{"total":61,"rows":[{"id":"2111.06578","title":"Differential privacy and robust statistics in high dimensions","created":"2021-11-12","cited_by_count":64,"area":null},{"id":"2203.00263","title":"Private Convex Optimization via Exponential Mechanism","created":"2022-03-01","cited_by_count":49,"area":null},{"id":"2103.11003","title":"Differentially private inference via noisy optimization","created":"2021-03-19","cited_by_count":27,"area":null},{"id":"2205.13709","title":"DP-PCA: Statistically Optimal and Differentially Private PCA","created":"2022-05-27","cited_by_count":24,"area":null},{"id":"2303.07152","title":"Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning","created":"2023-03-13","cited_by_count":21,"area":null},{"id":"2208.07353","title":"Easy Differentially Private Linear Regression","created":"2022-08-15","cited_by_count":19,"area":null},{"id":"2205.03014","title":"Differentially Private Generalized Linear Models Revisited","created":"2022-05-06","cited_by_count":17,"area":null},{"id":"2301.00457","title":"ReSQueing Parallel and Private Stochastic Convex Optimization","created":"2023-01-01","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":"2202.11199","title":"Differentially Private Regression with Unbounded Covariates","created":"2022-02-19","cited_by_count":10,"area":null},{"id":"2410.12085","title":"Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning","created":"2024-10-15","cited_by_count":10,"area":null},{"id":"2301.13273","title":"Near Optimal Private and Robust Linear Regression","created":"2023-01-30","cited_by_count":9,"area":null},{"id":"2405.08920","title":"Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning","created":"2024-05-14","cited_by_count":8,"area":null},{"id":"2106.10333","title":"Non-parametric Differentially Private Confidence Intervals for the Median","created":"2021-06-18","cited_by_count":7,"area":null},{"id":"2211.13403","title":"Differentially Private Image Classification from Features","created":"2022-11-24","cited_by_count":7,"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":"2107.08461","title":"Differentially Private Bayesian Neural Networks on Accuracy, Privacy and Reliability","created":"2021-07-18","cited_by_count":6,"area":null},{"id":"2301.00301","title":"Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy","created":"2022-12-31","cited_by_count":6,"area":null},{"id":"2303.03451","title":"Improved Differentially Private Regression via Gradient Boosting","created":"2023-03-06","cited_by_count":6,"area":null},{"id":"2310.15454","title":"Private Learning with Public Features","created":"2023-10-24","cited_by_count":6,"area":null},{"id":"2312.05659","title":"Optimal Unbiased Randomizers for Regression with Label Differential Privacy","created":"2023-12-09","cited_by_count":6,"area":null},{"id":"2306.00920","title":"Better Private Linear Regression Through Better Private Feature Selection","created":"2023-06-01","cited_by_count":5,"area":null},{"id":"2405.04034","title":"Differentially Private Post-Processing for Fair Regression","created":"2024-05-07","cited_by_count":5,"area":null},{"id":"2310.07367","title":"Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model","created":"2023-10-11","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":"2106.00474","title":"Gaussian Processes with Differential Privacy","created":"2021-06-01","cited_by_count":3,"area":null},{"id":"2111.12786","title":"Differentially Private Nonparametric Regression Under a Growth Condition","created":"2021-11-24","cited_by_count":3,"area":null},{"id":"2207.12602","title":"Differentially Private Estimation via Statistical Depth","created":"2022-07-26","cited_by_count":3,"area":null},{"id":"2302.07975","title":"Multi-Task Differential Privacy Under Distribution Skew","created":"2023-02-15","cited_by_count":3,"area":null},{"id":"2309.11516","title":"Private Matrix Factorization with Public Item Features","created":"2023-09-17","cited_by_count":3,"area":null},{"id":"2401.17127","title":"Personalized Differential Privacy for Ridge Regression","created":"2024-01-30","cited_by_count":3,"area":null},{"id":"2510.21060","title":"On the Sample Complexity of Differentially Private Policy Optimization","created":"2025-10-24","cited_by_count":3,"area":null},{"id":"2104.00245","title":"High-Dimensional Differentially-Private EM Algorithm: Methods and Near-Optimal Statistical Guarantees","created":"2021-04-01","cited_by_count":2,"area":null},{"id":"2402.06701","title":"Privacy Profiles for Private Selection","created":"2024-02-09","cited_by_count":2,"area":null},{"id":"2407.04884","title":"Convex Approximation of Two-Layer ReLU Networks for Hidden State Differential Privacy","created":"2024-07-05","cited_by_count":2,"area":null},{"id":"2503.21071","title":"Purifying Approximate Differential Privacy with Randomized Post-processing","created":"2025-03-27","cited_by_count":2,"area":null},{"id":"2503.24321","title":"Sample-Optimal Private Regression in Polynomial Time","created":"2025-03-31","cited_by_count":2,"area":null},{"id":"2505.24603","title":"The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches","created":"2025-05-30","cited_by_count":2,"area":null},{"id":"2301.13778","title":"Differentially Private Distributed Bayesian Linear Regression with MCMC","created":"2023-01-31","cited_by_count":1,"area":null},{"id":"2308.15250","title":"The Relative Gaussian Mechanism and its Application to Private Gradient Descent","created":"2023-08-29","cited_by_count":1,"area":null},{"id":"2310.07852","title":"On the Computational Complexity of Private High-dimensional Model Selection","created":"2023-10-11","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":"2408.04313","title":"Better Locally Private Sparse Estimation Given Multiple Samples Per User","created":"2024-08-08","cited_by_count":1,"area":null},{"id":"2412.00497","title":"Distributed Differentially Private Data Analytics via Secure Sketching","created":"2024-11-30","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":"2511.01303","title":"Differentially Private Nonparametric Confidence Intervals Under Minimal Distributional Assumptions","created":"2025-11-03","cited_by_count":1,"area":null},{"id":"2512.01708","title":"Differentially Private and Federated Structure Learning in Bayesian Networks","created":"2025-12-01","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":"2303.04195","title":"PRIMO: Private Regression in Multiple Outcomes","created":"2023-03-07","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":"2403.15999","title":"Near-Optimal differentially private low-rank trace regression with guaranteed private initialization","created":"2024-03-24","cited_by_count":0,"area":null},{"id":"2412.02578","title":"Private Linear Regression with Differential Privacy and PAC Privacy","created":"2024-12-03","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":"2510.22062","title":"Differentially Private High-dimensional Variable Selection via Integer Programming","created":"2025-10-24","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},{"id":"2606.21107","title":"Enhancing Differentially Private Mechanisms via Empirical Bayes","created":"2026-06-19","cited_by_count":0,"area":null},{"id":"2607.29675","title":"Differentially Private Nonparametric Modal Learning with Applications to Regression and Clustering","created":"2026-07-31","cited_by_count":0,"area":null}],"next_cursor":null}