{"total":82,"rows":[{"id":"2209.09326","title":"Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection","created":"2022-09-19","cited_by_count":28,"area":null},{"id":"2111.01743","title":"Designing Inherently Interpretable Machine Learning Models","created":"2021-11-02","cited_by_count":23,"area":null},{"id":"2301.06676","title":"Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life","created":"2023-01-17","cited_by_count":19,"area":null},{"id":"2301.11862","title":"Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean","created":"2023-01-27","cited_by_count":18,"area":null},{"id":"2501.15638","title":"A Comprehensive Survey on Self-Interpretable Neural Networks","created":"2025-01-26","cited_by_count":18,"area":null},{"id":"2307.05506","title":"Data-Driven Design for Metamaterials and Multiscale Systems: A Review","created":"2023-07-01","cited_by_count":17,"area":null},{"id":"2104.08903","title":"SurvNAM: The machine learning survival model explanation","created":"2021-04-18","cited_by_count":16,"area":null},{"id":"2305.16765","title":"Backpack Language Models","created":"2023-05-26","cited_by_count":15,"area":null},{"id":"2305.16905","title":"Improving Neural Additive Models with Bayesian Principles","created":"2023-05-26","cited_by_count":15,"area":null},{"id":"2209.10070","title":"Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring","created":"2022-09-21","cited_by_count":12,"area":null},{"id":"2407.11359","title":"Feature Inference Attack on Shapley Values","created":"2024-07-16","cited_by_count":9,"area":null},{"id":"2211.17264","title":"Interpretability with full complexity by constraining feature information","created":"2022-11-30","cited_by_count":7,"area":null},{"id":"2204.09123","title":"GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints","created":"2022-04-19","cited_by_count":6,"area":null},{"id":"2302.09275","title":"Structural Neural Additive Models: Enhanced Interpretable Machine Learning","created":"2023-02-18","cited_by_count":6,"area":null},{"id":"2307.08175","title":"Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models","created":"2023-07-17","cited_by_count":6,"area":null},{"id":"2510.24815","title":"Tree Ensemble Explainability through the Hoeffding Functional Decomposition and TreeHFD Algorithm","created":"2025-10-28","cited_by_count":6,"area":null},{"id":"2409.13723","title":"Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges","created":"2024-09-09","cited_by_count":5,"area":null},{"id":"2204.12365","title":"Explaining Adverse Actions in Credit Decisions Using Shapley Decomposition","created":"2022-04-26","cited_by_count":4,"area":null},{"id":"2301.07060","title":"Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance","created":"2023-01-17","cited_by_count":4,"area":null},{"id":"2305.00799","title":"How to address monotonicity for model risk management?","created":"2023-04-28","cited_by_count":4,"area":null},{"id":"2305.04214","title":"PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics","created":"2023-05-07","cited_by_count":4,"area":null},{"id":"2305.15670","title":"Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons","created":"2023-05-25","cited_by_count":4,"area":null},{"id":"2511.13234","title":"MorphBoost: Self-Organizing Universal Gradient Boosting with Adaptive Tree Morphing","created":"2025-11-17","cited_by_count":4,"area":null},{"id":"2604.16042","title":"Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures","created":"2026-04-17","cited_by_count":4,"area":"mechinterp"},{"id":"2108.04855","title":"Attention-like feature explanation for tabular data","created":"2021-08-10","cited_by_count":3,"area":null},{"id":"2108.11328","title":"Predicting Census Survey Response Rates With Parsimonious Additive Models and Structured Interactions","created":"2021-08-24","cited_by_count":3,"area":null},{"id":"2207.06950","title":"Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models","created":"2022-07-14","cited_by_count":3,"area":null},{"id":"2208.05369","title":"E Pluribus Unum Interpretable Convolutional Neural Networks","created":"2022-08-10","cited_by_count":3,"area":null},{"id":"2302.08434","title":"On marginal feature attributions of tree-based models","created":"2023-02-16","cited_by_count":3,"area":null},{"id":"2308.03730","title":"SurvBeX: An explanation method of the machine learning survival models based on the Beran estimator","created":"2023-08-07","cited_by_count":3,"area":null},{"id":"2504.07011","title":"FAME: Introducing Fuzzy Additive Models for Explainable AI","created":"2025-04-09","cited_by_count":3,"area":null},{"id":"2504.10169","title":"Challenges in interpretability of additive models","created":"2025-04-14","cited_by_count":3,"area":null},{"id":"2510.24601","title":"Comparison of generalised additive models and neural networks in applications: A systematic review","created":"2025-10-28","cited_by_count":3,"area":null},{"id":"2209.10082","title":"Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance","created":"2022-09-21","cited_by_count":2,"area":null},{"id":"2209.10127","title":"Interpretable Selective Learning in Credit Risk","created":"2022-09-21","cited_by_count":2,"area":null},{"id":"2405.05429","title":"How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression","created":"2024-05-08","cited_by_count":2,"area":null},{"id":"2405.17483","title":"Concept-based Explainable Malignancy Scoring on Pulmonary Nodules in CT Images","created":"2024-05-24","cited_by_count":2,"area":null},{"id":"2410.10535","title":"Transparent Networks for Multivariate Time Series","created":"2024-10-14","cited_by_count":2,"area":null},{"id":"2410.19067","title":"Less Discriminatory Alternative and Interpretable XGBoost Framework for Binary Classification","created":"2024-10-24","cited_by_count":2,"area":null},{"id":"2410.19098","title":"Inherently Interpretable Tree Ensemble Learning","created":"2024-10-24","cited_by_count":2,"area":null},{"id":"2412.00225","title":"Meta-learning Loss Functions of Parametric Partial Differential Equations Using Physics-Informed Neural Networks","created":"2024-11-29","cited_by_count":2,"area":null},{"id":"2503.01702","title":"Relating Piecewise Linear Kolmogorov Arnold Networks to ReLU Networks","created":"2025-03-03","cited_by_count":2,"area":null},{"id":"2602.04006","title":"Rational ANOVA Networks","created":"2026-02-03","cited_by_count":2,"area":null},{"id":"2604.13332","title":"Selecting Feature Interactions for Generalized Additive Models by Distilling Foundation Models","created":"2026-04-14","cited_by_count":2,"area":null},{"id":"2208.12814","title":"Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death","created":"2022-08-28","cited_by_count":1,"area":null},{"id":"2212.13392","title":"DeepCuts: Single-Shot Interpretability based Pruning for BERT","created":"2022-12-27","cited_by_count":1,"area":null},{"id":"2309.02426","title":"Monotone Tree-Based GAMI Models by Adapting XGBoost","created":"2023-09-05","cited_by_count":1,"area":null},{"id":"2309.15319","title":"DeepROCK: Error-controlled interaction detection in deep neural networks","created":"2023-09-26","cited_by_count":1,"area":null},{"id":"2309.16970","title":"Discrete-Choice Model with Generalized Additive Utility Network","created":"2023-09-29","cited_by_count":1,"area":null},{"id":"2405.13187","title":"A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis","created":"2024-05-21","cited_by_count":1,"area":null},{"id":"2602.03034","title":"KANFIS: A Neuro-Symbolic Framework for Interpretable and Uncertainty-Aware Learning","created":"2026-02-03","cited_by_count":1,"area":null},{"id":"2608.24582","title":"$\\texttt{findr}$: Transparent and Fair Credit Risk Decisions through Semi-Structured Regressions","created":"2026-08-25","cited_by_count":1,"area":null},{"id":"2111.08255","title":"A Unified and Fast Interpretable Model for Predictive Analytics","created":"2021-11-16","cited_by_count":0,"area":null},{"id":"2112.05071","title":"A Novel Tropical Geometry-based Interpretable Machine Learning Method: Application in Prognosis of Advanced Heart Failure","created":"2021-12-09","cited_by_count":0,"area":null},{"id":"2212.10707","title":"Extractive Text Summarization Using Generalized Additive Models with Interactions for Sentence Selection","created":"2022-12-21","cited_by_count":0,"area":null},{"id":"2307.08485","title":"Cross Feature Selection to Eliminate Spurious Interactions and Single Feature Dominance Explainable Boosting Machines","created":"2023-07-17","cited_by_count":0,"area":null},{"id":"2309.02417","title":"Computing SHAP Efficiently Using Model Structure Information","created":"2023-09-05","cited_by_count":0,"area":null},{"id":"2312.06638","title":"SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Models","created":"2023-12-11","cited_by_count":0,"area":null},{"id":"2401.16294","title":"Dual feature-based and example-based explanation methods","created":"2024-01-29","cited_by_count":0,"area":null},{"id":"2403.11363","title":"IGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insight","created":"2024-03-17","cited_by_count":0,"area":null},{"id":"2503.07642","title":"dnamite: A Python Package for Neural Additive Models","created":"2025-03-06","cited_by_count":0,"area":null},{"id":"2504.01223","title":"Explainable post-training bias mitigation with distribution-based fairness metrics","created":"2025-04-01","cited_by_count":0,"area":null},{"id":"2505.08610","title":"neuralGAM: An R Package for Fitting Generalized Additive Neural Networks","created":"2025-05-13","cited_by_count":0,"area":null},{"id":"2508.03913","title":"Fast and Accurate Explanations of Distance-Based Classifiers by Uncovering Latent Explanatory Structures","created":"2025-08-05","cited_by_count":0,"area":null},{"id":"2509.08467","title":"An Interpretable Deep Learning Model for General Insurance Pricing","created":"2025-09-10","cited_by_count":0,"area":null},{"id":"2509.10825","title":"CUBE: Contrastive Understanding by Balanced Experiments","created":"2025-09-13","cited_by_count":0,"area":null},{"id":"2509.21923","title":"Multiplicative-Additive Constrained Models:Toward Joint Visualization of Interactive and Independent Effects","created":"2025-09-26","cited_by_count":0,"area":null},{"id":"2509.23068","title":"Sparse Deep Additive Model with Interactions: Enhancing Interpretability and Predictability","created":"2025-09-27","cited_by_count":0,"area":null},{"id":"2511.11922","title":"Additive Large Language Models for Semi-Structured Text","created":"2025-11-14","cited_by_count":0,"area":null},{"id":"2512.03578","title":"When, How Long and How Much? Interpretable Neural Networks for Time Series Regression by Learning to Mask and Aggregate","created":"2025-12-03","cited_by_count":0,"area":null},{"id":"2602.16503","title":"Interpretability-by-Design with Accurate Locally Additive Models and Conditional Feature Effects","created":"2026-02-18","cited_by_count":0,"area":null},{"id":"2604.14880","title":"xFODE+: Explainable Type-2 Fuzzy Additive ODEs for Uncertainty Quantification","created":"2026-04-16","cited_by_count":0,"area":null},{"id":"2604.14883","title":"xFODE: An Explainable Fuzzy Additive ODE Framework for System Identification","created":"2026-04-16","cited_by_count":0,"area":null},{"id":"2604.18864","title":"ParamBoost: Gradient Boosted Piecewise Cubic Polynomials","created":"2026-04-20","cited_by_count":0,"area":null},{"id":"2605.05493","title":"A renormalization-group inspired lattice-based framework for piecewise generalized linear models","created":"2026-05-06","cited_by_count":0,"area":null},{"id":"2605.31189","title":"FlagGAM: Rule-Basis Generalized Additive Models for Explainable Tabular Prediction","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2605.31200","title":"Beyond Additive Decompositions: Interpretability Through Separability","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2607.16478","title":"K-IPO: Kendall-constrained Importance Preserving Oversampling for Imbalanced Tabular Data","created":"2026-07-17","cited_by_count":0,"area":null},{"id":"2607.20201","title":"The Quadrilateral Loss: Additivity as a Measurable Behavior of Dense Neural Networks","created":"2026-07-22","cited_by_count":0,"area":null},{"id":"2608.12194","title":"HYDRA: Hyperbolic Dynamic Representation Architecture for Kolmogorov-Arnold Networks","created":"2026-08-12","cited_by_count":0,"area":null},{"id":"2608.14820","title":"Handover Analysis for Vehicular Communication with Explainability on the Fly","created":"2026-08-14","cited_by_count":0,"area":null},{"id":"2609.05946","title":"Interpretable and Fair Generalized Additive Neural Networks via Multi-objective Learning","created":"2026-09-05","cited_by_count":0,"area":null}],"next_cursor":null}