| Breaking the communication-privacy-accuracy trilemma WN Chen, P Kairouz, A Ozgur Advances in Neural Information Processing Systems 33, 3312-3324, 2020 | 128 | 2020 |
| The fundamental price of secure aggregation in differentially private federated learning WN Chen, CAC Choo, P Kairouz, AT Suresh International Conference on Machine Learning, 3056-3089, 2022 | 65* | 2022 |
| Fisher information under local differential privacy LP Barnes, WN Chen, A Özgür IEEE Journal on Selected Areas in Information Theory 1 (3), 645-659, 2020 | 53 | 2020 |
| The poisson binomial mechanism for unbiased federated learning with secure aggregation WN Chen, A Ozgur, P Kairouz International Conference on Machine Learning, 3490-3506, 2022 | 42 | 2022 |
| Adaptive group testing on networks with community structure S Ahn, WN Chen, A Özgür 2021 IEEE International Symposium on Information Theory (ISIT), 1242-1247, 2021 | 37 | 2021 |
| Optimal compression of locally differentially private mechanisms A Shah, WN Chen, J Balle, P Kairouz, L Theis International Conference on Artificial Intelligence and Statistics, 7680-7723, 2022 | 35 | 2022 |
| Differentially private federated learning: An information-theoretic perspective S Asoodeh, WN Chen, FP Calmon, A Özgür 2021 IEEE international symposium on information theory (ISIT), 344-349, 2021 | 28 | 2021 |
| Anonymous heterogeneous distributed detection: Optimal decision rules, error exponents, and the price of anonymity WN Chen, IH Wang IEEE Transactions on Information Theory 65 (11), 7390-7406, 2019 | 18 | 2019 |
| Privacy amplification via compression: Achieving the optimal privacy-accuracy-communication trade-off in distributed mean estimation WN Chen, D Song, A Ozgur, P Kairouz Advances in Neural Information Processing Systems 36, 2024 | 15 | 2024 |
| Breaking the dimension dependence in sparse distribution estimation under communication constraints WN Chen, P Kairouz, A Ozgur Conference on Learning Theory, 1028-1059, 2021 | 14 | 2021 |
| Exact optimality of communication-privacy-utility tradeoffs in distributed mean estimation B Isik, WN Chen, A Ozgur, T Weissman, A No Advances in Neural Information Processing Systems 36, 2024 | 11 | 2024 |
| Differentially private decoupled graph convolutions for multigranular topology protection E Chien, WN Chen, C Pan, P Li, A Ozgur, O Milenkovic Advances in Neural Information Processing Systems 36, 2024 | 6 | 2024 |
| Pointwise bounds for distribution estimation under communication constraints WN Chen, P Kairouz, A Ozgur Advances in Neural Information Processing Systems 34, 24593-24603, 2021 | 6 | 2021 |
| The communication cost of security and privacy in federated frequency estimation WN Chen, A Ozgur, G Cormode, A Bharadwaj International Conference on Artificial Intelligence and Statistics, 4247-4274, 2023 | 4 | 2023 |
| Adaptive group testing on networks with community structure: The stochastic block model S Ahn, WN Chen, A Özgür IEEE Transactions on Information Theory 69 (7), 4758-4776, 2023 | 4 | 2023 |
| On the fundamental limits of heterogeneous distributed detection: Price of anonymity WN Chen, HC Chen, IH Wang 2018 IEEE International Symposium on Information Theory (ISIT), 1056-1060, 2018 | 3 | 2018 |
| Partial data extraction via noisy histogram queries: Information theoretic bounds WN Chen, IH Wang 2017 IEEE International Symposium on Information Theory (ISIT), 2488-2492, 2017 | 3 | 2017 |
| Noisy adaptive group testing for community-oriented models S Ahn, WN Chen, A Özgür 2023 IEEE International Symposium on Information Theory (ISIT), 1621-1626, 2023 | 2 | 2023 |
| Estimating Sparse Distributions Under Joint Communication and Privacy Constraints S Ahn, WN Chen, A Özgür 2022 IEEE International Symposium on Information Theory (ISIT), 3144-3149, 2022 | 2 | 2022 |
| Federated Experiment Design under Distributed Differential Privacy WN Chen, G Cormode, A Bharadwaj, P Romov, A Ozgur International Conference on Artificial Intelligence and Statistics, 2458-2466, 2024 | 1 | 2024 |
Ayfer OzgurAssociate Professor of Electrical Engineering, Stanford UniversityVerified email at stanford.edu
