user-level privacy

Distribution-Aware Mean Estimation under User-level Local Differential Privacy AISTATS 2025 A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy NeurIPS 2024 Faster Algorithms for User-Level Private Stochastic Convex Optimization NeurIPS 2024 Improved Bounds for Pure Private Agnostic Learning: Item-Level and User-Level Privacy ICML 2024 User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates AISTATS 2024 Algorithms for bounding contribution for histogram estimation under user-level privacy ICML 2023 Discrete Distribution Estimation under User-level Local Differential Privacy AISTATS 2023 Federated Linear Contextual Bandits with User-level Differential Privacy ICML 2023 On User-Level Private Convex Optimization ICML 2023 User-level Private Stochastic Convex Optimization with Optimal Rates ICML 2023 Differentially Private Federated Learning With Local Regularization and Sparsification CVPR 2022 Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams NeurIPS 2022 Mean Estimation with User-level Privacy under Data Heterogeneity NeurIPS 2022 Tight and Robust Private Mean Estimation with Few Users ICML 2022 Differentially Private Model Personalization NeurIPS 2021 Differentially Private n-gram Extraction NeurIPS 2021 Learning with User-Level Privacy NeurIPS 2021 Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates ICML 2021 User-Level Differentially Private Learning via Correlated Sampling NeurIPS 2021 Learning discrete distributions: user vs item-level privacy NeurIPS 2020 Learning Differentially Private Recurrent Language Models ICLR 2018