Publications (67)
Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing
Ryan Rogers, Aaron Roth, Adam Smith +1
Generalization in Adaptive Data Analysis and Holdout Reuse
Cynthia Dwork, Vitaly Feldman, Moritz Hardt +3
An Empirical Study of Rich Subgroup Fairness for Machine Learning
Michael Kearns, Seth Neel, Aaron Roth +1
Local Differential Privacy for Evolving Data
Matthew Joseph, Aaron Roth, Jonathan Ullman +1
Adaptive Learning with Robust Generalization Guarantees
Rachel Cummings, Katrina Ligett, Kobbi Nissim +2
Online Learning and Profit Maximization from Revealed Preferences
Kareem Amin, Rachel Cummings, Lili Dworkin +2
Computer-aided verification in mechanism design
Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias +3
Constrained Non-Monotone Submodular Maximization: Offline and Secretary Algorithms
Anupam Gupta, Aaron Roth, Grant Schoenebeck +1
Preserving Statistical Validity in Adaptive Data Analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt +3
An Anti-Folk Theorem for Large Repeated Games with Imperfect Monitoring
Mallesh M. Pai, Aaron Roth, Jonathan Ullman
The Strange Case of Privacy in Equilibrium Models
Rachel Cummings, Katrina Ligett, Mallesh M. Pai +1
Gaussian Differential Privacy
Jinshuo Dong, Aaron Roth, Weijie J. Su
Fuzzi: A Three-Level Logic for Differential Privacy
Hengchu Zhang, Edo Roth, Andreas Haeberlen +2
Asymptotically Truthful Equilibrium Selection in Large Congestion Games
Ryan Rogers, Aaron Roth
Beyond Worst-Case Analysis in Private Singular Vector Computation
Moritz Hardt, Aaron Roth
Fairness in Learning: Classic and Contextual Bandits
Matthew Joseph, Michael Kearns, Jamie Morgenstern +1
Privacy and Truthful Equilibrium Selection for Aggregative Games
Rachel Cummings, Michael Kearns, Aaron Roth +1
Coordination Complexity: Small Information Coordinating Large Populations
Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan +2
Fairness Incentives for Myopic Agents
Sampath Kannan, Michael Kearns, Jamie Morgenstern +4
Take it or Leave it: Running a Survey when Privacy Comes at a Cost
Katrina Ligett, Aaron Roth
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
Seth Neel, Aaron Roth
Privacy and Mechanism Design
Mallesh Pai, Aaron Roth
A Learning Theory Approach to Non-Interactive Database Privacy
Avrim Blum, Katrina Ligett, Aaron Roth
Fair Algorithms for Learning in Allocation Problems
Hadi Elzayn, Shahin Jabbari, Christopher Jung +4
Online Learning with an Unknown Fairness Metric
Stephen Gillen, Christopher Jung, Michael Kearns +1
Downstream Effects of Affirmative Action
Sampath Kannan, Aaron Roth, Juba Ziani
Fairness in Reinforcement Learning
Shahin Jabbari, Matthew Joseph, Michael Kearns +2
Exploiting Metric Structure for Efficient Private Query Release
Zhiyi Huang, Aaron Roth
Interactive Privacy via the Median Mechanism
Aaron Roth, Tim Roughgarden
Privately Solving Linear Programs
Justin Hsu, Aaron Roth, Tim Roughgarden +1
Beating Randomized Response on Incoherent Matrices
Moritz Hardt, Aaron Roth
Strategic Classification from Revealed Preferences
Jinshuo Dong, Aaron Roth, Zachary Schutzman +2
Private Pareto Optimal Exchange
Sampath Kannan, Jamie Morgenstern, Ryan Rogers +1
Multidimensional Dynamic Pricing for Welfare Maximization
Aaron Roth, Aleksandrs Slivkins, Jonathan Ullman +1
Inducing Approximately Optimal Flow Using Truthful Mediators
Ryan Rogers, Aaron Roth, Jonathan Ullman +1
Efficiently Learning from Revealed Preference
Morteza Zadimoghaddam, Aaron Roth
Iterative Constructions and Private Data Release
Anupam Gupta, Aaron Roth, Jonathan Ullman
How to Use Heuristics for Differential Privacy
Seth Neel, Aaron Roth, Zhiwei Steven Wu
Do Prices Coordinate Markets?
Justin Hsu, Jamie Morgenstern, Ryan Rogers +2
Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy)
Sampath Kannan, Jamie Morgenstern, Aaron Roth +1
Differential Privacy: An Economic Method for Choosing Epsilon
Justin Hsu, Marco Gaboardi, Andreas Haeberlen +4
Conducting Truthful Surveys, Cheaply
Aaron Roth, Grant Schoenebeck
Fair Algorithms for Infinite and Contextual Bandits
Matthew Joseph, Michael Kearns, Jamie Morgenstern +2
Jointly Private Convex Programming
Justin Hsu, Zhiyi Huang, Aaron Roth +1
Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy
Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias +3
Fast Private Data Release Algorithms for Sparse Queries
Avrim Blum, Aaron Roth
Buying Private Data without Verification
Arpita Ghosh, Katrina Ligett, Aaron Roth +1
Privacy for the Protected (Only)
Michael Kearns, Aaron Roth, Zhiwei Steven Wu +1
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs
Shahin Jabbari, Ryan Rogers, Aaron Roth +1
Mechanism Design in Large Games: Incentives and Privacy
Michael Kearns, Mallesh M. Pai, Aaron Roth +1
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM
Katrina Ligett, Seth Neel, Aaron Roth +2
Differentially Private Combinatorial Optimization
Anupam Gupta, Katrina Ligett, Frank McSherry +2
Dual Query: Practical Private Query Release for High Dimensional Data
Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu +2
Private Matchings and Allocations
Justin Hsu, Zhiyi Huang, Aaron Roth +2
Selling Privacy at Auction
Arpita Ghosh, Aaron Roth
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Michael Kearns, Seth Neel, Aaron Roth +1
Differential Privacy for the Analyst via Private Equilibrium Computation
Justin Hsu, Aaron Roth, Jonathan Ullman
Differentially Private Fair Learning
Matthew Jagielski, Michael Kearns, Jieming Mao +4
Privately Releasing Conjunctions and the Statistical Query Barrier
Anupam Gupta, Moritz Hardt, Aaron Roth +1
Robust Mediators in Large Games
Michael Kearns, Mallesh M. Pai, Ryan Rogers +2
Differential Privacy and the Fat-Shattering Dimension of Linear Queries
Aaron Roth
Watch and Learn: Optimizing from Revealed Preferences Feedback
Aaron Roth, Jonathan Ullman, Zhiwei Steven Wu
Constrained Signaling in Auction Design
Shaddin Dughmi, Nicole Immorlica, Aaron Roth
The Frontiers of Fairness in Machine Learning
Alexandra Chouldechova, Aaron Roth
Distributed Private Heavy Hitters
Justin Hsu, Sanjeev Khanna, Aaron Roth
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem
Sampath Kannan, Jamie Morgenstern, Aaron Roth +2
A Convex Framework for Fair Regression
Richard Berk, Hoda Heidari, Shahin Jabbari +5