{"total":39,"rows":[{"id":"2311.05877","title":"A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning","created":"2023-11-10","cited_by_count":24,"area":null},{"id":"2403.03838","title":"Feature Selection as Deep Sequential Generative Learning","created":"2024-03-06","cited_by_count":21,"area":null},{"id":"2112.06580","title":"How to Find a Good Explanation for Clustering?","created":"2021-12-13","cited_by_count":16,"area":null},{"id":"2210.00354","title":"Model-X Sequential Testing for Conditional Independence via Testing by Betting","created":"2022-10-01","cited_by_count":14,"area":null},{"id":"2209.14881","title":"Sequential Attention for Feature Selection","created":"2022-09-29","cited_by_count":9,"area":null},{"id":"2303.07200","title":"Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks","created":"2023-03-10","cited_by_count":7,"area":null},{"id":"2211.14144","title":"Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data","created":"2022-11-25","cited_by_count":5,"area":null},{"id":"2104.14787","title":"A User-Guided Bayesian Framework for Ensemble Feature Selection in Life Science Applications (UBayFS)","created":"2021-04-30","cited_by_count":4,"area":null},{"id":"2109.14719","title":"Deep neural networks with controlled variable selection for the identification of putative causal genetic variants","created":"2021-09-29","cited_by_count":4,"area":null},{"id":"2204.07293","title":"Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees","created":"2022-04-15","cited_by_count":4,"area":null},{"id":"2210.00604","title":"Ensembling improves stability and power of feature selection for deep learning models","created":"2022-10-02","cited_by_count":3,"area":null},{"id":"2408.04583","title":"Unveiling the Power of Sparse Neural Networks for Feature Selection","created":"2024-08-08","cited_by_count":3,"area":null},{"id":"2207.01022","title":"Learning to Increase the Power of Conditional Randomization Tests","created":"2022-07-03","cited_by_count":2,"area":null},{"id":"2304.02383","title":"How good Neural Networks interpretation methods really are? A quantitative benchmark","created":"2023-04-05","cited_by_count":2,"area":null},{"id":"2304.11237","title":"Effective Neural Network $L_0$ Regularization With BinMask","created":"2023-04-21","cited_by_count":2,"area":null},{"id":"2306.04766","title":"Enabling tabular deep learning when $d \\gg n$ with an auxiliary knowledge graph","created":"2023-06-07","cited_by_count":2,"area":null},{"id":"2307.13704","title":"eXplainable Artificial Intelligence (XAI) in aging clock models","created":"2023-07-21","cited_by_count":2,"area":null},{"id":"2404.04317","title":"DeepLINK-T: deep learning inference for time series data using knockoffs and LSTM","created":"2024-04-05","cited_by_count":2,"area":null},{"id":"2109.05402","title":"Differentially Private Variable Selection via the Knockoff Filter","created":"2021-09-12","cited_by_count":1,"area":null},{"id":"2203.04483","title":"Error-based Knockoffs Inference for Controlled Feature Selection","created":"2022-03-09","cited_by_count":1,"area":null},{"id":"2209.12282","title":"Deep Feature Selection Using a Novel Complementary Feature Mask","created":"2022-09-25","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":"2402.17176","title":"DeepDRK: Deep Dependency Regularized Knockoff for Feature Selection","created":"2024-02-27","cited_by_count":1,"area":null},{"id":"2410.02208","title":"Nonparametric IPSS: Fast, flexible feature selection with false discovery control","created":"2024-10-03","cited_by_count":1,"area":null},{"id":"2512.04696","title":"Provable FDR Control for Deep Feature Selection: Deep MLPs and Beyond","created":"2025-12-04","cited_by_count":1,"area":null},{"id":"2203.02849","title":"Variable Selection with the Knockoffs: Composite Null Hypotheses","created":"2022-03-06","cited_by_count":0,"area":null},{"id":"2209.07011","title":"Error Controlled Feature Selection for Ultrahigh Dimensional and Highly Correlated Feature Space Using Deep Learning","created":"2022-09-15","cited_by_count":0,"area":null},{"id":"2301.04740","title":"The Berkelmans-Pries Feature Importance Method: A Generic Measure of Informativeness of Features","created":"2023-01-11","cited_by_count":0,"area":null},{"id":"2312.15055","title":"Deep Learning for Efficient GWAS Feature Selection","created":"2023-12-22","cited_by_count":0,"area":null},{"id":"2408.17016","title":"Error-controlled non-additive interaction discovery in machine learning models","created":"2024-08-30","cited_by_count":0,"area":null},{"id":"2411.11436","title":"Implicit Regularization for Multi-label Feature Selection","created":"2024-11-18","cited_by_count":0,"area":null},{"id":"2411.16666","title":"CatNet: Controlling the False Discovery Rate in LSTM with SHAP Feature Importance and Gaussian Mirrors","created":"2024-11-25","cited_by_count":0,"area":null},{"id":"2508.18901","title":"Sparse minimum Redundancy Maximum Relevance for feature selection","created":"2025-08-26","cited_by_count":0,"area":null},{"id":"2510.01418","title":"DiffKnock: Diffusion-based Knockoff Statistics for Neural Networks Inference","created":"2025-10-01","cited_by_count":0,"area":null},{"id":"2602.00218","title":"GRIP2: A Robust and Powerful Deep Knockoff Method for Feature Selection","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2603.00955","title":"Beyond False Discovery Rate: A Stepdown Group SLOPE Approach for Grouped Variable Selection","created":"2026-03-01","cited_by_count":0,"area":null},{"id":"2603.24041","title":"Minimal Sufficient Representations for Self-interpretable Deep Neural Networks","created":"2026-03-25","cited_by_count":0,"area":null},{"id":"2606.04404","title":"Knockoffs-based False Discovery Rate Control and Simplification for Deep Neural Networks","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2608.00989","title":"Model-Agnostic FDR Control via Group Gaussian Mirror and Permutation SHAP","created":"2026-08-02","cited_by_count":0,"area":null}],"next_cursor":null}