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The problem of identifying a planted assignment given a random $k$-SAT formula consistent with the assignment exhibits a large algorithmic gap: while the planted solution becomes unique and can be identified given a formula with $O(n\log…

Computational Complexity · Computer Science 2018-03-07 Vitaly Feldman , Will Perkins , Santosh Vempala

An analysis of the average-case complexity of solving random 3-Satisfiability (SAT) instances with backtrack algorithms is presented. We first interpret previous rigorous works in a unifying framework based on the statistical physics…

Data Structures and Algorithms · Computer Science 2008-06-20 Simona Cocco , Remi Monasson

Logic programs, more specifically, Answer-set programs, can be annotated with probabilities on facts to express uncertainty. We address the problem of propagating weight annotations on facts (eg probabilities) of an ASP to its standard…

Logic in Computer Science · Computer Science 2025-03-31 Francisco Coelho , Bruno Dinis , Dietmar Seipel , Salvador Abreu

We study the susceptibility propagation, a message-passing algorithm to compute correlation functions. It is applied to constraint satisfaction problems and its accuracy is examined. As a heuristic method to find a satisfying assignment, we…

Disordered Systems and Neural Networks · Physics 2010-07-29 Saburo Higuchi , Marc Mézard

Expectation propagation is a general prescription for approximation of integrals in statistical inference problems. Its literature is mainly concerned with Bayesian inference scenarios. However, expectation propagation can also be used to…

Methodology · Statistics 2018-05-23 P. Hall , I. M. Johnstone , J. T. Ormerod , M. P. Wand , J. C. F. Yu

In this paper we analyze the performance of Warning Propagation, a popular message passing algorithm. We show that for 3CNF formulas drawn from a certain distribution over random satisfiable 3CNF formulas, commonly referred to as the…

Probability · Mathematics 2010-12-30 Uriel Feige , Elchanan Mossel , Dan Vilenchik

Gaussian process is one of the most popular non-parametric Bayesian methodologies for modeling the regression problem. It is completely determined by its mean and covariance functions. And its linear property makes it relatively…

Machine Learning · Statistics 2020-06-16 Wenqi Fang , Huiyun Li , Hui Huang , Shaobo Dang , Zhejun Huang , Zheng Wang

Graph sampling is a technique to pick a subset of vertices and/ or edges from original graph. Among various graph sampling approaches, Traversal Based Sampling (TBS) are widely used due to low cost and feasibility for many cases, in which…

Social and Information Networks · Computer Science 2022-09-28 Xiao Qi

For many call centers, customer satisfaction (CSAT) is a key performance indicator (KPI). However, only a fraction of customers take the CSAT survey after the call, leading to a biased and inaccurate average CSAT value, and missed…

Machine Learning · Computer Science 2024-11-20 Etienne Manderscheid , Matthias Lee

It is crucial to generate crafted SAT formulas with predefined solutions for the testing and development of SAT solvers since many SAT formulas from real-world applications have solutions. Although some generating algorithms have been…

Artificial Intelligence · Computer Science 2019-02-27 Yamin Hu , Wenjian Luo , Junteng Wang

In this paper, we investigate the problem of assessing statistical methods and effectively summarizing results from simulations. Specifically, we consider problems of the type where multiple methods are compared on a reasonably large test…

Applications · Statistics 2015-10-07 Abigail Arnold , Jason Loeppky

In the typical analysis of a data set, a single method is selected for statistical reporting even when equally applicable methods yield very different results. Examples of equally applicable methods can correspond to those of different…

Methodology · Statistics 2012-10-30 David R. Bickel

We propose PROPAGATE, a fast approximation framework to estimate distance-based metrics on very large graphs such as the (effective) diameter, the (effective) radius, or the average distance within a small error. The framework assigns seeds…

Data Structures and Algorithms · Computer Science 2023-08-23 Giambattista Amati , Antonio Cruciani , Daniele Pasquini , Paola Vocca , Simone Angelini

The satisfiability problem is one of the most famous problems in computer science. Its NP-completeness has been used to argue that SAT is intractable. However, there have been tremendous advances that allow SAT solvers to solve instances…

Data Structures and Algorithms · Computer Science 2022-10-25 Jan-Hendrik Lorenz , Florian Wörz

Rademacher complexity is often used to characterize the learnability of a hypothesis class and is known to be related to the class size. We leverage this observation and introduce a new technique for estimating the size of an arbitrary…

Machine Learning · Computer Science 2018-01-30 Jonathan Kuck , Ashish Sabharwal , Stefano Ermon

We study the behavior of ASAT, a heuristic for solving satisfiability problems by stochastic local search near the SAT/UNSAT transition. The heuristic is focused, i.e. only variables in unsatisfied clauses are updated in each step, and is…

Statistical Mechanics · Physics 2013-05-29 John Ardelius , Erik Aurell

The challenge of Out-of-Distribution (OOD) generalization poses a foundational concern for the application of machine learning algorithms to risk-sensitive areas. Inspired by traditional importance weighting and propensity weighting…

Machine Learning · Computer Science 2025-02-12 Han Yu , Yue He , Renzhe Xu , Dongbai Li , Jiayin Zhang , Wenchao Zou , Peng Cui

This paper proves the long-standing open conjecture rooted in chemical physics (Flory (1949)) that the self-avoiding walk (SAW) in the square lattice has root mean square displacement exponent \nu= 3/4. This value is an instance of the…

Probability · Mathematics 2007-05-23 Irene Hueter

Parallel solving via cube-and-conquer is a key method for scaling SAT solvers to hard instances. While cube-and-conquer has proven successful for pure SAT problems, notably the Pythagorean triples conjecture, its application to SAT solvers…

Artificial Intelligence · Computer Science 2025-01-30 Markus Kirchweger , Hai Xia , Tomáš Peitl , Stefan Szeider

The appropriateness of the Poisson model is frequently challenged when examining spatial count data marked by unbalanced distributions, over-dispersion, or under-dispersion. Moreover, traditional parametric models may inadequately capture…

Methodology · Statistics 2025-03-26 Mahsa Nadifar , Andriette Bekker , Mohammad Arashi , Abel Ramoelo
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