arXiv:2511.11767v2 Announce Type: replace 
Abstract: Despite recent advances in fairness-aware machine learning, predictive models often exhibit discriminatory behavior towards marginalized groups. Such unfairness might arise from biased training data, model design, or representational disparities across groups, posing significant challenges in high-stakes decision-making domains such as college admissions. While existing fair learning models aim to mitigate bias, achieving an optimal trade-off between fairness and accuracy remains a challenge. Moreover, the reliance on black-box models hinders interpretability, limiting their applicability in socially sensitive domains. To circumvent these issues, we propose integrating Kolmogorov-Arnold Networks (KANs) within a fair adversarial learning framework. Leveraging the adversarial robustness and interpretability of KANs, our approach facilitates stable adversarial learning. We derive theoretical insights into the spline-based KAN architecture that ensure stability during adversarial optimization. Additionally, an adaptive fairness penalty update mechanism is proposed to strike a balance between fairness and accuracy. We back these findings with empirical evidence on two real-world admissions datasets, demonstrating the proposed framework's efficiency in achieving fairness across sensitive attributes while preserving predictive performance.

تسلط التطورات الأخيرة في التعلم الآلي الواعي بالعدالة الضوء على السلوكيات التمييزية المستمرة في النماذج التنبؤية، خاصة تجاه الفئات المهمشة. تنشأ هذه المشكلات غالبًا من بيانات تدريب متحيزة وتصميم النموذج، مما يعقد القرارات الحرجة مثل قبول الجامعات. تكافح نماذج التعلم العادل الحالية لتحقيق توازن بين العدالة والدقة، بينما تحد النماذج غير الشفافة من قابلية التفسير. لمعالجة هذه التحديات، يُقترح دمج شبكات كولموغوروف-أرنولد (KANs) ضمن إطار تعلم عدائي عادل، مما يعزز من القوة والتفسير.

Los avances recientes en el aprendizaje automático consciente de la equidad destacan los comportamientos discriminatorios persistentes en los modelos predictivos, especialmente hacia grupos marginados. Estos problemas a menudo surgen de datos de entrenamiento sesgados y del diseño del modelo, complicando decisiones críticas como las admisiones universitarias. Los modelos de aprendizaje justo existentes luchan por equilibrar la equidad y la precisión, mientras que los modelos de caja negra limitan la interpretabilidad. Para abordar estos desafíos, se propone integrar Redes Kolmogorov-Arnold (KA…

Les avancées récentes dans l'apprentissage automatique conscient de l'équité mettent en lumière les comportements discriminatoires persistants dans les modèles prédictifs, en particulier envers les groupes marginalisés. Ces problèmes proviennent souvent de données d'entraînement biaisées et de conceptions de modèles, compliquant les décisions critiques comme les admissions universitaires. Les modèles d'apprentissage équitable existants peinent à équilibrer équité et précision, tandis que les modèles en boîte noire limitent l'interprétabilité. Pour relever ces défis, une nouvelle approche intèg…

Recent advancements in fairness-aware machine learning highlight persistent discriminatory behaviors in predictive models, particularly affecting marginalized groups. These issues often stem from biased training data and model design, complicating high-stakes decisions like college admissions. Existing fair learning models struggle to balance fairness and accuracy, while black-box models limit interpretability. To address these challenges, a new approach integrates Kolmogorov-Arnold Networks (KANs) into a fair adversarial learning framework, enhancing robustness and interpretability.

Learning Fair Representations with Kolmogorov-Arnold Networks

arXiv:2511.16986v1 Announce Type: new 
Abstract: Radiomap serves as a vital tool for wireless network management and deployment by providing powerful spatial knowledge of signal propagation and coverage. However, increasingly complex radio propagation behavior and surrounding environments pose strong challenges for radiomap estimation (RME). In this work, we propose a knowledge-guided RME framework that integrates Kolmogorov-Arnold Networks (KAN) with Mixture-of-Experts (MoE), namely RadioKMoE. Specifically, we design a KAN module to predict an initial coarse coverage map, leveraging KAN's strength in approximating physics models and global radio propagation patterns. The initial coarse map, together with environmental information, drives our MoE network for precise radiomap estimation. Unlike conventional deep learning models, the MoE module comprises expert networks specializing in distinct radiomap patterns to improve local details while preserving global consistency. Experimental results in both multi- and single-band RME demonstrate the enhanced accuracy and robustness of the proposed RadioKMoE in radiomap estimation.

التطور الأخير لـ RadioKMoE يقدم إطارًا موجهًا بالمعرفة لتقدير خرائط الإشعاع، حيث يجمع بين شبكات كولموغوروف-أرنولد (KAN) ومزيج من الخبراء (MoE) لتحسين إدارة الشبكات اللاسلكية. يهدف هذا النهج المبتكر إلى معالجة التحديات التي تطرحها سلوكيات انتشار الإشعاع المعقدة والبيئات المحيطة، مع السعي للحصول على توقعات أكثر دقة لتغطية الإشارة.

El reciente desarrollo de RadioKMoE introduce un marco guiado por el conocimiento para la estimación de radiomapas, combinando Redes de Kolmogorov-Arnold (KAN) con Mixtures of Experts (MoE) para mejorar la gestión de redes inalámbricas. Este enfoque innovador aborda los desafíos planteados por los comportamientos complejos de propagación de radio y los entornos, buscando predicciones más precisas de cobertura de señal.

Le développement récent de RadioKMoE introduit un cadre guidé par les connaissances pour l'estimation des radiomaps, combinant les réseaux de Kolmogorov-Arnold (KAN) avec des Mixtures d'Experts (MoE) pour améliorer la gestion des réseaux sans fil. Cette approche innovante vise à relever les défis posés par les comportements complexes de propagation radio et les environnements, en visant des prédictions de couverture de signal plus précises.

The recent development of RadioKMoE introduces a knowledge-guided framework for radiomap estimation, combining Kolmogorov-Arnold Networks (KAN) with Mixture-of-Experts (MoE) to enhance wireless network management. This innovative approach addresses the challenges posed by complex radio propagation behaviors and environments, aiming for more accurate signal coverage predictions.

RadioKMoE: Knowledge-Guided Radiomap Estimation with Kolmogorov-Arnold Networks and Mixture-of-Experts

arXiv:2509.23366v2 Announce Type: replace 
Abstract: Feature selection is a key step in many tabular prediction problems, where multiple candidate variables may be redundant, noisy, or weakly informative. We investigate feature selection based on Kolmogorov-Arnold Networks (KANs), which parameterize feature transformations with splines and expose per-feature importance scores in a natural way. From this idea we derive four KAN-based selection criteria (coefficient norms, gradient-based saliency, and knockout scores) and compare them with standard methods such as LASSO, Random Forest feature importance, Mutual Information, and SVM-RFE on a suite of real and synthetic classification and regression datasets. Using average F1 and $R^2$ scores across three feature-retention levels (20%, 40%, 60%), we find that KAN-based selectors are generally competitive with, and sometimes superior to, classical baselines. In classification, KAN criteria often match or exceed existing methods on multi-class tasks by removing redundant features and capturing nonlinear interactions. In regression, KAN-based scores provide robust performance on noisy and heterogeneous datasets, closely tracking strong ensemble predictors; we also observe characteristic failure modes, such as overly aggressive pruning with an $\ell_1$ criterion. Stability and redundancy analyses further show that KAN-based selectors yield reproducible feature subsets across folds while avoiding unnecessary correlation inflation, ensuring reliable and non-redundant variable selection. Overall, our findings demonstrate that KAN-based feature selection provides a powerful and interpretable alternative to traditional methods, capable of uncovering nonlinear and multivariate feature relevance beyond sparsity or impurity-based measures.

دراسة حديثة تقدم إطارًا لاختيار الميزات في البيانات الجدولية الخاضعة للإشراف باستخدام شبكات كولموغوروف-أرنولد (KAN)، التي تستخدم المنحنيات لاستنتاج درجات أهمية الميزات. تقارن الدراسة معايير الاختيار المستندة إلى KAN بأساليب تقليدية مثل LASSO وRandom Forest، مما يظهر أداءً تنافسيًا في مهام التصنيف والانحدار عبر مجموعات بيانات متنوعة.

Un estudio reciente presenta un marco para la selección de características en datos tabulares supervisados utilizando Redes de Kolmogorov-Arnold (KAN), que emplean splines para derivar puntajes de importancia de características. La investigación compara los criterios de selección basados en KAN con métodos tradicionales como LASSO y Random Forest, demostrando un rendimiento competitivo en tareas de clasificación y regresión en varios conjuntos de datos.

Une étude récente présente un cadre pour la sélection de caractéristiques dans des données tabulaires supervisées utilisant des Réseaux de Kolmogorov-Arnold (KAN), qui utilisent des splines pour dériver des scores d'importance des caractéristiques. La recherche compare les critères de sélection basés sur KAN à des méthodes traditionnelles comme LASSO et Random Forest, montrant une performance compétitive dans des tâches de classification et de régression sur divers ensembles de données.

A recent study introduces a framework for feature selection in supervised tabular data using Kolmogorov-Arnold Networks (KANs), which utilize splines to derive feature importance scores. The research compares KAN-based selection criteria against traditional methods like LASSO and Random Forest, demonstrating competitive performance in classification and regression tasks across various datasets.

Learning Fair Representations with Kolmogorov-Arnold Networks

Was this article worth reading? Share it

College Tools

LucidQuery AI

Augmeta