private learning

Privately Learning Decision Lists and a Differentially Private Winnow ALT 2026 pfl-research: simulation framework for accelerating research in Private Federated Learning NeurIPS 2024 All Rivers Run to the Sea: Private Learning with Asymmetric Flows CVPR 2024 Not All Learnable Distribution Classes are Privately Learnable ALT 2024 Oracle-Efficient Differentially Private Learning with Public Data NeurIPS 2024 Private Learning with Public Features AISTATS 2024 Private PAC Learning May be Harder than Online Learning ALT 2024 Differentially Private Sharpness-Aware Training ICML 2023 DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning NeurIPS 2023 Large-Scale Distributed Learning via Private On-Device LSH NeurIPS 2023 User-Level Differential Privacy With Few Examples Per User NeurIPS 2023 Differentially Private Learning with Margin Guarantees NeurIPS 2022 DP-PCA: Statistically Optimal and Differentially Private PCA NeurIPS 2022 Open Problem: Better Differentially Private Learning Algorithms with Margin Guarantees COLT 2022 Open Problem: Do you pay for Privacy in Online learning? COLT 2022 Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning ICLR 2021 On the Sample Complexity of Privately Learning Axis-Aligned Rectangles NeurIPS 2021 Privately Learning Mixtures of Axis-Aligned Gaussians NeurIPS 2021 Reconstruction Attack on Instance Encoding for Language Understanding EMNLP 2021 Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning AISTATS 2021 A Computational Separation between Private Learning and Online Learning NeurIPS 2020 Closure Properties for Private Classification and Online Prediction COLT 2020 On the Equivalence between Online and Private Learnability beyond Binary Classification NeurIPS 2020 Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity NeurIPS 2020 Privately Learning Thresholds: Closing the Exponential Gap COLT 2020