{"total":21,"rows":[{"id":"2107.05847","title":"Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges","created":"2021-07-13","cited_by_count":145,"area":null},{"id":"2206.04115","title":"Simplifying Polylogarithms with Machine Learning","created":"2022-06-08","cited_by_count":15,"area":null},{"id":"2201.11358","title":"Fairness Implications of Encoding Protected Categorical Attributes","created":"2022-01-27","cited_by_count":7,"area":null},{"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":"2307.09191","title":"A benchmark of categorical encoders for binary classification","created":"2023-07-17","cited_by_count":6,"area":null},{"id":"2201.13123","title":"Lessons from the AdKDD'21 Privacy-Preserving ML Challenge","created":"2022-01-31","cited_by_count":4,"area":null},{"id":"2312.16930","title":"Encoding categorical data: Is there yet anything 'hotter' than one-hot encoding?","created":"2023-12-28","cited_by_count":3,"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":"2607.00477","title":"Interpretable vs Learned Encoders for High-Cardinality Fraud Detection","created":"2026-07-01","cited_by_count":3,"area":null},{"id":"2401.09682","title":"Comparative Study on the Performance of Categorical Variable Encoders in Classification and Regression Tasks","created":"2024-01-18","cited_by_count":2,"area":null},{"id":"2309.16146","title":"T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systems","created":"2023-09-28","cited_by_count":1,"area":null},{"id":"2402.00054","title":"Predicting loss-of-function impact of genetic mutations: a machine learning approach","created":"2024-01-26","cited_by_count":1,"area":null},{"id":"2402.14980","title":"Comparative Analysis of Data Preprocessing Methods, Feature Selection Techniques and Machine Learning Models for Improved Classification and Regression Performance on Imbalanced Genetic Data","created":"2024-02-22","cited_by_count":1,"area":null},{"id":"2411.08482","title":"Methodology for an Analysis of Influencing Factors on 3D Object Detection Performance","created":"2024-11-13","cited_by_count":1,"area":null},{"id":"2109.08010","title":"WildWood: a new Random Forest algorithm","created":"2021-09-16","cited_by_count":0,"area":null},{"id":"2202.03212","title":"Introducing explainable supervised machine learning into interactive feedback loops for statistical production system","created":"2022-02-07","cited_by_count":0,"area":null},{"id":"2306.09775","title":"Using Machine Learning Methods for Automation of Size Grid Building and Management","created":"2023-06-16","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":"2510.12659","title":"SG-XDEAT: Sparsity-Guided Cross-Dimensional and Cross-Encoding Attention with Target-Aware Conditioning in Tabular Learning","created":"2025-10-14","cited_by_count":0,"area":null},{"id":"2602.00412","title":"Robustness of AutoML on Dirty Categorical Data","created":"2026-01-31","cited_by_count":0,"area":null},{"id":"2610.13676","title":"On the Encoding of Calendar Features in Covariate-Informed Time Series Foundation Models","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}