Optimal Differential Privacy Composition for Exponential Mechanisms and the Cost of Adaptivity. Joint work with Jinshuo Dong and David Durfee. In submission.
Practical Differentially Private Top-k Selection with Pay-what-you-get Composition. Joint work with David Durfee. To appear in the proceedings of NeurIPS 2019 as a spotlight presentation (top 3% of submissions).
Protection Against Reconstruction and Its Applications in Private Federated Learning. Joint work with Abhishek Bhowmick, John Duchi, Julien Freudiger, and Gaurav Kapoor. In submission.
Locally Private Mean Estimation: Z-test and Tight
Confidence Intervals. Joint work with Marco Gaboardi and Or
Sheffet. In the proceedings of AISTATS 2019.
Max-Information, Differential
Privacy, and Post-Selection Hypothesis Testing. Joint work
with Aaron Roth, Adam Smith, and Om Thakkar. In the proceedings of FOCS
2016.
Differentially Private
Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing.
Joint work with Marco
Gaboardi, Hyun woo Lim, and Salil Vadhan. In the proceedings
of ICML 2016.
Robust Mediators in Large Games.
Joint work with Michael Kearns, Mallesh
M. Pai, Aaron Roth, and Jonathan Ullman. In submission. (This paper
subsumes both Mechanism Design in
Large Games: Incentives and Privacy which appeared in ITCS 2014,
and Asymptotically Truthful
Equilibrium Selection which appeared in EC 2014).
Learning from Rational
Behavior: Predicting Solutions to Unknown Linear Programs. Joint
work with Shahin Jabbari,
Aaron Roth, and Steven Wu. In the proceedings of NIPS 2016.
Identification of localized
structure in a nonlinear damped harmonic oscillator using Hamilton's
Principle. Joint work with Thomas
Vogel. Involve
(2010); vol. 3, issue 4.
