{"total":60,"rows":[{"id":"2102.09159","title":"Robust and Differentially Private Mean Estimation","created":"2021-02-18","cited_by_count":66,"area":null},{"id":"2111.06578","title":"Differential privacy and robust statistics in high dimensions","created":"2021-11-12","cited_by_count":64,"area":null},{"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":"2106.01336","title":"Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data","created":"2021-06-02","cited_by_count":47,"area":null},{"id":"2212.05015","title":"Robustness Implies Privacy in Statistical Estimation","created":"2022-12-09","cited_by_count":47,"area":null},{"id":"2106.13329","title":"Covariance-Aware Private Mean Estimation Without Private Covariance Estimation","created":"2021-06-24","cited_by_count":46,"area":null},{"id":"2111.11320","title":"Private and polynomial time algorithms for learning Gaussians and beyond","created":"2021-11-22","cited_by_count":39,"area":null},{"id":"2112.03548","title":"Private Robust Estimation by Stabilizing Convex Relaxations","created":"2021-12-07","cited_by_count":38,"area":null},{"id":"2111.04609","title":"A Private and Computationally-Efficient Estimator for Unbounded Gaussians","created":"2021-11-08","cited_by_count":34,"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":"2303.04288","title":"Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models","created":"2023-03-07","cited_by_count":19,"area":null},{"id":"2112.14445","title":"Differentially-Private Clustering of Easy Instances","created":"2021-12-29","cited_by_count":17,"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":"2303.01256","title":"Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance","created":"2023-03-02","cited_by_count":16,"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":"2106.02162","title":"Privately Learning Mixtures of Axis-Aligned Gaussians","created":"2021-06-03","cited_by_count":13,"area":null},{"id":"2203.01537","title":"Private High-Dimensional Hypothesis Testing","created":"2022-03-03","cited_by_count":12,"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":"2309.12500","title":"User-Level Differential Privacy With Few Examples Per User","created":"2023-09-21","cited_by_count":12,"area":null},{"id":"2301.03566","title":"Simple Binary Hypothesis Testing under Local Differential Privacy and Communication Constraints","created":"2023-01-09","cited_by_count":10,"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":"2211.08193","title":"Differentially Private Sampling from Distributions","created":"2022-11-15","cited_by_count":8,"area":null},{"id":"2403.16981","title":"The Sample Complexity of Simple Binary Hypothesis Testing","created":"2024-03-25","cited_by_count":8,"area":null},{"id":"2108.07880","title":"Statistically Near-Optimal Hypothesis Selection","created":"2021-08-17","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":"2210.02215","title":"On the Statistical Complexity of Estimation and Testing under Privacy Constraints","created":"2022-10-05","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":"2212.09980","title":"Continual Mean Estimation Under User-Level Privacy","created":"2022-12-20","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":"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":"2403.17868","title":"An invitation to the sample complexity of quantum hypothesis testing","created":"2024-03-26","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":"2109.01064","title":"Lower Bounds on the Total Variation Distance Between Mixtures of Two Gaussians","created":"2021-09-02","cited_by_count":4,"area":null},{"id":"2306.05734","title":"DP-HyPO: An Adaptive Private Hyperparameter Optimization Framework","created":"2023-06-09","cited_by_count":4,"area":null},{"id":"2306.11312","title":"Data Structures for Density Estimation","created":"2023-06-20","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":"2312.05645","title":"Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity","created":"2023-12-09","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":"2210.06459","title":"Differentially private multivariate medians","created":"2022-10-12","cited_by_count":2,"area":null},{"id":"2212.00135","title":"Answering Private Linear Queries Adaptively using the Common Mechanism","created":"2022-11-30","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":"2410.23087","title":"Statistical-Computational Trade-offs for Density Estimation","created":"2024-10-30","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":"2502.01701","title":"Learning with Differentially Private (Sliced) Wasserstein Gradients","created":"2025-02-03","cited_by_count":2,"area":null},{"id":"2206.04713","title":"A Resilient Distributed Boosting Algorithm","created":"2022-06-09","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":"2502.14309","title":"On Theoretical Limits of Learning with Label Differential Privacy","created":"2025-02-20","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":"2505.20189","title":"Private Geometric Median in Nearly-Linear Time","created":"2025-05-26","cited_by_count":1,"area":null},{"id":"2206.10685","title":"Differentially Private Maximal Information Coefficients","created":"2022-06-21","cited_by_count":0,"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":"2311.14681","title":"Instance-Specific Asymmetric Sensitivity in Differential Privacy","created":"2023-11-02","cited_by_count":0,"area":null},{"id":"2411.02298","title":"Sample-Efficient Private Learning of Mixtures of Gaussians","created":"2024-11-04","cited_by_count":0,"area":null},{"id":"2506.01162","title":"Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor","created":"2025-06-01","cited_by_count":0,"area":null},{"id":"2509.16180","title":"Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graph","created":"2025-09-19","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}],"next_cursor":null}