{"total":64,"rows":[{"id":"2111.12981","title":"Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanism","created":"2021-11-25","cited_by_count":50,"area":null},{"id":"2212.05015","title":"Robustness Implies Privacy in Statistical Estimation","created":"2022-12-09","cited_by_count":47,"area":null},{"id":"2112.03548","title":"Private Robust Estimation by Stabilizing Convex Relaxations","created":"2021-12-07","cited_by_count":38,"area":null},{"id":"2205.08532","title":"New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma","created":"2022-05-17","cited_by_count":33,"area":null},{"id":"2208.07984","title":"Private Estimation with Public Data","created":"2022-08-16","cited_by_count":32,"area":null},{"id":"2302.01855","title":"From Robustness to Privacy and Back","created":"2023-02-03","cited_by_count":27,"area":null},{"id":"2211.00724","title":"Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation","created":"2022-11-01","cited_by_count":25,"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":"2302.04787","title":"On the Privacy-Robustness-Utility Trilemma in Distributed Learning","created":"2023-02-09","cited_by_count":24,"area":null},{"id":"2301.12250","title":"Fast, Sample-Efficient, Affine-Invariant Private Mean and Covariance Estimation for Subgaussian Distributions","created":"2023-01-28","cited_by_count":22,"area":null},{"id":"2301.04822","title":"Private estimation algorithms for stochastic block models and mixture models","created":"2023-01-11","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":"2303.04288","title":"Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models","created":"2023-03-07","cited_by_count":19,"area":null},{"id":"2310.06289","title":"Better and Simpler Lower Bounds for Differentially Private Statistical Estimation","created":"2023-10-10","cited_by_count":17,"area":null},{"id":"2205.14324","title":"Differentially Private Covariance Revisited","created":"2022-05-28","cited_by_count":15,"area":null},{"id":"2308.06239","title":"Private Distribution Learning with Public Data: The View from Sample Compression","created":"2023-08-11","cited_by_count":14,"area":null},{"id":"2301.13334","title":"A Bias-Accuracy-Privacy Trilemma for Statistical Estimation","created":"2023-01-30","cited_by_count":12,"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":"2301.07078","title":"A Fast Algorithm for Adaptive Private Mean Estimation","created":"2023-01-17","cited_by_count":11,"area":null},{"id":"2405.13453","title":"A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy","created":"2024-05-22","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":"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":"2404.17714","title":"Lower Bounds for Private Estimation of Gaussian Covariance Matrices under All Reasonable Parameter Regimes","created":"2024-04-26","cited_by_count":9,"area":null},{"id":"2211.13403","title":"Differentially Private Image Classification from Features","created":"2022-11-24","cited_by_count":7,"area":null},{"id":"2403.03905","title":"Black-Box $k$-to-$1$-PCA Reductions: Theory and Applications","created":"2024-03-06","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":"2406.13433","title":"Certification for Differentially Private Prediction in Gradient-Based Training","created":"2024-06-19","cited_by_count":7,"area":null},{"id":"2207.04686","title":"(Nearly) Optimal Private Linear Regression via Adaptive Clipping","created":"2022-07-11","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":"2411.00775","title":"Dimension-free Private Mean Estimation for Anisotropic Distributions","created":"2024-11-01","cited_by_count":6,"area":null},{"id":"2202.06825","title":"Robust Estimation of Discrete Distributions under Local Differential Privacy","created":"2022-02-14","cited_by_count":5,"area":null},{"id":"2304.06787","title":"A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions","created":"2023-04-13","cited_by_count":5,"area":null},{"id":"2307.10999","title":"Private Federated Learning with Autotuned Compression","created":"2023-07-20","cited_by_count":5,"area":null},{"id":"2309.03847","title":"Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples","created":"2023-09-07","cited_by_count":5,"area":null},{"id":"2209.07716","title":"Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning","created":"2022-09-16","cited_by_count":4,"area":null},{"id":"2305.17148","title":"Differentially Private Low-dimensional Synthetic Data from High-dimensional Datasets","created":"2023-05-26","cited_by_count":4,"area":null},{"id":"2406.17814","title":"Distribution Learnability and Robustness","created":"2024-06-25","cited_by_count":4,"area":null},{"id":"2312.07792","title":"Differentially private projection-depth-based medians","created":"2023-12-12","cited_by_count":3,"area":null},{"id":"2402.00267","title":"Not All Learnable Distribution Classes are Privately Learnable","created":"2024-02-01","cited_by_count":3,"area":null},{"id":"2508.10879","title":"An Iterative Algorithm for Differentially Private $k$-PCA with Adaptive Noise","created":"2025-08-14","cited_by_count":3,"area":null},{"id":"2210.06459","title":"Differentially private multivariate medians","created":"2022-10-12","cited_by_count":2,"area":null},{"id":"2405.16663","title":"Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust","created":"2024-05-26","cited_by_count":2,"area":null},{"id":"2407.04783","title":"Agnostic Private Density Estimation for GMMs via List Global Stability","created":"2024-07-05","cited_by_count":2,"area":null},{"id":"2412.02670","title":"The Broader Landscape of Robustness in Algorithmic Statistics","created":"2024-12-03","cited_by_count":2,"area":null},{"id":"2502.18698","title":"Tukey Depth Mechanisms for Practical Private Mean Estimation","created":"2025-02-25","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":"2602.11513","title":"Differentially Private and Communication Efficient Large Language Model Split Inference via Stochastic Quantization and Soft Prompt","created":"2026-02-12","cited_by_count":2,"area":null},{"id":"2303.10085","title":"Robust probabilistic inference via a constrained transport metric","created":"2023-03-17","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":"2501.14095","title":"Improved subsample-and-aggregate via the private modified winsorized mean","created":"2025-01-23","cited_by_count":1,"area":null},{"id":"2503.01766","title":"Optimal Differentially Private Sampling of Unbounded Gaussians","created":"2025-03-03","cited_by_count":1,"area":null},{"id":"2510.00322","title":"Privately Estimating Black-Box Statistics","created":"2025-09-30","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":"2603.19703","title":"Minimax and Adaptive Covariance Matrix Estimation under Differential Privacy","created":"2026-03-20","cited_by_count":1,"area":null},{"id":"2609.26508","title":"Gap-Free Streaming PCA Beyond Rank-One Updates: Near-Optimal Rates and Applications to Differential Privacy","created":"2026-09-22","cited_by_count":1,"area":null},{"id":"2212.08018","title":"Privately Estimating a Gaussian: Efficient, Robust and Optimal","created":"2022-12-15","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":"2311.14681","title":"Instance-Specific Asymmetric Sensitivity in Differential Privacy","created":"2023-11-02","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":"2606.12654","title":"Computationally tractable robust differentially private mean estimation","created":"2026-06-10","cited_by_count":0,"area":null},{"id":"2609.31614","title":"Gap-free Differentially Private PCA for Gaussian Data","created":"2026-09-25","cited_by_count":0,"area":null}],"next_cursor":null}