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Algorithms for binary classification based on adaptive tree partitioning are formulated and analyzed for both their risk performance and their friendliness to numerical implementation. The algorithms can be viewed as generating a set…

Statistics Theory · Mathematics 2014-11-05 Peter Binev , Albert Cohen , Wolfgang Dahmen , Ronald DeVore

We introduce random spatial forests, a method of bagging regression trees allowing for spatial correlation. Our main contribution is the development of a computationally efficient tree building algorithm which selects each split of the tree…

Methodology · Statistics 2020-07-24 Travis Hee Wai , Michael T. Young , Adam A. Szpiro

For the directed landscape, the putative universal space-time scaling limit object in the (1+1) dimensional Kardar-Parisi-Zhang (KPZ) universality class, consider the geodesic tree -- the tree formed by the coalescing semi-infinite…

Probability · Mathematics 2025-04-18 Riddhipratim Basu , Manan Bhatia

In binary and ordinal regression one can distinguish between a location component and a scaling component. While the former determines the location within the range of the response categories, the scaling indicates variance heterogeneity.…

Methodology · Statistics 2019-10-31 Gerhard Tutz , Moritz Berger

We study the number of random records in an arbitrary split tree (or equivalently, the number of random cuttings required to eliminate the tree). We show that a classical limit theorem for convergence of sums of triangular arrays to…

Probability · Mathematics 2010-05-26 Cecilia Holmgren

The conformations of topologically constrained double-folded ring polymers can be described as wrappings of randomly branched primitive trees. We extend previous work on the tree statistics under different (solvent) conditions to explore…

Soft Condensed Matter · Physics 2019-01-23 Angelo Rosa , Ralf Everaers

We consider a family of random trees satisfying a Markov branching property. Roughly, this property says that the subtrees above some given height are independent with a law that depends only on their total size, the latter being either the…

Probability · Mathematics 2012-11-06 Bénédicte Haas , Grégory Miermont

This paper introduces the Partition Tree Weighting technique, an efficient meta-algorithm for piecewise stationary sources. The technique works by performing Bayesian model averaging over a large class of possible partitions of the data…

Information Theory · Computer Science 2012-11-22 Joel Veness , Martha White , Michael Bowling , András György

In many domains, data measurements can naturally be associated with the leaves of a tree, expressing the relationships among these measurements. For example, companies belong to industries, which in turn belong to ever coarser divisions…

Methodology · Statistics 2021-08-12 Simeng Shao , Jacob Bien , Adel Javanmard

In this article we investigate the Uniform Spanning Forest ($\mathsf{USF}$) in the nearest-neighbour integer lattice $\mathbf{Z}^{d+1} = \mathbf{Z}\times \mathbf{Z}^d$ with an assignment of conductances that makes the underlying (Network)…

Probability · Mathematics 2020-09-03 Guillermo Martinez Dibene

Network design problems aim to compute low-cost structures such as routes, trees and subgraphs. Often, it is natural and desirable to require that these structures have small hop length or hop diameter. Unfortunately, optimization problems…

Data Structures and Algorithms · Computer Science 2020-11-13 Bernhard Haeupler , D Ellis Hershkowitz , Goran Zuzic

The simple random walk on $\mathbb{Z}^p$ shows two drastically different behaviours depending on the value of $p$: it is recurrent when $p\in\{1,2\}$ while it escapes (with a rate increasing with $p$) as soon as $p\geq3$. This classical…

Data Structures and Algorithms · Computer Science 2023-08-22 Farah Ben Naoum , Christophe Godin , Romain Azaïs

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

We present a new way of constructing an ensemble classifier, named the Guided Random Forest (GRAF) in the sequel. GRAF extends the idea of building oblique decision trees with localized partitioning to obtain a global partitioning. We show…

Machine Learning · Computer Science 2025-08-08 Prashant Gupta , Aashi Jindal , Jayadeva , Debarka Sengupta

Many discrete optimization problems amount to selecting a feasible set of edges of least weight. We consider in this paper the context of spatial graphs where the positions of the vertices are uncertain and belong to known uncertainty sets.…

Data Structures and Algorithms · Computer Science 2022-09-27 Marin Bougeret , Jérémy Omer , Michael Poss

Many machine learning tools for regression are based on recursive partitioning of the covariate space into smaller regions, where the regression function can be estimated locally. Among these, regression trees and their ensembles have…

Statistics Theory · Mathematics 2017-11-30 Stephanie van der Pas , Veronika Rockova

To model discriminative, i.e. competition induced, self-thinning in even-aged forest stands a concept has been explored that discriminative mortality alters spatial arrangement of trees which in turn alters the mortality. Function of…

Populations and Evolution · Quantitative Biology 2016-09-30 Vladimir L. Gavrikov , Rem G. Khlebopros

We investigate the random continuous trees called L\'evy trees, which are obtained as scaling limits of discrete Galton-Watson trees. We give a mathematically precise definition of these random trees as random variables taking values in the…

Probability · Mathematics 2007-05-23 Thomas Duquesne , Jean-Francois Le Gall

We consider a model of random tree growth, where at each time unit a new vertex is added and attached to an already existing vertex chosen at random. The probability with which a vertex with degree $k$ is chosen is proportional to $w(k)$,…

Probability · Mathematics 2007-05-23 Anna Rudas , Balint Toth , Benedek Valko

In this paper we relate the partition function to the max-statistics of random variables. In particular, we provide a novel framework for approximating and bounding the partition function using MAP inference on randomly perturbed models. As…

Machine Learning · Computer Science 2012-07-03 Tamir Hazan , Tommi Jaakkola