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Recent efforts in coding theory have focused on building codes for insertions and deletions, called insdel codes, with optimal trade-offs between their redundancy and their error-correction capabilities, as well as efficient encoding and…

Information Theory · Computer Science 2020-12-08 Alexander R. Block , Jeremiah Blocki , Elena Grigorescu , Shubhang Kulkarni , Minshen Zhu

Locally checkable labeling problems (LCLs) are distributed graph problems in which a solution is globally feasible if it is locally feasible in all constant-radius neighborhoods. Vertex colorings, maximal independent sets, and maximal…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-02-19 Alkida Balliu , Sebastian Brandt , Dennis Olivetti , Jukka Suomela

As a typical application, the Lenstra-Lenstra-Lovasz lattice basis reduction algorithm (LLL) is used to compute a reduced basis of the orthogonal lattice for a given integer matrix, via reducing a special kind of lattice bases. With such…

Symbolic Computation · Computer Science 2018-05-10 Jingwei Chen , Damien Stehlé , Gilles Villard

A general mathematical method is presented for the systematic construction of coupled map lattices (CMLs) out of deterministic cellular automata (CAs). The entire CA rule space is addressed by means of a universal map for CAs that we have…

Cellular Automata and Lattice Gases · Physics 2016-06-09 Vladimir García-Morales

Locally checkable labeling problems (LCLs) form the foundation of the modern theory of distributed graph algorithms. First introduced in the seminal paper by Naor and Stockmeyer [STOC 1993], these are graph problems that can be described by…

Distributed, Parallel, and Cluster Computing · Computer Science 2026-02-23 Antonio Cruciani , Avinandan Das , Alesya Raevskaya , Jukka Suomela

The LOCAL model is among the main models for studying locality in the framework of distributed network computing. This model is however subject to pertinent criticisms, including the facts that all nodes wake up simultaneously, perform in…

Distributed, Parallel, and Cluster Computing · Computer Science 2019-12-09 Carole Delporte-Gallet , Hugues Fauconnier , Pierre Fraigniaud , Mikaël Rabie

Partial penalized tests provide flexible approaches to testing linear hypotheses in high dimensional generalized linear models. However, because the estimators used in these tests are local minimizers of potentially non-convex…

Statistics Theory · Mathematics 2024-08-02 Tate Jacobson

We prove a Borel version of the local lemma, i.e. we show that, under suitable assumptions, if the set of variables in the local lemma has a structure of a Borel space, then there exists a satisfying assignment which is a Borel function.…

Combinatorics · Mathematics 2024-03-05 Endre Csóka , Łukasz Grabowski , András Máthé , Oleg Pikhurko , Konstantinos Tyros

In this work, we construct the first locally-correctable codes (LCCs), and locally-testable codes (LTCs) with constant rate, constant relative distance, and sub-polynomial query complexity. Specifically, we show that there exist binary LCCs…

Computational Complexity · Computer Science 2015-04-23 Swastik Kopparty , Or Meir , Noga Ron-Zewi , Shubhangi Saraf

The Metropolis-Adjusted Langevin Algorithm (MALA) is a widely used Markov Chain Monte Carlo (MCMC) method for sampling from high-dimensional distributions. However, MALA relies on differentiability assumptions that restrict its…

Methodology · Statistics 2025-07-10 Ning Ning

We study the local convergence rate of stochastic first-order methods under a local $\alpha$-Polyak-Lojasiewicz ($\alpha$-PL) condition in a neighborhood of a target connected component $\mathcal{M}$ of the local minimizer set. The…

Optimization and Control · Mathematics 2026-02-24 Saeed Masiha , Saber Salehkaleybar , Niao He , Negar Kiyavash , Patrick Thiran

We revisit Deep Linear Discriminant Analysis (Deep LDA) from a likelihood-based perspective. While classical LDA is a simple Gaussian model with linear decision boundaries, attaching an LDA head to a neural encoder raises the question of…

Machine Learning · Statistics 2026-02-23 Maxat Tezekbayev , Arman Bolatov , Zhenisbek Assylbekov

Probabilistic Component Latent Analysis (PLCA) is a statistical modeling method for feature extraction from non-negative data. It has been fruitfully applied to various research fields of information retrieval. However, the EM-solved…

Methodology · Statistics 2017-03-16 D. Cazau , G. Nuel

Sampling a random permutation with restricted positions, or equivalently approximating the permanent of a 0-1 matrix, is a fundamental problem in computer science, with several notable results achieved over the years. However, existing…

Data Structures and Algorithms · Computer Science 2025-08-15 Kun He , Guoliang Qiu , Xiaoming Sun

We present a general framework for derandomizing random linear codes with respect to a broad class of properties, known as local properties, which encompass several standard notions such as distance, list-decoding, list-recovery, and…

Information Theory · Computer Science 2025-11-21 Fernando Granha Jeronimo , Nikhil Shagrithaya

Locally decodable codes (LDC's) are error-correcting codes that allow recovery of individual message indices by accessing only a constant number of codeword indices. For substitution errors, it is evident that LDC's exist -- Hadamard codes…

Information Theory · Computer Science 2023-11-15 Meghal Gupta

An old result by Shearer relates the Lov\'asz Local Lemma with the independent set polynomial on graphs, and consequently, as observed by Scott and Sokal, with the partition function of the hard core lattice gas on graphs. We use this…

Combinatorics · Mathematics 2010-03-29 Rodrigo Bissacot , Roberto Fernández , Aldo Procacci , Benedetto Scoppola

Quantum cellular automata (QCAs) are automorphisms of tensor product algebras that preserve locality, with local quantum circuits as a simple example. We study approximate QCAs, where the locality condition is only satisfied up to a small…

Quantum Physics · Physics 2026-03-10 Daniel Ranard , Michael Walter , Freek Witteveen

Linear temporal logic (LTL) is a specification language for finite sequences (called traces) widely used in program verification, motion planning in robotics, process mining, and many other areas. We consider the problem of learning LTL…

Artificial Intelligence · Computer Science 2026-01-22 Ritam Raha , Rajarshi Roy , Nathanaël Fijalkow , Daniel Neider

Many randomized algorithms can be derandomized efficiently using either the method of conditional expectations or probability spaces with low (almost-) independence. A series of papers, beginning with Luby (1993) and continuing with Berger…

Data Structures and Algorithms · Computer Science 2023-10-13 David G. Harris
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