Ryan Rogers

According to our database, Ryan Rogers authored at least 30 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Privacy-Preserving Race/Ethnicity Estimation for Algorithmic Bias Measurement in the U.S.

Saikrishna Badrinarayanan, Osonde Osoba, Miao Cheng, Ryan Rogers, Sakshi Jain, Rahul Tandra, Natesh S. Pillai

CoRR, 2024

2023

A Unifying Privacy Analysis Framework for Unknown Domain Algorithms in Differential Privacy.

Ryan Rogers

CoRR, 2023

Challenges towards the Next Frontier in Privacy.

Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Matthew Jagielski, Yangsibo Huang, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie J. Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Sergey Yekhanin, Da Yu, Huanyu Zhang, Wanrong Zhang

CoRR, 2023

Adaptive Privacy Composition for Accuracy-first Mechanisms.

Ryan M. Rogers, Gennady Samorodnitsky, Zhiwei Steven Wu, Aaditya Ramdas

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fully-Adaptive Composition in Differential Privacy.

Justin Whitehouse, Aaditya Ramdas, Ryan Rogers, Steven Wu

Proceedings of the International Conference on Machine Learning, 2023

2022

Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints.

Justin Whitehouse, Aaditya Ramdas, Zhiwei Steven Wu, Ryan M. Rogers

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Differentially Private Histograms under Continual Observation: Streaming Selection into the Unknown.

Adrian Rivera Cardoso, Ryan Rogers

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Privacy Aware Experimentation over Sensitive Groups: A General Chi Square Approach.

Rina Friedberg, Ryan Rogers

Proceedings of the Algorithmic Fairness through the Lens of Causality and Privacy Workshop, 2022

2021

LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale.

Ryan Rogers, Subbu Subramaniam, Sean Peng, David Durfee, Seunghyun Lee, Santosh Kumar Kancha, Shraddha Sahay, Parvez Ahammad

J. Priv. Confidentiality, 2021

Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics.

Mark Cesar, Ryan Rogers

Proceedings of the Algorithmic Learning Theory, 2021

2020

A Members First Approach to Enabling LinkedIn's Labor Market Insights at Scale.

Ryan Rogers, Adrian Rivera Cardoso, Koray Mancuhan, Akash Kaura, Nikhil Gahlawat, Neha Jain, Paul Ko, Parvez Ahammad

CoRR, 2020

Unifying Privacy Loss Composition for Data Analytics.

Mark Cesar, Ryan Rogers

CoRR, 2020

Optimal Differential Privacy Composition for Exponential Mechanisms.

Jinshuo Dong, David Durfee, Ryan Rogers

Proceedings of the 37th International Conference on Machine Learning, 2020

Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis.

Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Optimal Differential Privacy Composition for Exponential Mechanisms and the Cost of Adaptivity.

Jinshuo Dong, David Durfee, Ryan Rogers

CoRR, 2019

Practical Differentially Private Top-k Selection with Pay-what-you-get Composition.

David Durfee, Ryan M. Rogers

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Locally Private Mean Estimation: $Z$-test and Tight Confidence Intervals.

Marco Gaboardi, Ryan Rogers, Or Sheffet

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Private Pareto Optimal Exchange.

Sampath Kannan, Jamie Morgenstern, Ryan Rogers, Aaron Roth

ACM Trans. Economics and Comput., 2018

Local Private Hypothesis Testing: Chi-Square Tests.

Marco Gaboardi, Ryan Rogers

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

A Decomposition of Forecast Error in Prediction Markets.

Miroslav Dudík, Sébastien Lahaie, Ryan M. Rogers, Jennifer Wortman Vaughan

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

A New Class of Private Chi-Square Hypothesis Tests.

Ryan Rogers, Daniel Kifer

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Do prices coordinate markets?

Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra

SIGecom Exch., 2016

A New Class of Private Chi-Square Tests.

Daniel Kifer, Ryan Rogers

CoRR, 2016

Privacy Odometers and Filters: Pay-as-you-Go Composition.

Ryan M. Rogers, Salil P. Vadhan, Aaron Roth, Jonathan R. Ullman

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs.

Shahin Jabbari, Ryan M. Rogers, Aaron Roth, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing.

Marco Gaboardi, Hyun-Woo Lim, Ryan M. Rogers, Salil P. Vadhan

Proceedings of the 33nd International Conference on Machine Learning, 2016

Max-Information, Differential Privacy, and Post-selection Hypothesis Testing.

Ryan M. Rogers, Aaron Roth, Adam D. Smith, Om Thakkar

Proceedings of the IEEE 57th Annual Symposium on Foundations of Computer Science, 2016

2015

Robust Mediators in Large Games.

Michael J. Kearns, Mallesh M. Pai, Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman

CoRR, 2015

Inducing Approximately Optimal Flow Using Truthful Mediators.

Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu

Proceedings of the Sixteenth ACM Conference on Economics and Computation, 2015

2014

Asymptotically truthful equilibrium selection in large congestion games.

Ryan M. Rogers, Aaron Roth

Proceedings of the ACM Conference on Economics and Computation, 2014