This paper establishes a large deviation principle for the generalization error of interpolating classifiers in the overparametrized regime.

This paper extends gradient boosting to functions of vector inputs using a simple algorithm with histogram-based decision trees.

This paper compares the effect of different initialization methods on the accuracy and complexity of solutions in genetic programming for symbolic regression, finding no significant differences.

The twoblock clustering tree is introduced as a new type of interpretable regression tree for multivariate responses, using local multivariate linear models as leaves and twoblock dimension reduction…

The study assesses the generalization capability of supervised machine learning models for intrusion detection using UNSW-NB15 and TON_IoT, finding a significant performance drop when models are teste…

This paper presents an improved parallel version of MaxFEM algorithm for maximal frequent episode mining, achieving up to 8x speedup in C++ implementation and 35x improvement overall.

The paper introduces PRAXIS, a novel algorithm that efficiently approximates the computation of 'Rashomon sets' for decision trees, significantly reducing memory and runtime complexity.

This paper enhances anomaly detection and threat intelligence in Zero Trust IoT environments by applying and comparing various machine learning classifiers, notably using SMOTE to improve accuracy on…

The paper proposes a structural method using decision tree rulesets and multiple complementary metrics to detect concept drift in evolving malware families, finding that fixed-interval windowing with…

This book provides a compact, derivation-oriented mathematical primer that connects major families of generative AI models, showing their underlying structural relationships.

This paper develops statistical learning theory for gradient boosting in Peaks-over-Threshold modeling using Generalized Pareto distributions, deriving error bounds and reducing gradient correlation.

This paper compares traditional machine learning models (Random Forests, XGBoost, SVM) against a complex Unified Multi-Task Time Series Model for churn prediction, concluding that conventional methods…

This paper improves IoT intrusion detection by addressing severe class imbalance using SMOTE and evaluating eight machine learning models, finding that Random Forest and Extra Trees achieve high perfo…

This paper improves IoT intrusion detection by addressing severe class imbalance using SMOTE and comparing the performance of multiple machine learning models on side-channel power data, showing Rando…

The paper analyzes a new class of asynchronous adaptive first-order optimization methods and proves their stochastic convergence rate is O(1/sqrt{t}) for non-convex functions.

This paper introduces a method to automatically determine the optimal learning period ($ au$) for the Random Gradient hyper-heuristic, enabling it to optimally solve Pseudo-Boolean Problems without ma…

This paper proposes using genetic programming (GP) to jointly evolve both the feature sets and the structure of survival trees, resulting in highly interpretable and high-performing shallow models for…