually correlated random potentials. Short scale decorrelation depend on a non trivial boundary layer regime with (possibly multiple) chaos…

Disordered Systems and Neural Networks · Physics 2009-11-11 Pierre Le Doussal

Deep convolutional neural networks often perform poorly when faced with datasets that suffer from quantity imbalances and classification difficulties. Despite advances in the field, existing two-stage approaches still exhibit dataset bias…

Machine Learning · Computer Science 2023-03-16 Liang Xu , Yi Cheng , Fan Zhang , Bingxuan Wu , Pengfei Shao , Peng Liu , Shuwei Shen , Peng Yao , Ronald X. Xu

The escape probability $\xi_{x}$ from a site $x$ of a one-dimensional disordered lattice with trapping is treated as a discrete dynamical evolution by random iterations over nonlinear maps parametrized by the right and left jump…

Condensed Matter · Physics 2016-08-31 Thomas Wichmann , Achille Giacometti , K. P. N. Murthy

In $\mathbb{Z}^d$ with $d\ge 5$, we consider the time constant $\rho_u$ associated to the chemical distance in random interlacements at low intensity $u \ll 1$. We prove an upper bound of order $u^{-1/2}$ and a lower bound of order…

Probability · Mathematics 2023-01-03 Sarai Hernandez-Torres , Eviatar B. Procaccia , Ron Rosenthal

We define two families of Poissonian soups of bidirectional trajectories on $\mathbb{Z}^2$, which can be seen to adequately describe the local picture of the trace left by a random walk on the two-dimensional torus $(\mathbb{Z}/N…

Probability · Mathematics 2017-05-05 Pierre-François Rodriguez

Randomized higher-order computation can be seen as being captured by a lambda calculus endowed with a single algebraic operation, namely a construct for binary probabilistic choice. What matters about such computations is the probability of…

Logic in Computer Science · Computer Science 2020-12-24 Ugo Dal Lago , Claudia Faggian , Simona Ronchi Della Rocca

This paper is concerned with factor of i.i.d. processes on the $d$-regular tree for $d \geq 3$. We study the mutual information of the values on two given vertices. If the vertices are neighbors (i.e., their distance is $1$), then a known…

Probability · Mathematics 2017-07-25 Balázs Gerencsér , Viktor Harangi

We investigate random interlacements on $\mathbb{Z}^d$ with $d \geq 3$, and derive the large deviation rate for the probability that the capacity of the interlacement set in a macroscopic box is much smaller than that of the box. As an…

Probability · Mathematics 2022-05-30 Xinyi Li , Zijie Zhuang

Dropout has been witnessed with great success in training deep neural networks by independently zeroing out the outputs of neurons at random. It has also received a surge of interest for shallow learning, e.g., logistic regression. However,…

Machine Learning · Computer Science 2016-12-06 Zhe Li , Boqing Gong , Tianbao Yang