Asymptotically truthful equilibrium selection in large congestion games. - Ryan M. Rogers; Aaron Roth 001 (EC, 2014)

Inducing Approximately Optimal Flow Using Truthful Mediators. - Ryan M. Rogers; Aaron Roth 001; Jonathan R. Ullman; Zhiwei Steven Wu (EC, 2015)

Robust Mediators in Large Games. - Michael J. Kearns; Mallesh M. Pai; Ryan M. Rogers; Aaron Roth 001; Jonathan R. Ullman (CoRR, 2015)

Max-Information, Differential Privacy, and Post-selection Hypothesis Testing. - Ryan M. Rogers; Aaron Roth 001; Adam D. Smith 001; Om Thakkar 001 (FOCS, 2016)

Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing. - Marco Gaboardi; Hyun-Woo Lim; Ryan M. Rogers; Salil P. Vadhan (ICML, 2016)

Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs. - Shahin Jabbari; Ryan M. Rogers; Aaron Roth 001; Zhiwei Steven Wu (NIPS, 2016)

Privacy Odometers and Filters: Pay-as-you-Go Composition. - Ryan M. Rogers; Salil P. Vadhan; Aaron Roth 001; Jonathan R. Ullman (NIPS, 2016)

A New Class of Private Chi-Square Tests. - Daniel Kifer; Ryan Rogers 002 (CoRR, 2016)

Do prices coordinate markets? - Justin Hsu; Jamie Morgenstern; Ryan M. Rogers; Aaron Roth 001; Rakesh Vohra (SIGecom Exch., 2016)

A New Class of Private Chi-Square Hypothesis Tests. - Ryan Rogers 002; Daniel Kifer (AISTATS, 2017)

A Decomposition of Forecast Error in Prediction Markets. - Miroslav Dudík; Sébastien Lahaie; Ryan M. Rogers; Jennifer Wortman Vaughan (NIPS, 2017)

Local Private Hypothesis Testing: Chi-Square Tests. - Marco Gaboardi; Ryan Rogers 002 (ICML, 2018)

Private Pareto Optimal Exchange. - Sampath Kannan; Jamie Morgenstern; Ryan Rogers 002; Aaron Roth 001 (ACM Trans. Economics and Comput., 2018)

Locally Private Mean Estimation: $Z$-test and Tight Confidence Intervals. - Marco Gaboardi; Ryan Rogers 002; Or Sheffet (AISTATS, 2019)

Practical Differentially Private Top-k Selection with Pay-what-you-get Composition. - David Durfee; Ryan M. Rogers (NeurIPS, 2019)

Optimal Differential Privacy Composition for Exponential Mechanisms and the Cost of Adaptivity. - Jinshuo Dong; David Durfee; Ryan Rogers 002 (CoRR, 2019)

Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. - Ryan Rogers 002; Aaron Roth 001; Adam D. Smith 001; Nathan Srebro; Om Thakkar 001; Blake E. Woodworth (AISTATS, 2020)

Optimal Differential Privacy Composition for Exponential Mechanisms. - Jinshuo Dong; David Durfee; Ryan Rogers 002 (ICML, 2020)

Unifying Privacy Loss Composition for Data Analytics. - Mark Cesar; Ryan Rogers 002 (CoRR, 2020)

A Members First Approach to Enabling LinkedIn's Labor Market Insights at Scale. - Ryan Rogers 002; Adrian Rivera Cardoso; Koray Mancuhan; Akash Kaura; Nikhil Gahlawat; Neha Jain; Paul Ko; Parvez Ahammad (CoRR, 2020)

Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics. - Mark Cesar; Ryan Rogers 002 (ALT, 2021)

LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale. - Ryan Rogers 002; Subbu Subramaniam; Sean Peng; David Durfee; Seunghyun Lee; Santosh Kumar Kancha; Shraddha Sahay; Parvez Ahammad (J. Priv. Confidentiality, 2021)

Privacy Aware Experimentation over Sensitive Groups: A General Chi Square Approach. - Rina Friedberg; Ryan Rogers 002 (AFCP, 2022)

Differentially Private Histograms under Continual Observation: Streaming Selection into the Unknown. - Adrian Rivera Cardoso; Ryan Rogers 002 (AISTATS, 2022)

Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints. - Justin Whitehouse; Aaditya Ramdas; Zhiwei Steven Wu; Ryan M. Rogers (NeurIPS, 2022)

Fully-Adaptive Composition in Differential Privacy. - Justin Whitehouse; Aaditya Ramdas; Ryan Rogers 002; Steven Wu 001 (ICML, 2023)

Adaptive Privacy Composition for Accuracy-first Mechanisms. - Ryan M. Rogers; Gennady Samorodnitsky; Zhiwei Steven Wu; Aaditya Ramdas (NeurIPS, 2023)

Challenges towards the Next Frontier in Privacy. - Rachel Cummings; Damien Desfontaines; David Evans 001; Roxana Geambasu; Matthew Jagielski; Yangsibo Huang; Peter Kairouz; Gautam Kamath 001; Sewoong Oh; Olga Ohrimenko; Nicolas Papernot; Ryan Rogers 002; et al. (CoRR, 2023)

A Unifying Privacy Analysis Framework for Unknown Domain Algorithms in Differential Privacy. - Ryan Rogers 002 (CoRR, 2023)

Privacy-Preserving Race/Ethnicity Estimation for Algorithmic Bias Measurement in the U.S. - Saikrishna Badrinarayanan; Osonde Osoba; Miao Cheng; Ryan Rogers 002; Sakshi Jain; Rahul Tandra; Natesh S. Pillai (CoRR, 2024)