Ryan Rogers

Orcid: 0000-0002-0545-9350

Affiliations:
  • LinkedIn, Applied Research
  • University of Pennsylvania, Department of Mathematics, Philadelphia, PA, USA (former)


According to our database1, Ryan Rogers authored at least 29 papers between 2014 and 2023.

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Bibliography

2023
A Unifying Privacy Analysis Framework for Unknown Domain Algorithms in Differential Privacy.
CoRR, 2023

Challenges towards the Next Frontier in Privacy.
CoRR, 2023

Adaptive Privacy Composition for Accuracy-first Mechanisms.
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.
Proceedings of the International Conference on Machine Learning, 2023

2022
Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints.
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.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Privacy Aware Experimentation over Sensitive Groups: A General Chi Square Approach.
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.
J. Priv. Confidentiality, 2021

Bounding, Concentrating, and Truncating: Unifying Privacy Loss Composition for Data Analytics.
Proceedings of the Algorithmic Learning Theory, 2021

2020
A Members First Approach to Enabling LinkedIn's Labor Market Insights at Scale.
CoRR, 2020

Unifying Privacy Loss Composition for Data Analytics.
CoRR, 2020

Optimal Differential Privacy Composition for Exponential Mechanisms.
Proceedings of the 37th International Conference on Machine Learning, 2020

Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis.
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.
CoRR, 2019

Practical Differentially Private Top-k Selection with Pay-what-you-get Composition.
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.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Private Pareto Optimal Exchange.
ACM Trans. Economics and Comput., 2018

Local Private Hypothesis Testing: Chi-Square Tests.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
A Decomposition of Forecast Error in Prediction Markets.
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.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Do prices coordinate markets?
SIGecom Exch., 2016

A New Class of Private Chi-Square Tests.
CoRR, 2016

Privacy Odometers and Filters: Pay-as-you-Go Composition.
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.
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.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Max-Information, Differential Privacy, and Post-selection Hypothesis Testing.
Proceedings of the IEEE 57th Annual Symposium on Foundations of Computer Science, 2016

2015
Robust Mediators in Large Games.
CoRR, 2015

Inducing Approximately Optimal Flow Using Truthful Mediators.
Proceedings of the Sixteenth ACM Conference on Economics and Computation, 2015

2014
Asymptotically truthful equilibrium selection in large congestion games.
Proceedings of the ACM Conference on Economics and Computation, 2014


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