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2020 – today
- 2024
[c11]Miguel Fuentes, Brett C. Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon:
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data. AISTATS 2024: 2404-2412- [i18]Miguel Fuentes, Brett Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon:
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data. CoRR abs/2403.07797 (2024) - [i17]Ryan McKenna:
Scaling up the Banded Matrix Factorization Mechanism for Differentially Private ML. CoRR abs/2405.15913 (2024) - [i16]Christopher Bian, Albert Cheu, Yannis Guzman, Marco Gruteser, Peter Kairouz, Ryan McKenna, Edo Roth:
Releasing Large-Scale Human Mobility Histograms with Differential Privacy. CoRR abs/2407.03496 (2024) - [i15]Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush:
Fine-Tuning Large Language Models with User-Level Differential Privacy. CoRR abs/2407.07737 (2024) - 2023
- [j9]Ryan McKenna, Gerome Miklau
, Michael Hay, Ashwin Machanavajjhala:
Optimizing Error of High-Dimensional Statistical Queries Under Differential Privacy. J. Priv. Confidentiality 13(1) (2023) - [c10]Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, John Rush, Abhradeep Guha Thakurta, Zheng Xu:
(Amplified) Banded Matrix Factorization: A unified approach to private training. NeurIPS 2023 - [c9]Anastasia Koloskova, Ryan McKenna, Zachary Charles, John Keith Rush, H. Brendan McMahan:
Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy. NeurIPS 2023 - [i14]Anastasia Koloskova, Ryan McKenna, Zachary Charles, Keith Rush, Brendan McMahan:
Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning. CoRR abs/2302.01463 (2023) - [i13]Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu:
(Amplified) Banded Matrix Factorization: A unified approach to private training. CoRR abs/2306.08153 (2023) - 2022
- [j8]Ryan McKenna, Brett Mullins, Daniel Sheldon, Gerome Miklau:
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. Proc. VLDB Endow. 15(11): 2599-2612 (2022) - [i12]Ryan McKenna, Brett Mullins, Daniel Sheldon, Gerome Miklau:
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. CoRR abs/2201.12677 (2022) - 2021
- [j7]Ryan McKenna, Gerome Miklau, Daniel Sheldon:
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data. J. Priv. Confidentiality 11(3) (2021) - [c8]Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau:
Relaxed Marginal Consistency for Differentially Private Query Answering. NeurIPS 2021: 20696-20707 - [i11]Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala:
HDMM: Optimizing error of high-dimensional statistical queries under differential privacy. CoRR abs/2106.12118 (2021) - [i10]Ryan McKenna, Gerome Miklau, Daniel Sheldon:
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data. CoRR abs/2108.04978 (2021) - [i9]Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau:
Relaxed Marginal Consistency for Differentially Private Query Answering. CoRR abs/2109.06153 (2021) - [i8]Yuchao Tao, Ryan McKenna, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
Benchmarking Differentially Private Synthetic Data Generation Algorithms. CoRR abs/2112.09238 (2021) - 2020
- [j6]Ryan McKenna, Raj Kumar Maity, Arya Mazumdar, Gerome Miklau:
A workload-adaptive mechanism for linear queries under local differential privacy. Proc. VLDB Endow. 13(11): 1905-1918 (2020) - [j5]Dan Zhang, Ryan McKenna, Ios Kotsogiannis, George Bissias, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
ϵKTELO: A Framework for Defining Differentially Private Computations. ACM Trans. Database Syst. 45(1): 2:1-2:44 (2020) - [c7]David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
Fair decision making using privacy-protected data. FAT* 2020: 189-199 - [c6]Ryan McKenna, Daniel Sheldon:
Permute-and-Flip: A new mechanism for differentially private selection. NeurIPS 2020 - [i7]Ryan McKenna, Raj Kumar Maity, Arya Mazumdar, Gerome Miklau:
A workload-adaptive mechanism for linear queries under local differential privacy. CoRR abs/2002.01582 (2020) - [i6]Ryan McKenna, Daniel Sheldon:
Permute-and-Flip: A new mechanism for differentially private selection. CoRR abs/2010.12603 (2020)
2010 – 2019
- 2019
- [j4]Zhiqi Huang, Ryan McKenna, George Bissias, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala:
PSynDB: Accurate and Accessible Private Data Generation. Proc. VLDB Endow. 12(12): 1918-1921 (2019) - [j3]Dan Zhang, Ryan McKenna, Ios Kotsogiannis, George Bissias, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
#8712;: A Framework for Defining Differentially-Private Computations. SIGMOD Rec. 48(1): 15-22 (2019) - [c5]Ryan McKenna, Daniel Sheldon, Gerome Miklau:
Graphical-model based estimation and inference for differential privacy. ICML 2019: 4435-4444 - [i5]Ryan McKenna, Daniel Sheldon, Gerome Miklau:
Graphical-model based estimation and inference for differential privacy. CoRR abs/1901.09136 (2019) - [i4]Satya Kuppam, Ryan McKenna, David Pujol, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
Fair Decision Making using Privacy-Protected Data. CoRR abs/1905.12744 (2019) - 2018
- [j2]Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala:
Optimizing error of high-dimensional statistical queries under differential privacy. Proc. VLDB Endow. 11(10): 1206-1219 (2018) - [c4]Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
EKTELO: A Framework for Defining Differentially-Private Computations. SIGMOD Conference 2018: 115-130 - [i3]Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala:
Optimizing error of high-dimensional statistical queries under differential privacy. CoRR abs/1808.03537 (2018) - [i2]Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau:
Ektelo: A Framework for Defining Differentially-Private Computations. CoRR abs/1808.03555 (2018) - 2017
- [c3]Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau:
Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models. ICML 2017: 478-487 - [i1]Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau:
Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models. CoRR abs/1706.04646 (2017) - 2016
- [j1]Cagri Sahin, Mian Wan, Philip Tornquist, Ryan McKenna, Zachary Pearson, William G. J. Halfond, James Clause:
How does code obfuscation impact energy usage? J. Softw. Evol. Process. 28(7): 565-588 (2016) - [c2]Ryan McKenna, Stephen Herbein, Adam Moody, Todd Gamblin, Michela Taufer:
Machine Learning Predictions of Runtime and IO Traffic on High-End Clusters. CLUSTER 2016: 255-258 - 2014
- [c1]Cagri Sahin, Philip Tornquist, Ryan McKenna, Zachary Pearson, James Clause:
How Does Code Obfuscation Impact Energy Usage? ICSME 2014: 131-140
Coauthor Index
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last updated on 2024-08-17 23:43 CEST by the dblp team
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