Tootfinder

CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis
Jianfei Li, Kevin Kam Fung Yuen
https://arxiv.org/abs/2507.14022

CPC-CMS: Cognitive Pairwise Comparison Classification Model Selection Framework for Document-level Sentiment Analysis
This study proposes the Cognitive Pairwise Comparison Classification Model Selection (CPC-CMS) framework for document-level sentiment analysis. The CPC, based on expert knowledge judgment, is used to calculate the weights of evaluation criteria, including accuracy, precision, recall, F1-score, specificity, Matthews Correlation Coefficient (MCC), Cohen's Kappa (Kappa), and efficiency. Naive Bayes, Linear Support Vector Classification (LSVC), Random Forest, Logistic Regression, Extreme Gradient B…

Instability of inverse probability weighting methods and a remedy for non-ignorable missing data
Pengfei Li, Jing Qin, Yukun Liu
https://arxiv.org/abs/2507.17137 https://…

Instability of inverse probability weighting methods and a remedy for non-ignorable missing data
Inverse probability weighting (IPW) methods are commonly used to analyze non-ignorable missing data under the assumption of a logistic model for the missingness probability. However, solving IPW equations numerically may involve non-convergence problems when the sample size is moderate and the missingness probability is high. Moreover, those equations often have multiple roots, and identifying the best root is challenging. Therefore, IPW methods may have low efficiency or even produce biased re…

When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values
Christophe Muller (PREMEDICAL), Erwan Scornet (LPSM), Julie Josse (PREMEDICAL)
https://arxiv.org/abs/2507.13024

When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values
Predicting a response with partially missing inputs remains a challenging task even in parametric models, since parameter estimation in itself is not sufficient to predict on partially observed inputs. Several works study prediction in linear models. In this paper, we focus on logistic models, which present their own difficulties. From a theoretical perspective, we prove that a Pattern-by-Pattern strategy (PbP), which learns one logistic model per missingness pattern, accurately approximates Ba…

Reliability, Embeddedness, and Agency: A Utility-Driven Mathematical Framework for Agent-Centric AI Adoption
Faruk Alpay, Taylan Alpay
https://arxiv.org/abs/2508.12896 https://

Reliability, Embeddedness, and Agency: A Utility-Driven Mathematical Framework for Agent-Centric AI Adoption
We formalize three design axioms for sustained adoption of agent-centric AI systems executing multi-step tasks: (A1) Reliability > Novelty; (A2) Embed > Destination; (A3) Agency > Chat. We model adoption as a sum of a decaying novelty term and a growing utility term and derive the phase conditions for troughs/overshoots with full proofs. We introduce: (i) an identifiability/confounding analysis for $(α,β,N_0,U_{\max})$ with delta-method gradients; (ii) a non-monotone comparator (logistic-with…

Multi-type logistic branching processes with selection: frequency process and genealogy for large carrying capacities
Marta Dai Pra, Julian Kern
https://arxiv.org/abs/2507.12601

Multi-type logistic branching processes with selection: frequency process and genealogy for large carrying capacities
We present a model for growth in a multi-species population. We consider two types evolving as a logistic branching process with mutation, where one of the types has a selective advantage, and are interested in the regime in which the carrying capacity of the system goes to $\infty$. We first study the genealogy of the population up until it almost reaches carrying capacity through a coupling with an independent branching process. We then focus on the phase in which the population has reached c…

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…

Let's play POLO: Integrating the probability of lesion origin into proton treatment plan optimization for low-grade glioma patients
Tim Ortkamp, Habiba Sallem, Semi Harrabi, Martin Frank, Oliver J\"akel, Julia Bauer, Niklas Wahl
https://arxiv.org/abs/2506.13539

Let's play POLO: Integrating the probability of lesion origin into proton treatment plan optimization for low-grade glioma patients
In proton therapy of low-grade glioma (LGG) patients, contrast-enhancing brain lesions (CEBLs) on magnetic resonance imaging are considered predictive of late radiation-induced lesions. From the observation that CEBLs tend to concentrate in regions of increased dose-averaged linear energy transfer (LET) and proximal to the ventricular system, the probability of lesion origin (POLO) model has been established as a multivariate logistic regression model for the voxel-wise probability prediction o…

Enjoying Non-linearity in Multinomial Logistic Bandits
Pierre Boudart (PSL, DI-ENS, Inria), Pierre Gaillard (PSL, DI-ENS, Inria), Alessandro Rudi (PSL, DI-ENS, Inria)
https://arxiv.org/abs/2507.05306

Enjoying Non-linearity in Multinomial Logistic Bandits
We consider the multinomial logistic bandit problem, a variant of generalized linear bandits where a learner interacts with an environment by selecting actions to maximize expected rewards based on probabilistic feedback from multiple possible outcomes. In the binary setting, recent work has focused on understanding the impact of the non-linearity of the logistic model (Faury et al., 2020; Abeille et al., 2021). They introduced a problem-dependent constant $κ_*$, that may be exponentially larg…

Testing parametric additive time-varying GARCH models
Niklas Ahlgren, Alexander Back, Timo Ter\"asvirta
https://arxiv.org/abs/2506.23821 https://

Testing parametric additive time-varying GARCH models
We develop misspecification tests for building additive time-varying (ATV-)GARCH models. In the model, the volatility equation of the GARCH model is augmented by a deterministic time-varying intercept modeled as a linear combination of logistic transition functions. The intercept is specified by a sequence of tests, moving from specific to general. The first test is the test of the standard stationary GARCH model against an ATV-GARCH model with one transition. The alternative model is unidentif…

FedCVD : Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization
Abdelrhman Gaber, Hassan Abd-Eltawab, John Elgallab, Youssif Abuzied, Dineo Mpanya, Turgay Celik, Swarun Kumar, Tamer ElBatt
https://arxiv.org/abs/2507.22963…

FedCVD++: Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization
Cardiovascular diseases (CVD) cause over 17 million deaths annually worldwide, highlighting the urgent need for privacy-preserving predictive systems. We introduce FedCVD++, an enhanced federated learning (FL) framework that integrates both parametric models (logistic regression, SVM, neural networks) and non-parametric models (Random Forest, XGBoost) for coronary heart disease risk prediction. To address key FL challenges, we propose: (1) tree-subset sampling that reduces Random Forest communi…

Soil Texture Prediction with Bayesian Generalized Additive Models for Spatial Compositional Data
Joaqu\'in Mart\'inez-Minaya, Lore Zumeta-Olaskoaga, Dae-Jin Lee
https://arxiv.org/abs/2508.07708

Soil Texture Prediction with Bayesian Generalized Additive Models for Spatial Compositional Data
Compositional data (CoDa) plays an important role in many fields such as ecology, geology, or biology. The most widely used modeling approaches are based on the Dirichlet and the logistic-normal formulation under Aitchison geometry. Recent developments in the mathematical field on the simplex geometry allow to express the regression model in terms of coordinates and estimate its coefficients. Once the model is projected in the real space, we can employ a multivariate Gaussian regression to deal…

Reinforcement Learning for Trade Execution with Market Impact
Patrick Cheridito, Moritz Weiss
https://arxiv.org/abs/2507.06345 https://

Reinforcement Learning for Trade Execution with Market Impact
In this paper, we introduce a novel reinforcement learning framework for optimal trade execution in a limit order book. We formulate the trade execution problem as a dynamic allocation task whose objective is the optimal placement of market and limit orders to maximize expected revenue. By employing multivariate logistic-normal distributions to model random allocations, the framework enables efficient training of the reinforcement learning algorithm. Numerical experiments show that the proposed…

Noise Reinstates Collapsed Populations: Stochastic Reversal of Deterministic Extinction
Vinesh Vijayan, B Priyadharshini, R Sathish Kumar, G Janaki
https://arxiv.org/abs/2507.03954

Noise Reinstates Collapsed Populations: Stochastic Reversal of Deterministic Extinction
Conventional wisdom suggests that environmental noise drives populations toward extinction. In contrast, we report a paradoxical phenomenon in which stochasticity reverses a deterministic tipping point, thereby preventing collapse. Using a hybrid model that integrates logistic growth with a density-triggered sigmoidal collapse, we uncover a striking reversal: deterministic fragility on one side, and stochastic rescue under weak noise on the other. Our analysis demonstrates that noise disrupts t…

Verification of the NOAA Space Weather Prediction Center solar flare forecast (1998-2024)
Enrico Camporeale, Thomas E. Berger
https://arxiv.org/abs/2508.01114 https://

Verification of the NOAA Space Weather Prediction Center solar flare forecast (1998-2024)
The NOAA Space Weather Prediction Center (SWPC) issues the official U.S. government forecast for M-class and X-class solar flares, yet the skill of these forecasts has never been comprehensively verified. In this study, we evaluate the SWPC probabilistic flare forecasts over a 26-year period (1998-2024), comparing them to several zero-cost and statistical baselines including persistence, climatology, Naive Bayes, and logistic regression. We find that the SWPC model does not outperform these bas…

Evaluating the performance of Bayesian cumulative logistic models in randomised controlled trials: a simulation study
Chris J. Selman, Katherine J. Lee, Steven Y. C. Tong, Mark Jones, Robert K. Mahar
https://arxiv.org/abs/2507.21473

Evaluating the performance of Bayesian cumulative logistic models in randomised controlled trials: a simulation study
Background: The proportional odds (PO) model is the most common analytic method for ordinal outcomes in randomised controlled trials. While parameter estimates obtained under departures from PO can be interpreted as an average odds ratio, they can obscure differing treatment effects across the distribution of the ordinal categories. Extensions to the PO model exist and this work evaluates their performance under deviations to the PO assumption. Methods: We evaluated the bias, coverage and mea…

This https://arxiv.org/abs/2501.19277 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

On Pareto Optimality for the Multinomial Logistic Bandit
We provide a new online learning algorithm for tackling the Multinomial Logit Bandit (MNL-Bandit) problem. Despite the challenges posed by the combinatorial nature of the MNL model, we develop a novel Upper Confidence Bound (UCB)-based method that achieves Pareto optimality by balancing regret minimization and estimation error of the assortment revenues and the MNL parameters. We develop theoretical guarantees characterizing the tradeoff between regret and estimation error for the MNL-Bandit pr…