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The $k$-SAT problem for \L{}-clausal forms has been found to be NP-complete if $k\geq 3$. Similar to Boolean CNF formulas, \L{}-clausal forms are important from a theoretical and practical points of view for their expressive power,…

Logic in Computer Science · Computer Science 2018-06-11 Mohamed El Halaby , Areeg Abdalla

We investigate parameterizing hard combinatorial problems by the size of the solution set compared to all solution candidates. Our main result is a uniform sampling algorithm for satisfying assignments of 2-CNF formulas that runs in…

Discrete Mathematics · Computer Science 2017-08-04 Jean Cardinal , Jerri Nummenpalo , Emo Welzl

We develop minimax optimal risk bounds for the general learning task consisting in predicting as well as the best function in a reference set G up to the smallest possible additive term, called the convergence rate. When the reference set…

Statistics Theory · Mathematics 2008-03-04 Jean-Yves Audibert

The determination of the fundamental parameters of the Standard Model (and its extensions) is often limited by the presence of statistical and theoretical uncertainties. We present several models for the latter uncertainties (random,…

High Energy Physics - Phenomenology · Physics 2017-04-26 Jérôme Charles , Sébastien Descotes-Genon , Valentin Niess , Luiz Vale Silva

Suppose that $P$ is a property that may be satisfied by a random code $C \subset \Sigma^n$. For example, for some $p \in (0,1)$, ${P}$ might be the property that there exist three elements of $C$ that lie in some Hamming ball of radius…

Information Theory · Computer Science 2024-07-11 Venkatesan Guruswami , Jonathan Mosheiff , Nicolas Resch , Shashwat Silas , Mary Wootters

Random constraint satisfaction problems (CSPs) have been widely studied both in AI and complexity theory. Empirically and theoretically, many random CSPs have been shown to exhibit a phase transition. As the ratio of constraints to…

Discrete Mathematics · Computer Science 2017-01-24 Colin Wei , Stefano Ermon

We provide an upper bound on the number of neurons required in a shallow neural network to approximate a continuous function on a compact set with a given accuracy. This method, inspired by a specific proof of the Stone-Weierstrass theorem,…

Machine Learning · Statistics 2025-10-09 Frantisek Hakl , Vit Fojtik

Consider averages along the prime integers $ \mathbb P $ given by \begin{equation*} \mathcal{A}_N f (x) = N ^{-1} \sum_{ p \in \mathbb P \;:\; p\leq N} (\log p) f (x-p). \end{equation*} These averages satisfy a uniform scale-free $ \ell…

Classical Analysis and ODEs · Mathematics 2020-06-23 Rui Han , Ben Krause , Michael Lacey , Fan Yang

We address a special case of a conjecture of M. Talagrand relating two notions of "threshold" for an increasing family $\mathcal F$ of subsets of a finite set $V$. The full conjecture implies equivalence of the "Fractional…

Combinatorics · Mathematics 2021-05-25 Keith Frankston , Jeff Kahn , Jinyoung Park

The runtime performance of modern SAT solvers on random $k$-CNF formulas is deeply connected with the 'phase-transition' phenomenon seen empirically in the satisfiability of random $k$-CNF formulas. Recent universal hashing-based approaches…

Discrete Mathematics · Computer Science 2017-02-28 Jeffrey M. Dudek , Kuldeep S. Meel , Moshe Y. Vardi

Estimates are constructed for the deviation of the concentration functions of sums of independent random variables with finite variances from the folded normal distribution function without any assumptions concerning the existence of the…

Probability · Mathematics 2016-08-11 V. Yu. Korolev , A. V. Dorofeeva

We introduce some new classes of words and permutations characterized by the second difference condition $\pi(i-1) + \pi(i+1) - 2\pi(i) \leq k$, which we call the $k$-convexity condition. We demonstrate that for any sized alphabet and…

Combinatorics · Mathematics 2015-07-08 Christopher Coscia , Jonathan DeWitt

Normalizing Flows (NFs) are able to model complicated distributions p(y) with strong inter-dimensional correlations and high multimodality by transforming a simple base density p(z) through an invertible neural network under the change of…

Machine Learning · Computer Science 2023-11-14 Christina Winkler , Daniel Worrall , Emiel Hoogeboom , Max Welling

We present a new method for counterfactual explanations (CFEs) based on Bayesian optimisation that applies to both classification and regression models. Our method is a globally convergent search algorithm with support for arbitrary…

Machine Learning · Computer Science 2021-06-30 Thomas Spooner , Danial Dervovic , Jason Long , Jon Shepard , Jiahao Chen , Daniele Magazzeni

We introduce the problem of finding a satisfying assignment to a CNF formula that must further belong to a prescribed input subspace. Equivalent formulations of the problem include finding a point outside a union of subspaces (the…

Data Structures and Algorithms · Computer Science 2021-08-16 Vikraman Arvind , Venkatesan Guruswami

Bottom-up knowledge compilation is a paradigm for generating representations of functions by iteratively conjoining constraints using a so-called apply function. When the input is not efficiently compilable into a language - generally a…

Computational Complexity · Computer Science 2021-12-24 Alexis de Colnet , Stefan Mengel

For any real $k\geq 2$ and large prime $q$, we prove a lower bound on the $2k$-th moment of the Dirichlet character sum \begin{equation*} \frac{1}{\phi(q)} \sum_{\substack{\chi \text{ mod }q\\ \chi\neq \chi_0}} \Big| \sum_{n\leq x}…

Number Theory · Mathematics 2024-09-23 Barnabás Szabó

We show unconditional parameterized lower bounds in the area of knowledge compilation, more specifically on the size of circuits in decomposable negation normal form (DNNF) that encode CNF-formulas restricted by several graph width…

Artificial Intelligence · Computer Science 2016-04-25 Stefan Mengel

We prove two lower bounds for stopping times of sequential tests between general composite nulls and alternatives. The first lower bound is for the setting where the type-1 error level $\alpha$ approaches zero, and equals $\log(1/\alpha)$…

Statistics Theory · Mathematics 2025-04-29 Shubhada Agrawal , Aaditya Ramdas

We define a general method for finding a quasi-best approximant in sup-norm to a target density belonging to a given model, based on independent samples drawn from distributions which average to the target (which does not necessarily belong…

Statistics Theory · Mathematics 2025-06-26 Guillaume Maillard
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