Ryan McKenna - RankMe

58 papers A* 9A 2C 1Journal 43Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	DP-λCGD: Efficient Noise Correlation for Differentially Private Model Training. CoRR Nikita P. Kalinin, Ryan McKenna, Rasmus Pagh, Christoph H. Lampert
2026	J	`jnl`	JAX-Privacy: A library for differentially private machine learning. CoRR Ryan McKenna, Galen Andrew, Borja Balle, Vadym Doroshenko, Arun Ganesh, Weiwei Kong, Alex Kurakin, Brendan McMahan, Mikhail Pravilov
2026	J	`jnl`	MAPLE: Metadata Augmented Private Language Evolution. CoRR Eli Chien, Yuzheng Hu, Ryan McKenna, Shanshan Wu, Zheng Xu, Peter Kairouz
2026	J	`jnl`	Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Data. CoRR Lucas Rosenblatt, Peihan Liu, Ryan McKenna, Natalia Ponomareva
2025	J	`jnl`	ACTG-ARL: Differentially Private Conditional Text Generation with RL-Boosted Control. CoRR Yuzheng Hu, Ryan McKenna, Da Yu, Shanshan Wu, Han Zhao, Zheng Xu, Peter Kairouz
2025	J	`jnl`	Back to Square Roots: An Optimal Bound on the Matrix Factorization Error for Multi-Epoch Differentially Private SGD. CoRR Nikita P. Kalinin, Ryan McKenna, Jalaj Upadhyay, Christoph H. Lampert
2025	J	`jnl`	Benchmarking Differentially Private Tabular Data Synthesis. CoRR Kai Chen, Xiaochen Li, Chen Gong, Ryan McKenna, Tianhao Wang
2025	J	`jnl`	Benchmarking Differentially Private Tabular Data Synthesis: [Experiments & Analysis]. Proc. ACM Manag. Data Kai Chen, Xiaochen Li, Chen Gong, Ryan McKenna, Tianhao Wang
2025	J	`jnl`	Correlated Noise Mechanisms for Differentially Private Learning. CoRR Krishna Pillutla, Jalaj Upadhyay, Christopher A. Choquette-Choo, Krishnamurthy Dvijotham, Arun Ganesh, Monika Henzinger, Jonathan Katz, Ryan McKenna, H. Brendan McMahan, Keith Rush, Thomas Steinke, Abhradeep Thakurta
2025	J	`jnl`	How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy. CoRR Natalia Ponomareva, Zheng Xu, H. Brendan McMahan, Peter Kairouz, Lucas Rosenblatt, Vincent Cohen-Addad, Cristóbal Guzmán, Ryan McKenna, Galen Andrew, Alex Bie, Da Yu, Alex Kurakin, Morteza Zadimoghaddam, Sergei Vassilvitskii, Andreas Terzis
2025	J	`jnl`	Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data? CoRR Marika Swanberg, Ryan McKenna, Edo Roth, Albert Cheu, Peter Kairouz
2025	A*	`conf`	It's My Data Too: Private ML for Datasets with Multi-User Training Examples. ICML Arun Ganesh, Ryan McKenna, Hugh Brendan McMahan, Adam Smith, Fan Wu
2025	J	`jnl`	It's My Data Too: Private ML for Datasets with Multi-User Training Examples. CoRR Arun Ganesh, Ryan McKenna, Brendan McMahan, Adam Smith, Fan Wu
2025	J	`jnl`	Learn from the Past: Language-conditioned Object Rearrangement with Large Language Models. CoRR Guanqun Cao, Ryan McKenna, John Oyekan
2025	—	`conf`	Learning with User-Level Differential Privacy Under Fixed Compute Budgets. SaTML Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush
2025	A*	`conf`	Scaling Laws for Differentially Private Language Models. ICML Ryan McKenna, Yangsibo Huang, Amer Sinha, Borja Balle, Zachary Charles, Christopher A. Choquette-Choo, Badih Ghazi, Georgios Kaissis, Ravi Kumar, Ruibo Liu, Da Yu, Chiyuan Zhang
2025	J	`jnl`	Scaling Laws for Differentially Private Language Models. CoRR Ryan McKenna, Yangsibo Huang, Amer Sinha, Borja Balle, Zachary Charles, Christopher A. Choquette-Choo, Badih Ghazi, Georgios Kaissis, Ravi Kumar, Ruibo Liu, Da Yu, Chiyuan Zhang
2025	A*	`conf`	Scaling up the Banded Matrix Factorization Mechanism for Large Scale Differentially Private ML. ICLR Ryan McKenna
2025	J	`jnl`	VaultGemma: A Differentially Private Gemma Model. CoRR Amer Sinha, Thomas Mesnard, Ryan McKenna, Daogao Liu, Christopher A. Choquette-Choo, Yangsibo Huang, Da Yu, Georgios Kaissis, Zachary Charles, Ruibo Liu, Lynn Chua, Pritish Kamath, Pasin Manurangsi, Steve He, Chiyuan Zhang, Badih Ghazi, Borja de Balle Pigem, Prem Eruvbetine, Tris Warkentin, Armand Joulin, Ravi Kumar
2024	J	`jnl`	Fine-Tuning Large Language Models with User-Level Differential Privacy. CoRR Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush
2024	A	`conf`	Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data. AISTATS Miguel Fuentes, Brett C. Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon
2024	J	`jnl`	Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data. CoRR Miguel Fuentes, Brett Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon
2024	J	`jnl`	Mayfly: Private Aggregate Insights from Ephemeral Streams of On-Device User Data. CoRR Christopher Bian, Albert Cheu, Stanislav Chiknavaryan, Zoe Gong, Marco Gruteser, Oliver Guinan, Yannis Guzman, Peter Kairouz, Artem Lagzdin, Ryan McKenna, Grace Ni, Edo Roth, Maya Spivak, Timon Van Overveldt, Ren Yi
2024	J	`jnl`	Releasing Large-Scale Human Mobility Histograms with Differential Privacy. CoRR Christopher Bian, Albert Cheu, Yannis Guzman, Marco Gruteser, Peter Kairouz, Ryan McKenna, Edo Roth
2024	J	`jnl`	Scaling up the Banded Matrix Factorization Mechanism for Differentially Private ML. CoRR Ryan McKenna
2023	A*	`conf`	(Amplified) Banded Matrix Factorization: A unified approach to private training. NeurIPS Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, John Rush, Abhradeep Guha Thakurta, Zheng Xu
2023	J	`jnl`	(Amplified) Banded Matrix Factorization: A unified approach to private training. CoRR Christopher A. Choquette-Choo, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu
2023	J	`jnl`	Convergence of Gradient Descent with Linearly Correlated Noise and Applications to Differentially Private Learning. CoRR Anastasia Koloskova, Ryan McKenna, Zachary Charles, Keith Rush, Brendan McMahan
2023	A*	`conf`	Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy. NeurIPS Anastasia Koloskova, Ryan McKenna, Zachary Charles, John Keith Rush, H. Brendan McMahan
2023	J	`jnl`	Optimizing Error of High-Dimensional Statistical Queries Under Differential Privacy. J. Priv. Confidentiality Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala
2022	J	`jnl`	AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. CoRR Ryan McKenna, Brett Mullins, Daniel Sheldon, Gerome Miklau
2022	J	`jnl`	AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. Proc. VLDB Endow. Ryan McKenna, Brett Mullins, Daniel Sheldon, Gerome Miklau
2021	J	`jnl`	Benchmarking Differentially Private Synthetic Data Generation Algorithms. CoRR Yuchao Tao, Ryan McKenna, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
2021	J	`jnl`	HDMM: Optimizing error of high-dimensional statistical queries under differential privacy. CoRR Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala
2021	A*	`conf`	Relaxed Marginal Consistency for Differentially Private Query Answering. NeurIPS Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau
2021	J	`jnl`	Relaxed Marginal Consistency for Differentially Private Query Answering. CoRR Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau
2021	J	`jnl`	Winning the NIST Contest: A scalable and general approach to differentially private synthetic data. CoRR Ryan McKenna, Gerome Miklau, Daniel Sheldon
2021	J	`jnl`	Winning the NIST Contest: A scalable and general approach to differentially private synthetic data. J. Priv. Confidentiality Ryan McKenna, Gerome Miklau, Daniel Sheldon
2020	J	`jnl`	A workload-adaptive mechanism for linear queries under local differential privacy. CoRR Ryan McKenna, Raj Kumar Maity, Arya Mazumdar, Gerome Miklau
2020	J	`jnl`	A workload-adaptive mechanism for linear queries under local differential privacy. Proc. VLDB Endow. Ryan McKenna, Raj Kumar Maity, Arya Mazumdar, Gerome Miklau
2020	—	`conf`	Fair decision making using privacy-protected data. FAT* David Pujol, Ryan McKenna, Satya Kuppam, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
2020	A*	`conf`	Permute-and-Flip: A new mechanism for differentially private selection. NeurIPS Ryan McKenna, Daniel Sheldon
2020	J	`jnl`	Permute-and-Flip: A new mechanism for differentially private selection. CoRR Ryan McKenna, Daniel Sheldon
2020	J	`jnl`	ϵKTELO: A Framework for Defining Differentially Private Computations. ACM Trans. Database Syst. Dan Zhang, Ryan McKenna, Ios Kotsogiannis, George Bissias, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
2019	J	`jnl`	#8712;: A Framework for Defining Differentially-Private Computations. SIGMOD Rec. Dan Zhang, Ryan McKenna, Ios Kotsogiannis, George Bissias, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
2019	J	`jnl`	Fair Decision Making using Privacy-Protected Data. CoRR Satya Kuppam, Ryan McKenna, David Pujol, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
2019	A*	`conf`	Graphical-model based estimation and inference for differential privacy. ICML Ryan McKenna, Daniel Sheldon, Gerome Miklau
2019	J	`jnl`	Graphical-model based estimation and inference for differential privacy. CoRR Ryan McKenna, Daniel Sheldon, Gerome Miklau
2019	J	`jnl`	PSynDB: Accurate and Accessible Private Data Generation. Proc. VLDB Endow. Zhiqi Huang, Ryan McKenna, George Bissias, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala
2018	—	`conf`	EKTELO: A Framework for Defining Differentially-Private Computations. SIGMOD Conference Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
2018	J	`jnl`	Ektelo: A Framework for Defining Differentially-Private Computations. CoRR Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, Gerome Miklau
2018	J	`jnl`	Optimizing error of high-dimensional statistical queries under differential privacy. CoRR Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala
2018	J	`jnl`	Optimizing error of high-dimensional statistical queries under differential privacy. Proc. VLDB Endow. Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala
2017	A*	`conf`	Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models. ICML Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau
2017	J	`jnl`	Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models. CoRR Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau
2016	J	`jnl`	How does code obfuscation impact energy usage? J. Softw. Evol. Process. Cagri Sahin, Mian Wan, Philip Tornquist, Ryan McKenna, Zachary Pearson, William G. J. Halfond, James Clause
2016	C	`conf`	Machine Learning Predictions of Runtime and IO Traffic on High-End Clusters. CLUSTER Ryan McKenna, Stephen Herbein, Adam Moody, Todd Gamblin, Michela Taufer
2014	A	`conf`	How Does Code Obfuscation Impact Energy Usage? ICSME Cagri Sahin, Philip Tornquist, Ryan McKenna, Zachary Pearson, James Clause