{"total":22,"rows":[{"id":"2301.12710","title":"Machine Learning with High-Cardinality Categorical Features in Actuarial Applications","created":"2023-01-30","cited_by_count":7,"area":null},{"id":"2306.08149","title":"Neural Mixed Effects for Nonlinear Personalized Predictions","created":"2023-06-13","cited_by_count":6,"area":null},{"id":"2601.17782","title":"Shortcut Learning in Binary Classifier Black Boxes: Applications to Voice Anti-Spoofing and Biometrics","created":"2026-01-25","cited_by_count":6,"area":null},{"id":"2407.01115","title":"Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo Methods","created":"2024-07-01","cited_by_count":3,"area":null},{"id":"2307.02071","title":"A Comparison of Machine Learning Methods for Data with High-Cardinality Categorical Variables","created":"2023-07-05","cited_by_count":2,"area":null},{"id":"2310.11654","title":"Subject-specific Deep Neural Networks for Count Data with High-cardinality Categorical Features","created":"2023-10-18","cited_by_count":1,"area":null},{"id":"2403.03613","title":"Reducing the dimensionality and granularity in hierarchical categorical variables","created":"2024-03-06","cited_by_count":1,"area":null},{"id":"2410.24079","title":"Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models","created":"2024-10-31","cited_by_count":1,"area":null},{"id":"2412.16899","title":"Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data","created":"2024-12-22","cited_by_count":1,"area":null},{"id":"2505.00216","title":"Online Federation For Mixtures of Proprietary Agents with Black-Box Encoders","created":"2025-04-30","cited_by_count":1,"area":null},{"id":"2604.10976","title":"Neural Generalized Mixed-Effects Models","created":"2026-04-13","cited_by_count":1,"area":null},{"id":"2411.18416","title":"Probabilistic size-and-shape functional mixed models","created":"2024-11-27","cited_by_count":0,"area":null},{"id":"2505.09552","title":"Scalable Krylov Subspace Methods for Generalized Mixed-Effects Models with Crossed Random Effects","created":"2025-05-14","cited_by_count":0,"area":null},{"id":"2507.05064","title":"Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processes","created":"2025-07-07","cited_by_count":0,"area":null},{"id":"2510.22198","title":"MMbeddings: Parameter-Efficient, Low-Overfitting Probabilistic Embeddings Inspired by Nonlinear Mixed Models","created":"2025-10-25","cited_by_count":0,"area":null},{"id":"2603.26575","title":"The Climber's Grip -- Personalized Deep Learning Models for Fear and Muscle Activity in Climbing","created":"2026-03-27","cited_by_count":0,"area":null},{"id":"2605.04744","title":"MixINN: Accelerating Plant Breeding by Combining Mixed Models and Deep Learning for Interaction Prediction","created":"2026-05-06","cited_by_count":0,"area":null},{"id":"2606.02228","title":"Bayesian meta-learning for modeling Alzheimer's disease progression","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2607.04647","title":"Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Data","created":"2026-07-06","cited_by_count":0,"area":null},{"id":"2607.08238","title":"Structure Learning on Clustered Data","created":"2026-07-09","cited_by_count":0,"area":null},{"id":"2608.29714","title":"Neural ODE enhanced linear mixed effect models for estimating complex association patterns of time-varying covariates with the marker trajectory","created":"2026-08-30","cited_by_count":0,"area":null},{"id":"2609.28122","title":"NPBoost: Neural Processes with Gradient-Boosted Fixed Effects","created":"2026-09-23","cited_by_count":0,"area":null}],"next_cursor":null}