PUBLICATIONS

(Unless specified otherwise, authors are listed in alphabetical order. * indicates equal contribution.)



Preprints

Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms

Xiangyi Chen*, Tiancong Chen*, Haoran Sun, Z. S. W., and Mingyi Hong (contributional order)

arXiv

Private Hypothesis Selection

with Mark Bun, Gautam Kamath, and Thomas Steinke

arXiv

Eliciting and Enforcing Subjective Individual Fairness

with Chris Jung, Michael Kearns, Seth Neel, Aaron Roth, and Logan Stapleton

arXiv

Equal Opportunity in Online Classification with Partial Feedback

with Yahav Bechavod, Katrina Ligett, Aaron Roth, and Bo Waggoner

arXiv

Locally Private Gaussian Estimation

with Matthew Joseph, Janardhan Kulkarni, and Jieming Mao

arXiv

Incentivizing Exploration with Unbiased Histories

with Nicole Immorlica, Jieming Mao, and Alex Slivkins

arXiv

2019

Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing

Brett Beaulieu-Jones, Z. S. W., Chris Williams, Ran Lee, Sanjeev Bhavnani, James Byrd, and Casey Greene (contributional order)
Circulation: Cardiovascular Quality and Outcomes 2019; 12

bioRxiv Circ link GitHub

How to Use Heuristics for Differential Privacy

with Seth Neel and Aaron Roth
The 60th Annual IEEE Symposium on Foundations of Computer Science (FOCS'19)

arXiv

Fair Regression: Quantitative Definitions and Reduction-Based Algorithms

with Alekh Agarwal and Miro Dudik
The 36th International Conference on Machine Learning (ICML'19)

arXiv

Locally Private Bayesian Inference for Count Models

Aaron Schein, Z. S. W., Xanda Schofield, Mingyuan Zhou, and Hanna Wallach (contributional order)
The 36th International Conference on Machine Learning (ICML'19)

arXiv

Orthogonal Random Forest for Causal Inference

with Miruna Oprescu and Vasilis Syrgkanis
The 36th International Conference on Machine Learning (ICML'19)

arXiv GitHub

The Perils of Exploration under Competition: A Computational Modeling Approach

with Guy Aridor, Kevin Liu, and Alex Slivkins
The 20th ACM conference on Economics and Computation (EC'19)

arXiv

Bayesian Exploration with Heterogeneous Agents

with Nicole Immorlica, Jieming Mao, and Alex Slivkins
The Web Conference 2019 (TheWebConf'19 w/ Oral presentation)

arXiv MSFT Blog Post

An Empirical Study of Rich Subgroup Fairness for Machine Learning

with Michael Kearns, Seth Neel, and Aaron Roth
The Second Annual ACM Conference on Fairness, Accountability, and Transparency (FAT*'19)

arXiv GitHub

2018

A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem

with Sampath Kannan, Jamie Morgenstern, Aaron Roth, and Bo Waggoner
The Thirty-Second Conference on Neural Information Processing Systems (NeurIPS'18 w/ Spotlight)

arXiv

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

with Michael Kearns, Seth Neel, and Aaron Roth
The 35th International Conference on Machine Learning (ICML'18)

arXiv Penn News Coverage Aaron's Blog Post TCS + talk by MK

Semiparametric Contextual Bandits

Akshay Krishnamurthy, Z. S. W., and Vasilis Syrgkanis (contributional order)
The 35th International Conference on Machine Learning (ICML'18)

arXiv

The Externalities of Exploration and How Data Diversity Helps Exploitation

with Manish Raghavan, Alex Slivkins, and Jenn Wortman Vaughan
The 31st Annual Conference on Learning Theory (COLT'18)

arXiv

Strategic Classification from Revealed Preferences

with Jinshuo Dong, Aaron Roth, Zachary Schutzman, and Bo Waggoner
The 19th ACM conference on Economics and Computation (EC'18)

arXiv

Competing Bandits: Learning under Competition

with Yishay Mansour and Alex Slivkins
The 9th Innovations in Theoretical Computer Science (ITCS'18)

arXiv Talk

2017

Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM

with Katrina Ligett, Seth Neel, Aaron Roth, and Bo Waggoner
The Thirty-First Annual Conference on Neural Information Processing Systems (NIPS'17)

arXiv GitHub

Meritocratic Fairness in Cross-Population Selection

with Michael Kearns, and Aaron Roth
The 34th International Conference on Machine Learning (ICML'17)

Proceeding

Predicting with Distributions

with Michael Kearns
The 30th Annual Conference on Learning Theory (COLT'17)

arXiv

Multidimensional Dynamic Pricing for Welfare Maximization

with Aaron Roth, Alex Slivkins, and Jonathan Ullman
The 18th ACM Conference on Economics and Computation (EC'17)

arXiv Talk

Fairness Incentives for Myopic Agents

with Sampath Kannan, Michael Kearns, Jamie Morgenstern, Mallesh Pai, Aaron Roth, and Rakesh Vohra
The 18th ACM Conference on Economics and Computation (EC'17)

arXiv

2016

Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs

with Shahin Jabbari, Ryan Rogers, and Aaron Roth
The Thirtieth Annual Conference on Neural Information Processing Systems (NIPS'16)

arXiv

Bayesian Exploration: Incentivizing Exploration in Bayesian Games

with Yishay Mansour, Alex Slivkins, and Vasilis Syrgkanis
The 17th ACM Conference on Economics and Computation (EC'16)

arXiv

Private Algorithms for the Protected in Social Network Search

with Michael Kearns, Aaron Roth, and Grigory Yaroslavtsev
In Proceedings of the National Academy of Sciences (PNAS), 2016

PNAS Link arXiv

Press coverage by Quartz Pacific Standard Naked Scientist


Watch and Learn: Optimizing from Revealed Preferences Feedback

with Aaron Roth and Jonathan Ullman
The 48th ACM Symposium on Theory of Computing (STOC'16)

arXiv Slides Sigecom Exchanges

Adaptive Learning with Robust Generalization Guarantees

with Rachel Cummings, Katrina Ligett, Kobbi Nissim, and Aaron Roth
The 29th Annual Conference on Learning Theory (COLT'16)

arXiv

Logarithmic Query Complexity for Approximate Nash Computation in Large Games

with Paul Goldberg and Francisco J. Marmolejo Cossio
The 9th International Symposium on Algorithmic Game Theory (SAGT'16)

Springer Link arXiv

Coordination Complexity: Small Information Coordinating Large Populations

with Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, and Aaron Roth
The 7th Innovations in Theoretical Computer Science (ITCS'16)

arXiv

Jointly Private Convex Programming

with Justin Hsu, Zhiyi Huang, and Aaron Roth
The 27th ACM-SIAM Symposium on Discrete Algorithms (SODA'16)

arXiv

2015

Privacy and Truthful Equilibrium Selection for Aggregative Games

with Rachel Cummings, Michael Kearns, and Aaron Roth
The 11th Conference on Web and Internet Economics (WINE'15)

arXiv

Inducing Approximately Optimal Flow Using Truthful Mediators

with Ryan Rogers, Aaron Roth, and Jonathan Ullman
The 16th ACM Conference on Economics and Computation (EC'15)

arXiv Slides

Accuracy for Sale: Aggregating Data with a Variance Constraint

with Rachel Cummings, Katrina Ligett, Aaron Roth, and Juba Ziani
The 6th Innovations in Theoretical Computer Science (ITCS'15)

Proceeding

Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy)

with Sampath Kannan, Jamie Morgenstern, and Aaron Roth
The 26th ACM-SIAM Symposium on Discrete Algorithms (SODA'15)

arXiv Blog Post by Lipton and Regan

2014

Dual Query: Practical Private Query Release for High Dimensional Data

with Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, and Aaron Roth
The 31st International Conference on Machine Learning (ICML'14)
In the special issue of Journal of Privacy and Confidentiality, 2016

arXiv Talk GitHub

Private Matchings and Allocations

with Justin Hsu, Zhiyi Huang, Aaron Roth, and Tim Roughgarden
The 46th ACM Symposium on Theory of Computing (STOC'14)
SIAM Journal on Computing, 45(6):1953–1984, 2016.

arXiv