Ozgur, Ayfer

20 publications

ICML 2025 Leveraging Randomness in Model and Data Partitioning for Privacy Amplification Andy Dong, Wei-Ning Chen, Ayfer Ozgur

AISTATS 2024 Federated Experiment Design Under Distributed Differential Privacy Wei-Ning Chen, Graham Cormode, Akash Bharadwaj, Peter Romov, Ayfer Ozgur

JMLR 2024 Understanding Entropic Regularization in GANs Daria Reshetova, Yikun Bai, Xiugang Wu, Ayfer Özgür

NeurIPS 2024 Universal Exact Compression of Differentially Private Mechanisms Yanxiao Liu, Wei-Ning Chen, Ayfer Özgür, Cheuk Ting Li

NeurIPS 2023 Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection Eli Chien, Wei-Ning Chen, Chao Pan, Pan Li, Ayfer Ozgur, Olgica Milenkovic

ICMLW 2023 Exact Optimality in Communication-Privacy-Utility Tradeoffs Berivan Isik, Wei-Ning Chen, Ayfer Ozgur, Tsachy Weissman, Albert No

NeurIPS 2023 Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation Berivan Isik, Wei-Ning Chen, Ayfer Ozgur, Tsachy Weissman, Albert No

ICMLW 2023 Federated Experiment Design Under Distributed Differential Privacy Wei-Ning Chen, Graham Cormode, Akash Bharadwaj, Peter Romov, Ayfer Ozgur

ICMLW 2023 Local Differential Privacy with Entropic Wasserstein Distance Daria Reshetova, Wei-Ning Chen, Ayfer Ozgur

NeurIPS 2023 Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-Off in Distributed Mean Estimation Wei-Ning Chen, Dan Song, Ayfer Ozgur, Peter Kairouz

ICMLW 2023 Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-Off in Distributed Mean Estimation Wei-Ning Chen, Dan Song, Ayfer Ozgur, Peter Kairouz

AISTATS 2023 The Communication Cost of Security and Privacy in Federated Frequency Estimation Wei-Ning Chen, Ayfer Ozgur, Graham Cormode, Akash Bharadwaj

ICML 2022 The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation Wei-Ning Chen, Ayfer Ozgur, Peter Kairouz

FnTML 2021 Advances and Open Problems in Federated Learning Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao

NeurIPS 2021 Batched Thompson Sampling Cem Kalkanli, Ayfer Ozgur

COLT 2021 Breaking the Dimension Dependence in Sparse Distribution Estimation Under Communication Constraints Wei-Ning Chen, Peter Kairouz, Ayfer Ozgur

NeurIPS 2021 Pointwise Bounds for Distribution Estimation Under Communication Constraints Wei-Ning Chen, Peter Kairouz, Ayfer Ozgur

NeurIPS 2020 Breaking the Communication-Privacy-Accuracy Trilemma Wei-Ning Chen, Peter Kairouz, Ayfer Ozgur

JMLR 2020 Lower Bounds for Learning Distributions Under Communication Constraints via Fisher Information Leighton Pate Barnes, Yanjun Han, Ayfer Ozgur

COLT 2018 Geometric Lower Bounds for Distributed Parameter Estimation Under Communication Constraints Yanjun Han, Ayfer Özgür, Tsachy Weissman