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Neural Networks and Decision Trees: two popular techniques for supervised learning that are seemingly disconnected in their formulation and optimization method, have recently been combined in a single construct. The connection pivots on…

Machine Learning · Statistics 2020-02-27 Giuseppe Nuti , Lluís Antoni Jiménez Rugama , Kaspar Thommen

We develop a finite-sample, design-based theory for random forests in which each tree is a randomized conditional predictor acting on fixed covariates and the forest is their Monte Carlo average. An exact variance identity separates Monte…

Machine Learning · Statistics 2026-03-03 Nathaniel S. O'Connell

We consider the problem of estimating the factors of a rank-$1$ matrix with i.i.d. Gaussian, rank-$1$ measurements that are nonlinearly transformed and corrupted by noise. Considering two prototypical choices for the nonlinearity, we study…

Optimization and Control · Mathematics 2024-10-02 Kabir Aladin Chandrasekher , Mengqi Lou , Ashwin Pananjady

We introduce a family of stochastic processes on the integers, depending on a parameter $p \in [0,1]$ and interpolating between the deterministic rotor walk (p=0) and the simple random walk (p=1/2). This p-rotor walk is not a Markov chain…

Probability · Mathematics 2016-04-08 Wilfried Huss , Lionel Levine , Ecaterina Sava-Huss

Few problems in statistics are as perplexing as variable selection in the presence of very many redundant covariates. The variable selection problem is most familiar in parametric environments such as the linear model or additive variants…

Methodology · Statistics 2021-02-25 Yi Liu , Veronika Ročková , Yuexi Wang

In many real-world planning tasks, agents must tackle uncertainty about the environment's state and variability in the outcomes induced by stochastic dynamics and rewards. Motivated by recent progress in world model approaches, where latent…

Artificial Intelligence · Computer Science 2026-05-07 Larry Preuett , Qiuyi Zhang , Muhammad Aurangzeb Ahmad

Bayesian inference is now a leading technique for reconstructing phylogenetic trees from aligned sequence data. In this short note, we formally show that the maximum posterior tree topology provides a statistically consistent estimate of a…

Populations and Evolution · Quantitative Biology 2013-07-12 Mike Steel

Latent tree analysis seeks to model the correlations among a set of random variables using a tree of latent variables. It was proposed as an improvement to latent class analysis --- a method widely used in social sciences and medicine to…

Machine Learning · Computer Science 2016-10-04 Nevin L. Zhang , Leonard K. M. Poon

We introduce a new generalization of the Pseudo-Lindley distribution by applying alpha power transformation. The obtained distribution is referred as the Pseudo-Lindley alpha power transformed distribution (\textit{PL-APT}). Some tractable…

Statistics Theory · Mathematics 2022-01-20 Modou Ngom , Moumouni Diallo , Adja Mbarka Fall , Gane Samb Lo

Hypothesis testing results often rely on simple, yet important assumptions about the behaviour of the distribution of p-values under the null and the alternative. We examine tests for one dimensional parameters of interest that converge to…

Statistics Theory · Mathematics 2021-08-06 Yanbo Tang , Radu Craiu , Lei Sun

This article describes a purely analytic approach to urn models of the generalized or extended P\'olya-Eggenberger type, in the case of two types of balls and constant ``balance,'' that is, constant row sum. The treatment starts from a…

Probability · Mathematics 2007-05-23 Philippe Flajolet , Joaquim Gabarro , Helmut Pekari

The properties of current-carrying steady states of strongly correlated systems away from the linear-response regime are of topical interest. In this article, we review the renormalized perturbation theory, or renormalized SPT of reference…

Strongly Correlated Electrons · Physics 2017-04-26 Enrique Muñoz , Farzaneh Zamani , Lukas Merker , Theo Costi , Stefan Kirchner

We propose a novel Dirichlet-based P\'olya tree (D-P tree) prior on the copula and based on the D-P tree prior, a nonparametric Bayesian inference procedure. Through theoretical analysis and simulations, we are able to show that the…

Methodology · Statistics 2017-02-24 Shaoyang Ning , Neil Shephard

The classification of imbalanced data has presented a significant challenge for most well-known classification algorithms that were often designed for data with relatively balanced class distributions. Nevertheless skewed class distribution…

Machine Learning · Statistics 2023-04-21 Jiaju Miao , Wei Zhu

We study the size of the automorphism group of two different types of random trees: Galton--Watson trees and rooted P\'olya trees. In both cases, we prove that it asymptotically follows a log-normal distribution and provide asymptotic…

Probability · Mathematics 2023-03-23 Christoffer Olsson , Stephan Wagner

The goal of these lectures is to review some mathematical aspects of random tree models used in evolutionary biology to model gene trees or species trees. We start with stochastic models of tree shapes (finite trees without edge lengths),…

Probability · Mathematics 2017-08-30 Amaury Lambert

Lateral gene transfer (LGT) is a common mechanism of non-vertical evolution where genetic material is transferred between two more or less distantly related organisms. It is particularly common in bacteria where it contributes to adaptive…

Probability · Mathematics 2012-06-18 Sebastien Roch , Sagi Snir

We extend several recent results providing symmetry-based guarantees for variational inference (VI) with location-scale families. VI approximates a target density $p$ by the best match $q^*$ in a family $Q$ of tractable distributions that…

Machine Learning · Statistics 2025-12-11 Charles C. Margossian , Lawrence K. Saul

The prevailing mindset is that a single decision tree underperforms classic random forests in testing accuracy, despite its advantages in interpretability and lightweight structure. This study challenges such a mindset by significantly…

Machine Learning · Computer Science 2024-11-27 Qiangqiang Mao , Yankai Cao

In genetic association studies, detecting disease-genotype associations is a primary goal. For most diseases, the underlying genetic model is unknown, and we study seven robust test statistics for monotone association. For a given test…

Methodology · Statistics 2020-04-13 Mette Langaas , Øyvind Bakke