Tootfinder

Review of the Higgs boson production via vector boson fusion and its decay into bottom quarks in $pp$ collisions at $\sqrt{s}=8,13$ TeV
Fikriye C I Kaya, Ibrahim Mirza
https://arxiv.org/abs/2507.22765 …

Review of the Higgs boson production via vector boson fusion and its decay into bottom quarks in $pp$ collisions at $\sqrt{s}=8,13$ TeV
We present a comprehensive overview of the vector boson fusion production and the following decay of the Higgs boson to two bottom quarks (\(H\!\to\! b\bar{b}\)), which dominates the Higgs total width yet remains experimentally challenging because of substantial QCD multijet backgrounds. We overview the use of boosted decision trees, which advance jet reconstruction and flavour tagging by including boosted topologies and deep-learning-based \(b\)-tagging and which improve sensitivity in associa…

TRUST: Transparent, Robust and Ultra-Sparse Trees
Albert Dorador
https://arxiv.org/abs/2506.15791 https://arxiv.org/pdf/2506.15791

TRUST: Transparent, Robust and Ultra-Sparse Trees
Piecewise-constant regression trees remain popular for their interpretability, yet often lag behind black-box models like Random Forest in predictive accuracy. In this work, we introduce TRUST (Transparent, Robust, and Ultra-Sparse Trees), a novel regression tree model that combines the accuracy of Random Forests with the interpretability of shallow decision trees and sparse linear models. TRUST further enhances transparency by leveraging Large Language Models to generate tailored, user-friendl…

BarkBeetle: Stealing Decision Tree Models with Fault Injection
Qifan Wang, Jonas Sander, Minmin Jiang, Thomas Eisenbarth, David Oswald
https://arxiv.org/abs/2507.06986

BarkBeetle: Stealing Decision Tree Models with Fault Injection
Machine learning models, particularly decision trees (DTs), are widely adopted across various domains due to their interpretability and efficiency. However, as ML models become increasingly integrated into privacy-sensitive applications, concerns about their confidentiality have grown, particularly in light of emerging threats such as model extraction and fault injection attacks. Assessing the vulnerability of DTs under such attacks is therefore important. In this work, we present BarkBeetle, a…

A Bayesian Additive Regression Trees Model for zero and one inflated data for Predicting Individual Treatment Effects in Alcohol Use Disorder Trials
Pamela Solano, M Lee Van Horn, Kyle Walters, Philipp Besendorfer, Alena Kuhlemeier, Manel Mart\'inez-Ram\'on, Thomas Jaki
https://arxiv.org/abs/2507.19848

A Bayesian Additive Regression Trees Model for zero and one inflated data for Predicting Individual Treatment Effects in Alcohol Use Disorder Trials
Alcohol Use Disorder (AUD) treatment presents high individual-level heterogeneity, with outcomes ranging from complete abstinence to persistent heavy drinking. This variability-driven by complex behavioral, social, and environmental factors-poses major challenges for treatment evaluation and individualized decision-making. In particular, accurately modeling bounded semicontinuous outcomes and estimating predictive individual treatment effects (PITEs) remains methodologically demanding. For th…

Oldies but Goldies: The Potential of Character N-grams for Romanian Texts
Dana Lupsa, Sanda-Maria Avram
https://arxiv.org/abs/2506.15650 https://

Oldies but Goldies: The Potential of Character N-grams for Romanian Texts
This study addresses the problem of authorship attribution for Romanian texts using the ROST corpus, a standard benchmark in the field. We systematically evaluate six machine learning techniques: Support Vector Machine (SVM), Logistic Regression (LR), k-Nearest Neighbors (k-NN), Decision Trees (DT), Random Forests (RF), and Artificial Neural Networks (ANN), employing character n-gram features for classification. Among these, the ANN model achieved the highest performance, including perfect clas…

Interpretable reinforcement learning for heat pump control through asymmetric differentiable decision trees
Toon Van Puyvelde, Mehran Zareh, Chris Develder
https://arxiv.org/abs/2506.01641

Interpretable reinforcement learning for heat pump control through asymmetric differentiable decision trees
In recent years, deep reinforcement learning (DRL) algorithms have gained traction in home energy management systems. However, their adoption by energy management companies remains limited due to the black-box nature of DRL, which fails to provide transparent decision-making feedback. To address this, explainable reinforcement learning (XRL) techniques have emerged, aiming to make DRL decisions more transparent. Among these, soft differential decision tree (DDT) distillation provides a promisin…

This https://arxiv.org/abs/2412.06552 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCC_…

Direct Sums for Parity Decision Trees
Direct sum theorems state that the cost of solving $k$ instances of a problem is at least $Ω(k)$ times the cost of solving a single instance. We prove the first such results in the randomised parity decision tree model. We show that a direct sum theorem holds whenever (1) the lower bound for parity decision trees is proved using the discrepancy method; or (2) the lower bound is proved relative to a product distribution.

Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[1/3]:
Improved Quantum Query Upper Bounds Based on Classical Decision Trees

B0 -> K*0 tau tau- Decay: Using Machine Learning to Separate Signal from Background
Ziyao Xiong, Qixing Deng, Yidan Sun, Junhua Yang
https://arxiv.org/abs/2506.19501

B0 -> K*0 tau+ tau- Decay: Using Machine Learning to Separate Signal from Background
This study investigates the rare decay B0 -> K*0 tau+ tau-, which is sensitive to potential violations of lepton flavor universality predicted by the Standard Model. A Monte Carlo simulated dataset containing both signal and the dominant background process B0 -> K*0 D+ D- was used to train and evaluate machine learning classifiers. After feature selection and parameter tuning, two supervised models -- Boosted Decision Trees (BDTs) and Fully Connected Neural Networks (FCNNs) -- were trained. Fea…

This https://arxiv.org/abs/2503.12181 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Value Gradients with Action Adaptive Search Trees in Continuous (PO)MDPs
Solving Partially Observable Markov Decision Processes (POMDPs) in continuous state, action and observation spaces is key for autonomous planning in many real-world mobility and robotics applications. Current approaches are mostly sample based, and cannot hope to reach near-optimal solutions in reasonable time. We propose two complementary theoretical contributions. First, we formulate a novel Multiple Importance Sampling (MIS) tree for value estimation, that allows to share value information b…

A comparative analysis of machine learning algorithms for predicting probabilities of default
Adrian Iulian Cristescu, Matteo Giordano
https://arxiv.org/abs/2506.19789

A comparative analysis of machine learning algorithms for predicting probabilities of default
Predicting the probability of default (PD) of prospective loans is a critical objective for financial institutions. In recent years, machine learning (ML) algorithms have achieved remarkable success across a wide variety of prediction tasks; yet, they remain relatively underutilised in credit risk analysis. This paper highlights the opportunities that ML algorithms offer to this field by comparing the performance of five predictive models-Random Forests, Decision Trees, XGBoost, Gradient Boosti…