{"total":58,"rows":[{"id":"2103.01278","title":"Non-Euclidean Differentially Private Stochastic Convex Optimization: Optimal Rates in Linear Time","created":"2021-03-01","cited_by_count":51,"area":null},{"id":"2201.08430","title":"Reproducibility in Learning","created":"2022-01-20","cited_by_count":44,"area":null},{"id":"2209.07400","title":"Private Synthetic Data for Multitask Learning and Marginal Queries","created":"2022-09-15","cited_by_count":35,"area":null},{"id":"2110.10132","title":"FriendlyCore: Practical Differentially Private Aggregation","created":"2021-10-19","cited_by_count":34,"area":null},{"id":"2210.08371","title":"Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability","created":"2022-10-15","cited_by_count":25,"area":null},{"id":"2102.08244","title":"Differentially Private Quantiles","created":"2021-02-16","cited_by_count":23,"area":null},{"id":"2202.11205","title":"Constant matters: Fine-grained Complexity of Differentially Private Continual Observation","created":"2022-02-23","cited_by_count":23,"area":null},{"id":"2211.06387","title":"\\~Optimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization","created":"2022-11-11","cited_by_count":18,"area":null},{"id":"2112.14445","title":"Differentially-Private Clustering of Easy Instances","created":"2021-12-29","cited_by_count":17,"area":null},{"id":"2110.05429","title":"Differentially Private Approximate Quantiles","created":"2021-10-11","cited_by_count":12,"area":null},{"id":"2403.00028","title":"Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries","created":"2024-02-28","cited_by_count":12,"area":null},{"id":"2106.13513","title":"Littlestone Classes are Privately Online Learnable","created":"2021-06-25","cited_by_count":10,"area":null},{"id":"2202.11199","title":"Differentially Private Regression with Unbounded Covariates","created":"2022-02-19","cited_by_count":10,"area":null},{"id":"2107.14527","title":"A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators","created":"2021-07-30","cited_by_count":9,"area":null},{"id":"2302.05707","title":"On Differential Privacy and Adaptive Data Analysis with Bounded Space","created":"2023-02-11","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":"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":"2204.10376","title":"Differentially Private Learning with Margin Guarantees","created":"2022-04-21","cited_by_count":7,"area":null},{"id":"2203.01440","title":"Near-Optimal Correlation Clustering with Privacy","created":"2022-03-02","cited_by_count":6,"area":null},{"id":"2305.01177","title":"Unbounded Differentially Private Quantile and Maximum Estimation","created":"2023-05-02","cited_by_count":6,"area":null},{"id":"2306.08745","title":"PLAN: Variance-Aware Private Mean Estimation","created":"2023-06-14","cited_by_count":6,"area":null},{"id":"2402.09483","title":"Oracle-Efficient Differentially Private Learning with Public Data","created":"2024-02-13","cited_by_count":6,"area":null},{"id":"2407.07765","title":"Ramsey Theorems for Trees and a General 'Private Learning Implies Online Learning' Theorem","created":"2024-07-10","cited_by_count":6,"area":null},{"id":"2202.13736","title":"On the Robustness of CountSketch to Adaptive Inputs","created":"2022-02-28","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":"2305.09579","title":"Private Everlasting Prediction","created":"2023-05-16","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":"2411.05483","title":"The Limits of Differential Privacy in Online Learning","created":"2024-11-08","cited_by_count":5,"area":null},{"id":"2105.10594","title":"Privacy Amplification Via Bernoulli Sampling","created":"2021-05-21","cited_by_count":4,"area":null},{"id":"2107.10870","title":"Multiclass versus Binary Differentially Private PAC Learning","created":"2021-07-22","cited_by_count":4,"area":null},{"id":"2209.04053","title":"Algorithms with More Granular Differential Privacy Guarantees","created":"2022-09-08","cited_by_count":4,"area":null},{"id":"2402.11119","title":"Private PAC Learning May be Harder than Online Learning","created":"2024-02-16","cited_by_count":4,"area":null},{"id":"2107.11526","title":"On the Sample Complexity of Privately Learning Axis-Aligned Rectangles","created":"2021-07-24","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":"2302.06943","title":"Private Statistical Estimation of Many Quantiles","created":"2023-02-14","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":"2406.07407","title":"Private Geometric Median","created":"2024-06-11","cited_by_count":3,"area":null},{"id":"2406.15916","title":"Credit Attribution and Stable Compression","created":"2024-06-22","cited_by_count":3,"area":null},{"id":"2510.00076","title":"Private Learning of Littlestone Classes, Revisited","created":"2025-09-30","cited_by_count":3,"area":null},{"id":"2604.07238","title":"On the Price of Privacy for Language Identification and Generation","created":"2026-04-08","cited_by_count":3,"area":null},{"id":"2305.13440","title":"Differentially Private Medians and Interior Points for Non-Pathological Data","created":"2023-05-22","cited_by_count":2,"area":null},{"id":"2401.04311","title":"Private Truly-Everlasting Robust-Prediction","created":"2024-01-09","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":"2505.06581","title":"An $\\tilde{O}$ptimal Differentially Private Learner for Concept Classes with VC Dimension 1","created":"2025-05-10","cited_by_count":2,"area":null},{"id":"2505.19589","title":"Model Agnostic Differentially Private Causal Inference","created":"2025-05-26","cited_by_count":2,"area":null},{"id":"2506.12856","title":"Private List Learnability vs. Online List Learnability","created":"2025-06-15","cited_by_count":2,"area":null},{"id":"2511.13999","title":"On the Gradient Complexity of Private Optimization with Private Oracles","created":"2025-11-17","cited_by_count":2,"area":null},{"id":"2106.12959","title":"Differentially Private Algorithms for Clustering with Stability Assumptions","created":"2021-06-11","cited_by_count":1,"area":null},{"id":"2312.08685","title":"Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions","created":"2023-12-14","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":"2510.00574","title":"Private Online Learning against an Adaptive Adversary: Realizable and Agnostic Settings","created":"2025-10-01","cited_by_count":1,"area":null},{"id":"2510.01291","title":"Private Realizable-to-Agnostic Transformation with Near-Optimal Sample Complexity","created":"2025-10-01","cited_by_count":1,"area":null},{"id":"2210.15175","title":"Private Isotonic Regression","created":"2022-10-27","cited_by_count":0,"area":null},{"id":"2212.04216","title":"Differentially-Private Bayes Consistency","created":"2022-12-08","cited_by_count":0,"area":null},{"id":"2301.09203","title":"Relaxed Models for Adversarial Streaming: The Advice Model and the Bounded Interruptions Model","created":"2023-01-22","cited_by_count":0,"area":null},{"id":"2602.05219","title":"Private Prediction via Shrinkage","created":"2026-02-05","cited_by_count":0,"area":null},{"id":"2605.22756","title":"Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees","created":"2026-05-21","cited_by_count":0,"area":null},{"id":"2610.05102","title":"Private Component-by-Component Learning","created":"2026-10-04","cited_by_count":0,"area":null}],"next_cursor":null}