conftrace_

private learning

28 papers

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Co-occurring keywords

differential privacy (1016) sample complexity (1169) pac learning (283) online learning (1777) vc dimension (108) total variation distance (52) neural network (6616) margin guarantee (3) approximate differential privacy (4) littlestone dimension (24)

Papers

Privately Learning Decision Lists and a Differentially Private Winnow ALT 2026
$\texttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning NIPS 2024
Private Learning with Public Features AISTATS 2024
All Rivers Run to the Sea: Private Learning with Asymmetric Flows CVPR 2024
Private PAC Learning May be Harder than Online Learning ALT 2024
Not All Learnable Distribution Classes are Privately Learnable ALT 2024
Oracle-Efficient Differentially Private Learning with Public Data NIPS 2024
Large-Scale Distributed Learning via Private On-Device LSH NIPS 2023
Differentially Private Sharpness-Aware Training ICML 2023
DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning NIPS 2023
User-Level Differential Privacy With Few Examples Per User NIPS 2023
Open Problem: Better Differentially Private Learning Algorithms with Margin Guarantees COLT 2022
Open Problem: Do you pay for Privacy in Online learning? COLT 2022
DP-PCA: Statistically Optimal and Differentially Private PCA NIPS 2022
Differentially Private Learning with Margin Guarantees NIPS 2022
Reconstruction Attack on Instance Encoding for Language Understanding EMNLP 2021
Privately Learning Mixtures of Axis-Aligned Gaussians NIPS 2021
On the Sample Complexity of Privately Learning Axis-Aligned Rectangles NIPS 2021
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning AISTATS 2021
A Computational Separation between Private Learning and Online Learning NIPS 2020
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity NIPS 2020
Synthetic Data Generators -- Sequential and Private NIPS 2020
Privately Learning Thresholds: Closing the Exponential Gap COLT 2020
On the Equivalence between Online and Private Learnability beyond Binary Classification NIPS 2020
Characterizing the Sample Complexity of Pure Private Learners JMLR 2019