PUBLICATIONS

Unless specified otherwise, authors are listed in alphabetical order. The * sign indicates equal contribution.



Preprints

2021

Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods

with Terrance Liu, and Giuseppe Vietri
The Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS'21)

arXiv

Stateful Strategic Regression

with Keegan Harris, and Hoda Heidari
The Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS'21)

arXiv

Of Moments and Matching: Trade-offs and Treatments in Imitation Learning

Gokul Swamy, Sanjiban Choudhury, J. Drew Bagnell, and Z. S. W. (contributional order)
The 38th International Conference on Machine Learning (ICML'21)

arXiv

Leveraging Public Data for Practical Private Query Release

Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, and Z. S. W. (contributional order)
The 38th International Conference on Machine Learning (ICML'21)

arXiv

Towards the Unification and Robustness of Perturbation and Gradient Based Explanations

Sushant Agarwal, Shahin Jabbari, Chirag Agarwal*, Sohini Upadhyay*, Z. S. W., and Hima Lakkaraju (contributional order)
The 38th International Conference on Machine Learning (ICML'21)

arXiv

Incentivizing Compliance with Algorithmic Instruments

Daniel Ngo*, Logan Stapleton*, Vasilis Syrgkanis, Z. S. W. (contributional order)
The 38th International Conference on Machine Learning (ICML'21)

arXiv

An Algorithmic Framework for Fairness Elicitation

with Chris Jung, Michael Kearns, Seth Neel, Aaron Roth, and Logan Stapleton
The second annual Symposium on Foundations of Responsible Computing (FORC'21)

arXiv

Private Post-GAN Boosting

Marcel Neunhoeffer, Z. S. W., and Cynthia Dwork (contributional order)
The Ninth International Conference on Learning Representations (ICLR'21)

arXiv

Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification

Yingxue Zhou, Z. S. W., and Arindam Banerjee (contributional order)
The Ninth International Conference on Learning Representations (ICLR'21)

arXiv

Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization

with Vikas K. Garg, Katrina Ligett, and Adam Kalai
The 24th International Conference on Artificial Intelligence and Statistics (AISTATS'21)

arXiv

Gaming Helps! Learning from Strategic Interactions in Natural Dynamics

with Yahav Bechavod, Katrina Ligett, and Juba Ziani
The 24th International Conference on Artificial Intelligence and Statistics (AISTATS'21)

arXiv

Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems

Hao-Fei Cheng, Logan Stapleton, Ruiqi Wang, Paige Bullock, Alexandra Chouldechova, Z. S. W., and Haiyi Zhu (contributional order)
The 2021 ACM CHI Conference on Human Factors in Computing Systems (CHI'21)

arXiv

Value Cards: An Educational Toolkits for Teaching Social Impacts of Machine Learning through Deliberation

Hong Shen, Wesley Deng, Aditi Chattopadhyay, Z. S. W., Xu Wang, and Haiyi Zhu (contributional order)
The Fourth ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT'21)

arXiv

2020

Metric-Free Individual Fairness in Online Learning

with Yahav Bechavod and Chris Jung
The Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS'20 Oral)

arXiv

Understanding Gradient Clipping in Private SGD: A Geometric Perspective

Xiangyi Chen, Z. S. W., and Mingyi Hong (contributional order)
The Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS'20 Spotlight)

arXiv

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)
The Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS'20)

arXiv

Private Reinforcement Learning with PAC and Regret Guarantees

Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, and Z. S. W. (contributional order)
The 37th International Conference on Machine Learning (ICML'20)

arXiv

New Oracle-Efficient Algorithms for Private Synthetic Data Release

Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, and Z. S. W. (contributional order)
The 37th International Conference on Machine Learning (ICML'20)

arXiv

Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis

Vidyashankar Sivakumar, Z. S. W., and Arindam Banerjee (contributional order)
The 37th International Conference on Machine Learning (ICML'20)

arXiv

Private Query Release Assisted by Public Data

with Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, and Jonathan Ullman
The 37th International Conference on Machine Learning (ICML'20)

arXiv

Oracle Efficient Private Non-Convex Optimization

with Seth Neel, Aaron Roth, and Giuseppe Vietri
The 37th International Conference on Machine Learning (ICML'20)

arXiv

Privately Learning Markov Random Fields

Huanyu Zhang, Gautam Kamath*, Janardhan Kulkarni*, and Z. S. W.* (contributional order)
The 37th International Conference on Machine Learning (ICML'20)

arXiv

Locally Private Hypothesis Selection

with Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, and Huanyu Zhang
The 33rd Annual Conference on Learning Theory (COLT'20)

arXiv

Keeping Designers in the Loop: Communicating Inherent Algorithmic Trade-offs Across Multiple Objectives

Bowen Yu, Irene Yuan, Loren Terveen, Z. S. W., Jodi Forlizzi, and Haiyi Zhu (contributional order)
The 2020 ACM conference on Designing Interactive Systems (DIS'20)

arXiv

Incentivizing Exploration with Selective Data Disclosure

with Nicole Immorlica, Jieming Mao, and Alex Slivkins
The 21st ACM Conference on Economics and Computation (EC'20)

arXiv

2019

Private Hypothesis Selection

with Mark Bun, Gautam Kamath, and Thomas Steinke
The Thirty-Third Conference on Neural Information Processing Systems (NeurIPS'19)

arXiv

Equal Opportunity in Online Classification with Partial Feedback

with Yahav Bechavod, Katrina Ligett, Aaron Roth, and Bo Waggoner
The Thirty-Third Conference on Neural Information Processing Systems (NeurIPS'19)

arXiv

Locally Private Gaussian Estimation

with Matthew Joseph, Janardhan Kulkarni, and Jieming Mao
The Thirty-Third Conference on Neural Information Processing Systems (NeurIPS'19)

arXiv

Random Qudratic Forms with Dependence: Applications to Restricted Isometry and Beyond

with Arindam Banerjee, Qilong Gu, and Vidyashankar Sivakumar
The Thirty-Third Conference on Neural Information Processing Systems (NeurIPS'19)

arxiv

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 GitHub

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 Talk

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 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 Full Version

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)
Invited to a special issue of Transactions on Economics and Computation for 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)
Operations Research
Invited to a special issue of Transactions on Economics and Computation for 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 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)
In the special issue of Theory of Computing for 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

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)
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 Talk