{"total":35,"rows":[{"id":"2103.11003","title":"Differentially private inference via noisy optimization","created":"2021-03-19","cited_by_count":27,"area":null},{"id":"2307.03848","title":"Optimal Learners for Realizable Regression: PAC Learning and Online Learning","created":"2023-07-07","cited_by_count":23,"area":null},{"id":"2102.06202","title":"Private Prediction Sets","created":"2021-02-11","cited_by_count":17,"area":null},{"id":"2402.13531","title":"Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation","created":"2024-02-21","cited_by_count":12,"area":null},{"id":"2103.00564","title":"An Introduction to Johnson-Lindenstrauss Transforms","created":"2021-02-28","cited_by_count":11,"area":null},{"id":"2202.11199","title":"Differentially Private Regression with Unbounded Covariates","created":"2022-02-19","cited_by_count":10,"area":null},{"id":"2210.06140","title":"Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies","created":"2022-10-12","cited_by_count":10,"area":null},{"id":"2302.04260","title":"The Test of Tests: A Framework For Differentially Private Hypothesis Testing","created":"2023-02-08","cited_by_count":9,"area":null},{"id":"2404.15409","title":"Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares","created":"2024-04-23","cited_by_count":7,"area":null},{"id":"2303.03451","title":"Improved Differentially Private Regression via Gradient Boosting","created":"2023-03-06","cited_by_count":6,"area":null},{"id":"2104.10740","title":"Robust Testing and Estimation under Manipulation Attacks","created":"2021-04-21","cited_by_count":5,"area":null},{"id":"2402.07131","title":"Resampling methods for private statistical inference","created":"2024-02-11","cited_by_count":4,"area":null},{"id":"2405.15002","title":"Private Regression via Data-Dependent Sufficient Statistic Perturbation","created":"2024-05-23","cited_by_count":4,"area":null},{"id":"2206.07234","title":"Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints","created":"2022-06-15","cited_by_count":3,"area":null},{"id":"2503.24321","title":"Sample-Optimal Private Regression in Polynomial Time","created":"2025-03-31","cited_by_count":2,"area":null},{"id":"2505.21641","title":"PrivATE: Differentially Private Confidence Intervals for Average Treatment Effects","created":"2025-05-27","cited_by_count":2,"area":null},{"id":"2505.24603","title":"The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches","created":"2025-05-30","cited_by_count":2,"area":null},{"id":"2507.20560","title":"Statistical Inference for Differentially Private Stochastic Gradient Descent","created":"2025-07-28","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":"2310.10092","title":"Label Differential Privacy via Aggregation","created":"2023-10-16","cited_by_count":1,"area":null},{"id":"2505.12541","title":"Private Statistical Estimation via Truncation","created":"2025-05-18","cited_by_count":1,"area":null},{"id":"2508.05212","title":"High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference","created":"2025-08-07","cited_by_count":1,"area":null},{"id":"2510.16974","title":"Differentially Private Linear Regression and Synthetic Data Generation with Statistical Guarantees","created":"2025-10-19","cited_by_count":1,"area":null},{"id":"2601.07545","title":"Near-Optimal Private Linear Regression via Iterative Hessian Mixing","created":"2026-01-12","cited_by_count":1,"area":null},{"id":"2202.02448","title":"Linear Model Against Malicious Adversaries with Local Differential Privacy","created":"2022-02-05","cited_by_count":0,"area":null},{"id":"2206.07998","title":"Differentially Private Multi-Party Data Release for Linear Regression","created":"2022-06-16","cited_by_count":0,"area":null},{"id":"2309.00008","title":"Large-Scale Public Data Improves Differentially Private Image Generation Quality","created":"2023-08-04","cited_by_count":0,"area":null},{"id":"2309.01243","title":"The Normal Distributions Indistinguishability Spectrum and its Application to Privacy-Preserving Machine Learning","created":"2023-09-03","cited_by_count":0,"area":null},{"id":"2504.11304","title":"Differentially Private Geodesic Regression","created":"2025-04-15","cited_by_count":0,"area":null},{"id":"2508.18037","title":"Enhancing Differentially Private Linear Regression via Public Second-Moment","created":"2025-08-25","cited_by_count":0,"area":null},{"id":"2509.22794","title":"Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression","created":"2025-09-26","cited_by_count":0,"area":null},{"id":"2511.07365","title":"Private Sketches for Linear Regression","created":"2025-11-10","cited_by_count":0,"area":null},{"id":"2602.22282","title":"Differentially Private Truncation of Unbounded Data via Public Second Moments","created":"2026-02-25","cited_by_count":0,"area":null},{"id":"2605.24295","title":"Private Adaptive Covariance Estimation via Gaussian Graphical Models","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2605.30600","title":"The Fast Mixing Mechanism for Differential Privacy","created":"2026-05-28","cited_by_count":0,"area":null}],"next_cursor":null}