DIFFERENTIALLY PRIVATE ANONYMIZED HISTOGRAMS

von Suresh, Ananda Theertha
Veröffentlicht in NeurIPS (33. : 2019 : Vancouver, British Columbia) 32nd Conference on Neural Information Processing Systems (NeurIPS 2019) ; Volume 10 of 20 (2020)

Differentially Private Learning with Margin Guarantees

von Bassily, Raef
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 42 of 50 (2023)

RELATIVE DEVIATION MARGIN BOUNDS

von CORTES, CORINNA
Veröffentlicht in International Conference on Machine Learning (38. : 2021 : Online) International Conference on Machine Learning (ICML 2021 ; Part 3 of 16 (2022)

Remember What You Want to Forget: Algorithms for Machine Unlearning

von Sekhari, Ayush
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 22 of 36 (2022)

Boosting with Multiple Sources

von Cortes, Corinna
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 21 of 36 (2022)

Correlated Quantization for Distributed Mean Estimation and Optimization

von Suresh, Ananda Theertha
Veröffentlicht in International Conference on Machine Learning (39. : 2022 : Baltimore, Md.; Online) International Conference on Machine Learning (ICML 2022) ; Part 35 of 33 (2023)

A DISCRIMINATIVE TECHNIQUE FOR MULTIPLE-SOURCE ADAPTATION

von CORTES, CORINNA
Veröffentlicht in International Conference on Machine Learning (38. : 2021 : Online) International Conference on Machine Learning (ICML 2021 ; Part 3 of 16 (2022)

COMMUNICATION-EFFICIENT AGNOSTIC FEDERATED AVERAGING

von Ro, Jae
Veröffentlicht in INTERSPEECH (22. : 2021 : Brünn; Online) 22nd Annual Conference of the International Speech Communication Association (INTERSPEECH 2021) ; Volume 3 of 6 (2021)

The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning

von Chen, Wei-Ning
Veröffentlicht in International Conference on Machine Learning (39. : 2022 : Baltimore, Md.; Online) International Conference on Machine Learning (ICML 2022) ; Part 4 of 33 (2023)

LEARNING DISCRETE DISTRIBUTIONS: USER VS ITEM-LEVEL PRIVACY

von Liu, Yuhan
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 25 of 27 (2021)

SAMPLED SOFTMAX WITH RANDOM FOURIER FEATURES

von Rawat, Ankit Singh
Veröffentlicht in NeurIPS (33. : 2019 : Vancouver, British Columbia) 32nd Conference on Neural Information Processing Systems (NeurIPS 2019) ; Volume 18 of 20 (2020)

Learning with User-Level Privacy

von Levy, Daniel
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 15 of 36 (2022)

Breaking the centralized barrier for cross-device federated learning

von Karimireddy, Sai Praneeth
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 34 of 36 (2022)