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Under the assumption that the distribution of a nonnegative random variable $X$ admits a bounded coupling with its size biased version, we prove simple and strong concentration bounds. In particular the upper tail probability is shown to…

Probability · Mathematics 2014-07-15 Richard Arratia , Peter Baxendale

Given a finite planar graph, a grove is a spanning forest in which every component tree contains one or more of a specified set of vertices (called nodes) on the outer face. For the uniform measure on groves, we compute the probabilities of…

Probability · Mathematics 2011-06-30 Richard W. Kenyon , David B. Wilson

In this experimental study we consider Steiner tree approximations that guarantee a constant approximation of ratio smaller than $2$. The considered greedy algorithms and approaches based on linear programming involve the incorporation of…

Data Structures and Algorithms · Computer Science 2015-12-10 Stephan Beyer , Markus Chimani

In the book Boolean Function Complexity by Stasys Jukna, two lower bound techniques for Tree-like Cutting Plane proofs (henceforth, "Tree-CP proofs") using Karchmer-Widgerson type communication games (henceforth, "KW games") are presented:…

Computational Complexity · Computer Science 2013-01-08 Daniel Apon

Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The success of MCTS depends heavily on how the MCTS statistical tree is built and the selection policy plays a fundamental role in this. A…

Artificial Intelligence · Computer Science 2023-02-08 Fred Valdez Ameneyro , Edgar Galvan

We study the $\textit{average-case deterministic query complexity}$ of boolean functions under a $\textit{uniform input distribution}$, denoted by $\mathrm{D}_\mathrm{ave}(f)$, the minimum average depth of zero-error decision trees that…

Computational Complexity · Computer Science 2025-06-12 Yuan Li , Haowei Wu , Yi Yang

We develop the complex-analytic viewpoint on the tree convolutions studied by the second author and Weihua Liu in "An operad of non-commutative independences defined by trees" (Dissertationes Mathematicae, 2020, doi:10.4064/dm797-6-2020),…

Operator Algebras · Mathematics 2021-04-13 Ethan Davis , David Jekel , Zhichao Wang

We study the fundamental limits to the expressive power of neural networks. Given two sets $F$, $G$ of real-valued functions, we first prove a general lower bound on how well functions in $F$ can be approximated in $L^p(\mu)$ norm by…

Machine Learning · Computer Science 2022-12-21 El Mehdi Achour , Armand Foucault , Sébastien Gerchinovitz , François Malgouyres

We show optimal lower bounds for spanning forest computation in two different models: * One wants a data structure for fully dynamic spanning forest in which updates can insert or delete edges amongst a base set of $n$ vertices. The sole…

Data Structures and Algorithms · Computer Science 2019-11-27 Jelani Nelson , Huacheng Yu

Over some types of trees with a given number of vertices, which trees minimize or maximize the total number of subtrees or leaf containing subtrees are studied. Here are some of the main results:\ (1)\, Sharp upper bound on the total number…

Combinatorics · Mathematics 2012-06-15 Shuchao Li , Shujing Wang

Place an active particle at the root of the infinite $d$-ary tree and dormant particles at each non-root site. Active particles move towards the root with probability $p$ and otherwise move to a uniformly sampled child vertex. When an…

Probability · Mathematics 2023-09-28 Poly Mathews

We consider the biased random walk on a tree constructed from the set of finite self-avoiding walks on a lattice, and use it to construct probability measures on infinite self-avoiding walks. The limit measure (if it exists) obtained when…

Probability · Mathematics 2019-12-25 Vincent Beffara , Cong Bang Huynh

Selective inference is considered for testing trees and edges in phylogenetic tree selection from molecular sequences. This improves the previously proposed approximately unbiased test by adjusting the selection bias when testing many trees…

Applications · Statistics 2019-05-27 Hidetoshi Shimodaira , Yoshikazu Terada

The threshold-$k$ metric dimension ($\mathrm{Tmd}_k$) of a graph is the minimum number of sensors -- a subset of the vertex set -- needed to uniquely identify any vertex in the graph, solely based on its distances from the sensors, when the…

Combinatorics · Mathematics 2021-11-18 Zsolt Bartha , Júlia Komjáthy , Järvi Raes

In recent years, significant progress has been made on algorithms for learning optimal decision trees, primarily in the context of binary features. Extending these methods to continuous features remains substantially more challenging due to…

Machine Learning · Computer Science 2026-01-22 Harold Kiossou , Pierre Schaus , Siegfried Nijssen

We consider lower bounds on the number of spanning trees of connected graphs with degree bounded by $d$. The question is of interest because such bounds may improve the analysis of the improvement produced by memorisation in the runtime of…

Discrete Mathematics · Computer Science 2009-02-13 John Michael Robson

Decision trees (DTs) epitomize what have become to be known as interpretable machine learning (ML) models. This is informally motivated by paths in DTs being often much smaller than the total number of features. This paper shows that in…

Machine Learning · Computer Science 2020-10-22 Yacine Izza , Alexey Ignatiev , Joao Marques-Silva

As well known the rotation distance D(S,T) between two binary trees S, T of n vertices is the minimum number of rotations of pairs of vertices to transform S into T. We introduce the new operation of chain rotation on a tree, involving two…

Data Structures and Algorithms · Computer Science 2012-04-27 Fabrizio Luccio , Linda Pagli

We prove a metric space scaling limit for a critical random graph with independent and identically distributed degrees having power-law tail behaviour with exponent $\alpha+1$, where $\alpha \in (1,2)$. The limiting components are…

Probability · Mathematics 2021-08-02 Guillaume Conchon--Kerjan , Christina Goldschmidt

Machine learning is increasingly used to guide branch-and-cut (B&C) for mixed-integer linear programming by learning score-based policies for selecting branching variables and cutting planes. Many approaches train on local signals from…

Optimization and Control · Mathematics 2026-02-02 Hongyu Cheng , Amitabh Basu