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Related papers: Locality Bounds for Sampling Hamming Slices

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Geographical data are generally autocorrelated. In this case, it is preferable to select spread units. In this paper, we propose a new method for selecting well-spread samples from a finite spatial population with equal or unequal inclusion…

Methodology · Statistics 2020-08-11 Raphaël Jauslin , Yves Tillé

Standard Markov chain Monte Carlo methods struggle to explore distributions that are concentrated in the neighbourhood of low-dimensional structures. These pathologies naturally occur in a number of situations. For example, they are common…

Computation · Statistics 2021-12-02 Khai Xiang Au , Matthew M. Graham , Alexandre H. Thiery

This paper presents a local energy distribution based hyperparameter determination for stochastic simulated annealing (SSA). SSA is capable of solving combinatorial optimization problems faster than typical simulated annealing (SA), but…

Machine Learning · Computer Science 2023-11-02 Naoya Onizawa , Kyo Kuroki , Duckgyu Shin , Takahiro Hanyu

Binary Neural Networks (BiNNs), which employ single-bit precision weights, have emerged as a promising solution to reduce memory usage and power consumption while maintaining competitive performance in large-scale systems. However, training…

Quantum Physics · Physics 2025-11-18 Luca Nepote , Alix Lhéritier , Nicolas Bondoux , Marios Kountouris , Maurizio Filippone

We address the problem of producing a lower bound for the mean of a discrete probability distribution, with known support over a finite set of real numbers, from an iid sample of that distribution. Up to a constant, this is equivalent to…

Statistics Theory · Mathematics 2025-02-25 Erik Learned-Miller

In a decision-theoretic framework, the minimax lower bound provides the worst-case performance of estimators relative to a given class of statistical models. For parametric and semiparametric models, the H\'{a}jek--Le Cam local asymptotic…

Statistics Theory · Mathematics 2024-10-22 Kenta Takatsu , Arun Kumar Kuchibhotla

This paper develops upper and lower bounds for the probability of Boolean expressions by treating multiple occurrences of variables as independent and assigning them new individual probabilities. Our technique generalizes and extends the…

Artificial Intelligence · Computer Science 2015-03-19 Wolfgang Gatterbauer , Dan Suciu

We introduce the concept of functions of locally bounded variation on abstract Wiener spaces and study their properties. Some nontrivial examples and applications to stochastic analysis are also discussed.

Probability · Mathematics 2019-09-16 Masanori Hino

Certain disorder-free Hamiltonians can be non-ergodic due to a \emph{strong fragmentation} of the Hilbert space into disconnected sectors. Here, we characterize such systems by introducing the notion of `statistically localized integrals of…

Strongly Correlated Electrons · Physics 2020-05-13 Tibor Rakovszky , Pablo Sala , Ruben Verresen , Michael Knap , Frank Pollmann

We completely characterize $\Delta$- and local subexponentialities of positive-half compound Poisson distributions and extend the characterization on two-sided distributions. Moreover, $\Delta$-subexponentiality of infinitely divisible…

Probability · Mathematics 2023-02-21 Muneya Matsui , Toshiro Watanabe

Learning the joint dependence of discrete variables is a fundamental problem in machine learning, with many applications including prediction, clustering and dimensionality reduction. More recently, the framework of copula modeling has…

Machine Learning · Statistics 2013-11-15 Alfredo Kalaitzis , Ricardo Silva

We propose a new method for identifying cutting locations for quantum circuit cutting, with a primary focus on partitioning circuits into three or more parts. Under the assumption that the classical postprocessing function is decomposable,…

Quantum Physics · Physics 2025-07-02 Junya Nakamura , Takahiko Satoh , Shinichiro Sanji

We demonstrate for the first time that ill-conditioned, non-smooth, constrained distributions in very high dimension, upwards of 100,000, can be sampled efficiently $\textit{in practice}$. Our algorithm incorporates constraints into the…

Machine Learning · Computer Science 2022-10-18 Yunbum Kook , Yin Tat Lee , Ruoqi Shen , Santosh S. Vempala

In this paper, we propose a new interpretation of local limit theorems for univariate and multivariate distributions on lattices. We show that - given a local limit theorem in the standard sense - the distributions are approximated well by…

Probability · Mathematics 2022-08-09 Michael Fleermann , Werner Kirsch , Gabor Toth

As the most fundamental problem in statistics, robust location estimation has many prominent solutions, such as the trimmed mean, Winsorized mean, Hodges Lehmann estimator, Huber M estimator, and median of means. Recent studies suggest that…

Statistics Theory · Mathematics 2024-09-12 Li Tuobang

Classifiers label data as belonging to one of a set of groups based on input features. It is challenging to obtain accurate classification performance when the feature distributions in the different classes are complex, with nonlinear,…

Machine Learning · Statistics 2021-10-06 Didong Li , David B Dunson

Two commonly arising computational tasks in Bayesian learning are Optimization (Maximum A Posteriori estimation) and Sampling (from the posterior distribution). In the convex case these two problems are efficiently reducible to each other.…

Machine Learning · Computer Science 2019-11-07 Kunal Talwar

Function values are, in some sense, "almost as good" as general linear information for $L_2$-approximation (optimal recovery, data assimilation) of functions from a reproducing kernel Hilbert space. This was recently proved by new upper…

Numerical Analysis · Mathematics 2022-03-23 Aicke Hinrichs , David Krieg , Erich Novak , Jan Vybiral

Given a set of strings over a specified alphabet, identifying a median or consensus string that minimizes the total distance to all input strings is a fundamental data aggregation problem. When the Hamming distance is considered as the…

Data Structures and Algorithms · Computer Science 2026-02-11 Diptarka Chakraborty , Rudrayan Kundu , Nidhi Purohit , Aravinda Kanchana Ruwanpathirana

It has been shown that uniform as well as non-uniform cellular automata (CA) can be evolved to perform certain computational tasks. Random Boolean networks are a generalization of two-state cellular automata, where the interconnection…

Disordered Systems and Neural Networks · Physics 2007-05-23 Bertrand Mesot , Christof Teuscher