{"total":97,"rows":[{"id":"2303.05981","title":"Feature Importance: A Closer Look at Shapley Values and LOCO","created":"2023-03-10","cited_by_count":23,"area":null},{"id":"2301.12649","title":"Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery","created":"2023-01-30","cited_by_count":15,"area":null},{"id":"2302.07294","title":"Derandomized Novelty Detection with FDR Control via Conformal E-values","created":"2023-02-14","cited_by_count":15,"area":null},{"id":"2403.04015","title":"Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent","created":"2024-03-06","cited_by_count":15,"area":null},{"id":"2112.11079","title":"Data fission: splitting a single data point","created":"2021-12-21","cited_by_count":14,"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":"2211.00631","title":"Composite Feature Selection using Deep Ensembles","created":"2022-11-01","cited_by_count":11,"area":null},{"id":"2110.06048","title":"The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control","created":"2021-10-12","cited_by_count":10,"area":null},{"id":"2405.20039","title":"Task-Agnostic Machine-Learning-Assisted Inference","created":"2024-05-30","cited_by_count":10,"area":null},{"id":"2306.14693","title":"Conformal link prediction for false discovery rate control","created":"2023-06-26","cited_by_count":9,"area":null},{"id":"2102.12328","title":"Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning","created":"2021-02-23","cited_by_count":7,"area":null},{"id":"2303.14281","title":"Sequential Knockoffs for Variable Selection in Reinforcement Learning","created":"2023-03-24","cited_by_count":7,"area":null},{"id":"2307.12544","title":"Adaptive debiased machine learning using data-driven model selection techniques","created":"2023-07-24","cited_by_count":7,"area":null},{"id":"2209.11497","title":"Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions","created":"2022-09-23","cited_by_count":5,"area":null},{"id":"2401.15139","title":"FDR-Controlled Portfolio Optimization for Sparse Financial Index Tracking","created":"2024-01-26","cited_by_count":5,"area":null},{"id":"2102.00951","title":"Counterfactual Generation with Knockoffs","created":"2021-02-01","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":"2111.00043","title":"Multivariate rank via entropic optimal transport: sample efficiency and generative modeling","created":"2021-10-29","cited_by_count":4,"area":null},{"id":"2207.04055","title":"Causal Discovery using Model Invariance through Knockoff Interventions","created":"2022-07-08","cited_by_count":4,"area":null},{"id":"2401.08375","title":"Sparse PCA with False Discovery Rate Controlled Variable Selection","created":"2024-01-16","cited_by_count":4,"area":null},{"id":"2109.10817","title":"Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals","created":"2021-09-22","cited_by_count":3,"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":"2211.02778","title":"Near-optimal multiple testing in Bayesian linear models with finite-sample FDR control","created":"2022-11-04","cited_by_count":3,"area":null},{"id":"2401.15796","title":"High-Dimensional False Discovery Rate Control for Dependent Variables","created":"2024-01-28","cited_by_count":3,"area":null},{"id":"2403.00563","title":"Indirectly Parameterized Concrete Autoencoders","created":"2024-03-01","cited_by_count":3,"area":null},{"id":"2411.06140","title":"Deep Nonparametric Conditional Independence Tests for Images","created":"2024-11-09","cited_by_count":3,"area":null},{"id":"2105.00393","title":"Directional FDR Control for Sub-Gaussian Sparse GLMs","created":"2021-05-02","cited_by_count":2,"area":null},{"id":"2110.10713","title":"PPFS: Predictive Permutation Feature Selection","created":"2021-10-20","cited_by_count":2,"area":null},{"id":"2111.02802","title":"Distributed Sparse Feature Selection in Communication-Restricted Networks","created":"2021-11-02","cited_by_count":2,"area":null},{"id":"2205.14613","title":"A Conditional Randomization Test for Sparse Logistic Regression in High-Dimension","created":"2022-05-29","cited_by_count":2,"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":"2303.04301","title":"Optimal Sparse Recovery with Decision Stumps","created":"2023-03-08","cited_by_count":2,"area":null},{"id":"2305.19482","title":"Adaptive False Discovery Rate Control with Privacy Guarantee","created":"2023-05-31","cited_by_count":2,"area":null},{"id":"2306.05679","title":"Bayes optimal learning in high-dimensional linear regression with network side information","created":"2023-06-09","cited_by_count":2,"area":null},{"id":"2307.14825","title":"Simplified Concrete Dropout -- Improving the Generation of Attribution Masks for Fine-grained Classification","created":"2023-07-27","cited_by_count":2,"area":null},{"id":"2308.00549","title":"Copula for Instance-wise Feature Selection and Ranking","created":"2023-08-01","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":"2409.19088","title":"Solving FDR-Controlled Sparse Regression Problems with Five Million Variables on a Laptop","created":"2024-09-27","cited_by_count":2,"area":null},{"id":"2508.07066","title":"Membership Inference Attacks with False Discovery Rate Control","created":"2025-08-09","cited_by_count":2,"area":"safety"},{"id":"2511.11711","title":"Which Sparse Autoencoder Features Are Real? Model-X Knockoffs for False Discovery Rate Control","created":"2025-11-12","cited_by_count":2,"area":"mechinterp"},{"id":"2605.26631","title":"Data-driven sparse identification of governing PDEs via knockoff filters and multi-criteria trade-offs","created":"2026-05-26","cited_by_count":2,"area":null},{"id":"2106.02590","title":"Spatially relaxed inference on high-dimensional linear models","created":"2021-06-04","cited_by_count":1,"area":null},{"id":"2108.05350","title":"Controlling the False Split Rate in Tree-Based Aggregation","created":"2021-08-11","cited_by_count":1,"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":"2202.12989","title":"Flexible variable selection in the presence of missing data","created":"2022-02-25","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":"2203.14504","title":"Black-box Selective Inference via Bootstrapping","created":"2022-03-28","cited_by_count":1,"area":null},{"id":"2207.01822","title":"Ensemble feature selection with data-driven thresholding for Alzheimer's disease biomarker discovery","created":"2022-07-05","cited_by_count":1,"area":null},{"id":"2208.08230","title":"Two-Stage Robust and Sparse Distributed Statistical Inference for Large-Scale Data","created":"2022-08-17","cited_by_count":1,"area":null},{"id":"2209.02064","title":"GRASP: A Goodness-of-Fit Test for Classification Learning","created":"2022-09-05","cited_by_count":1,"area":null},{"id":"2301.00545","title":"Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels","created":"2023-01-02","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":"2401.08386","title":"Deep Learning-based Group Causal Inference in Multivariate Time-series","created":"2024-01-16","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":"2405.03063","title":"Stability of a Generalized Debiased Lasso with Applications to Resampling-Based Variable Selection","created":"2024-05-05","cited_by_count":1,"area":null},{"id":"2405.08975","title":"A distribution-free valid p-value for finite samples of bounded random variables","created":"2024-05-14","cited_by_count":1,"area":null},{"id":"2410.05211","title":"The Informed Elastic Net for Fast Grouped Variable Selection and FDR Control in Genomics Research","created":"2024-10-07","cited_by_count":1,"area":null},{"id":"2411.18575","title":"Functional relevance based on the continuous Shapley value","created":"2024-11-27","cited_by_count":1,"area":null},{"id":"2501.17889","title":"Knoop: Practical Enhancement of Knockoff with Over-Parameterization for Variable Selection","created":"2025-01-28","cited_by_count":1,"area":null},{"id":"2510.25531","title":"Using latent representations to link disjoint longitudinal data for mixed-effects regression","created":"2025-10-29","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":"2602.16568","title":"Separating Oblivious and Adaptive Models of Variable Selection","created":"2026-02-18","cited_by_count":1,"area":null},{"id":"2603.06609","title":"Valid Feature-Level Inference for Tabular Foundation Models via the Conditional Randomization Test","created":"2026-02-19","cited_by_count":1,"area":null},{"id":"2605.17559","title":"Controlling False Discovery in Arbitrarily Structured Hypothesis Spaces via Reproducing Kernels","created":"2026-05-17","cited_by_count":1,"area":null},{"id":"2606.09046","title":"Measurement Under Selection: Decoy-Calibrated Failure Audits for Language Models","created":"2026-06-08","cited_by_count":1,"area":null},{"id":"2103.11327","title":"Comments on Leo Breiman's paper 'Statistical Modeling: The Two Cultures' (Statistical Science, 2001, 16(3), 199-231)","created":"2021-03-21","cited_by_count":0,"area":null},{"id":"2106.15812","title":"AdaPT-GMM: Powerful and robust covariate-assisted multiple testing","created":"2021-06-30","cited_by_count":0,"area":null},{"id":"2202.05612","title":"High-dimensional Inference and FDR Control for Simulated Markov Random Fields","created":"2022-02-11","cited_by_count":0,"area":null},{"id":"2202.13054","title":"Extending Model-x Framework to Missing Data","created":"2022-02-26","cited_by_count":0,"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":"2206.05391","title":"Feature Selection using e-values","created":"2022-06-11","cited_by_count":0,"area":null},{"id":"2310.04919","title":"The Conditional Prediction Function: A Novel Technique to Control False Discovery Rate for Complex Models","created":"2023-10-07","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":"2411.08821","title":"Conditional Local Importance by Quantile Expectations","created":"2024-11-13","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":"2503.13304","title":"AutoNFS: Automatic Neural Feature Selection","created":"2025-03-17","cited_by_count":0,"area":null},{"id":"2503.18048","title":"Interpretable Feature Interaction via Statistical Self-supervised Learning on Tabular Data","created":"2025-03-23","cited_by_count":0,"area":null},{"id":"2505.24727","title":"Knockoff-Guided Compressive Sensing: A Statistical Machine Learning Framework for Support-Assured Signal Recovery","created":"2025-05-30","cited_by_count":0,"area":null},{"id":"2507.04187","title":"Where to Intervene: Action Selection in Deep Reinforcement Learning","created":"2025-07-05","cited_by_count":0,"area":"deeprl"},{"id":"2508.13890","title":"Diffusion-Driven High-Dimensional Variable Selection","created":"2025-08-19","cited_by_count":0,"area":"diffusion"},{"id":"2508.14268","title":"Comparing Model-agnostic Feature Selection Methods through Relative Efficiency","created":"2025-08-19","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":"2510.00875","title":"False Discovery Rate Control via Bayesian Mirror Statistic","created":"2025-10-01","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":"2510.23906","title":"Group Interventions on Deep Networks for Causal Discovery in Subsystems","created":"2025-10-27","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":"2602.05798","title":"Learning False Discovery Rate Control via Model-Based Neural Networks","created":"2026-02-05","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":"2604.07464","title":"Virtual Dummies: Enabling Scalable FDR-Controlled Variable Selection via Sequential Sampling of Null Features","created":"2026-04-08","cited_by_count":0,"area":null},{"id":"2604.11253","title":"Trustworthy Feature Importance Avoids Unrestricted Permutations","created":"2026-04-13","cited_by_count":0,"area":null},{"id":"2606.00402","title":"A Distribution-Free Framework for Rewrite-Based Human-text Detection via Knockoff Filtering","created":"2026-05-29","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":"2606.22336","title":"Null-Calibrated Conformal Selection via Target-Membership Scores","created":"2026-06-21","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},{"id":"2609.19511","title":"Null importance: Disentangling relevance for interpretable machine learning","created":"2026-09-16","cited_by_count":0,"area":null},{"id":"2609.33549","title":"Calibrated Derivative-Process Sensitivity for Gaussian-Process Variable Selection","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.38979","title":"Mitigating Object Hallucination in Large Vision-Language Models via False Discovery Controlled Visual Data Splitting","created":"2026-09-30","cited_by_count":0,"area":"mllm"}],"next_cursor":null}