PUBLICATIONS

Kandinsky Conformal Prediction: Beyond Class- and Covariate-Conditional Coverage

with Konstantina Bairaktari, Jiayun Wu
The Forty-second International Conference on Machine Learning (ICML'25)
arXiv

Leveraging Model Guidance to Extract Training Data from Personalized Diffusion Models

Xiaoyu Wu, Jiaru Zhang, Z. S. W. (contributional order)
The Forty-second International Conference on Machine Learning (ICML'25)
arXiv

Orthogonal Causal Calibration

Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Z. S. W. (contributional order)
The 38th Annual Conference on Learning Theory (COLT'25)
arXiv

Time-Uniform Self-Normalized Concentration for Vector-Valued Processes

Justin Whitehouse, Z. S. W., Aaditya Ramdas (contributional order)
The 38th Annual Conference on Learning Theory (COLT'25)
arXiv

Multi-group Uncertainty Quantification for Long-form Text Generation

Terrance Liu, Z. S. W. (contributional order)
The 41st Conference on Uncertainty in Artificial Intelligence (UAI'25)
arXiv

Winning the MIDST Challenge: New Membership Inference Attacks on Diffusion Models for Tabular Data Synthesis

Xiaoyu Wu, Yifei Pang, Terrance Liu, Z. S. W. (contributional order)
Proceedings of the Theory and Practice of Differential Privacy Workshop (TPDP'25)
Winning solutions for all four tracks of The MIDST challenge at SaTML 2025
arXiv GitHub

Reconciling Model Multiplicity for Downstream Decision Making

Ally Yalei Du, Dung Daniel Ngo, Z. S. W.
The Thirteenth International Conference on Learning Representations (ICLR'25)
arXiv

Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning

Shengyuan Hu, Yiwei Fu, Z. S. W., Virginia Smith (contributional order)
The Thirteenth International Conference on Learning Representations (ICLR'25)
arXiv

Utility-Directed Conformal Prediction: A Decision-Aware Framework for Actionable Uncertainty Quantification

Santiago Cortes-Gomez, Carlos Miguel Patiño, Yewon Byun, Z. S. W., Eric Horvitz, Bryan Wilder (contributional order)
The Thirteenth International Conference on Learning Representations (ICLR'25)
arXiv

Position: LLM Unlearning Benchmarks are Weak Measures of Progress

Pratiksha Thaker, Shengyuan Hu, Neil Kale, Yash Maurya, Z. S. W., Virginia Smith (contributional order)
The IEEE Secure and Trustworthy Machine Learning Conference (SaTML'25)
arXiv

Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance

Xin Gu, Gautam Kamath, Z. S. W. (contributional order)
The IEEE Secure and Trustworthy Machine Learning Conference (SaTML'25)
arXiv

Benchmarking Fraud Detectors on Private Graph Data

Alexander Goldberg, Giulia Fanti, Nihar B. Shah, Z. S. W. (contributional order)
The 31st SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'25)
ACM Link

Inferentially-Private Private Information

Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Z. S. W. (contributional order)
Proceedings of The Web Conference (WWW'25)
arXiv

Differentially Private Bayesian Persuasion

with Yuqi Pan, Haifeng Xu, Shuran Zheng
Proceedings of The Web Conference (WWW'25 Oral)
arXiv

Quantifying Privacy Risks of Public Statistics to Residents of Subsidized Housing

Ryan Steed, Diana Qing, Z. S. W. (contributional order)
To appear in Harvard Data Science Review (HDSR'25)
arXiv

Competing Bandits: The Perils of Exploration Under Competition

with Guy Aridor, Yishay Mansour, Aleksandrs Slivkins
ACM Transactions on Economics and Computation, Volume 13, Issue 1, March 2025 (TEAC'25)
Extended version of the EC'19 and EC'18 papers.
ACM Link arXiv

Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift

Jiayun Wu, Jiashuo Liu, Peng Cui, Z. S. W. (contributional order)
The Thiry-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS'24)
arXiv

Oracle-Efficient Differentially Private Learning with Public Data

Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty, Z. S. W. (contributional order)
The Thiry-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS'24)
arXiv

Multi-Agent Imitation Learning: Value is Easy, Regret is Hard

Jingwu Tang, Gokul Swamy, Fei Fang, Z. S. W. (contributional order)
The Thiry-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS'24)
arXiv

On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift

Pratiksha Thaker, Amrith Setlur, Z. S. W., Virginia Smith (contributional order)
The Thiry-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS'24)
arXiv

Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable

Martin Bertran, Shuai Tang, Michael Kearns, Jamie H. Morgenstern, Aaron Roth, Z. S. W. (contributional order)
The Thiry-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS'24)
arXiv

Regret Minimization in Stackelberg Games with Side Information

Keegan Harris, Z. S. W., Maria-Florina Balcan (contributional order)
The Thiry-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS'24)
arXiv

Ex-post Individually Rational Bayesian Persuasion

Jiahao Zhang, Shuran Zheng, Renato Paes Leme, Z. S. W. (contributional order)
The 21st International Conference on Web and Internet Economics (WINE'24)
arXiv

A Minimaximalist Approach to Reinforcement Learning from Human Feedback

Gokul Swamy, Christoph Dann, Rahul Kidambi, Z. S. W., Alekh Agarwal (contributional order)
The 41st International Conference on Machine Learning (ICML'24)
arXiv

Predictive Performance Comparison of Decision Policies Under Confounding

Luke Guerdan, Amanda Coston, Kenneth Holstein, Z. S. W. (contributional order)
The 41st International Conference on Machine Learning (ICML'24)
arXiv

Hybrid Inverse Reinforcement Learning

Juntao Ren, Gokul Swamy, Z. S. W., J. Andrew Bagnell, Sanjiban Choudhury (contributional order)
The 41st International Conference on Machine Learning (ICML'24)
arXiv

Membership Inference Attacks on Diffusion Models via Quantile Regression

Shuai Tang*, Z. S. W.*, Sergül Aydöre, Michael Kearns, Aaron Roth (contributional order)
The 41st International Conference on Machine Learning (ICML'24)
arXiv

Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach

Xinwei Zhang, Zhiqi Bu, Z. S. W., Mingyi Hong (contributional order)
The Twelfth International Conference on Learning Representations (ICLR'24)
arXiv

Strategyproof Decision-Making in Panel Data Settings and Beyond

Keegan Harris, Anish Agarwal, Chara Podimata, Z. S. W. (contributional order)
The 2024 ACM SIGMETRICS / IFIP Performance conference (SIGMETRICS'24)
arXiv

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective

Satyapriya Krishna, Tessa Han, Alex Gu, Z. S. W., Shahin Jabbari, Himabindu Lakkaraju (contributional order)
Transactions on Machine Learning Research (TMLR'24)
arXiv

Fair Federated Learning via Bounded Group Loss

Shengyuan Hu, Z. S. W., Virginia Smith (contributional order)
The 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML'24)
Best Paper Award at ICLR 2022 Socially Responsible ML Workshop
arXiv

Improved Differentially Private Regression via Gradient Boosting

Shuai Tang, Sergül Aydöre, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Z. S. W. (contributional order)
The 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML'24)
arXiv

Confidence-ranked reconstruction of census microdata from published statistics

with Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth, and Giuseppe Vietri
Proceedings of the National Academy of Sciences (PNAS'23)
PNAS Link arXiv GitHub

Counterfactual Prediction Under Outcome Measurement Error

Luke Guerdan, Amanda Coston, Kenneth Holstein, and Z. S. W. (contributional order)
The Sixth ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT'23)
Best paper award at FAccT'23
arXiv

Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making

Luke Guerdan, Amanda Coston, Z. S. W., and Kenneth Holstein (contributional order)
The Sixth ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT'23)
arXiv

Learning Shared Safety Constraints from Multi-task Demonstrations

Konwoo Kim, Gokul Swamy, Zuxin Liu, Ding Zhao, Sanjiban Choudhury, and Z. S. W. (contributional order)
The Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS'23)
arXiv

On the Sublinear Regret of GP-UCB

Justin Whitehouse, Z. S. W., and Aaditya Ramdas (contributional order)
The Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS'23)
arXiv

Adaptive Privacy Composition for Accuracy-first Mechanisms

Ryan Rogers, Gennady Samorodnitsky, Z. S. W., and Aaditya Ramdas (contributional order)
The Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS'23)
arXiv

Scalable Membership Inference Attacks via Quantile Regression

Martin Andres Bertran, Shuai Tang, Aaron Roth, Michael Kearns, Jamie Morgenstern, and Z. S. W. (contributional order)
The Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS'23)
arXiv

Strategic Apple Tasting

Keegan Harris, Chara Podimata, and Z. S. W. (contributional order)
The Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS'23)
arXiv

Adaptive Principal Component Regression with Applications to Panel Data

with Anish Agarwal, Keegan Harris, and Justin Whitehouse
The Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS'23)
arXiv

Meta-Learning Adversarial Bandit Algorithms

Mikhail Khodak, Ilya Osadchiy, Keegan Harris, Nina Balcan, Kfir Yehuda Levy, Ron Meir, and Z. S. W. (contributional order)
The Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS'23)
arXiv

Private Data Stream Analysis for Universal Symmetric Norm Estimation

with Vladimir Braverman, Joel Manning, and Samson Zhou
The Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023)
arXiv

Inverse Reinforcement Learning without Reinforcement Learning

Gokul Swamy, David Wu, Sanjiban Choudhury, J. Drew Bagnell, and Z. S. W. (contributional order)
The 40th International Conference on Machine Learning (ICML'23)
arXiv

Generating Private Synthetic Data with Genetic Algorithms

with Terrance Liu, Jingwu Tang, and Giuseppe Vietri
The 40th International Conference on Machine Learning (ICML'23)
arXiv GitHub

Fully-Adaptive Composition in Differential Privacy

Justin Whitehouse, Aaditya Ramdas, Ryan Rogers, and Z. S. W. (contributional order)
The 40th International Conference on Machine Learning (ICML'23)
arXiv

Nonparametric Extensions of Randomized Response for Private Confidence Sets

Ian Waudby-Smith, Z. S. W., and Aaditya Ramdas (contributional order)
The 40th International Conference on Machine Learning (ICML'23)
arXiv

Greedy Algorithm Almost Dominates in Smoothed Contextual Bandits

with Manish Raghavan, Aleksandrs Slivkins, and Jennifer Wortman Vaughan
SIAM Journal on Computing (SICOMP'23)
Extended version of the COLT'18 paper
SIAM Link arXiv

Private Multi-Task Learning: Formulation and Applications to Federated Learning

Shengyuan Hu, Z. S. W., and Virginia Smith (contributional order)
Transactions on Machine Learning Research (TMLR'23)
TMLR Link arXiv

Reinforcement Learning with Stepwise Fairness Constraints

Zhun Deng, He Sun, Z. S. W., Linjun Zhang, and David Parkes (contributional order)
The 26th International Conference on Artificial Intelligence and Statistics (AISTATS'23)
arXiv

Meta-Learning in Games

Keegan Harris, Ioannis Anagnostides, Gabriele Farina, Mikhail Khodak, Z. S. W., and Tuomas Sandholm (contributional order)
The Eleventh Conference on Learning Representations (ICLR'23)
arXiv

Policy impacts of statistical uncertainty and privacy

Ryan Steed, Terrance Liu, Z. S. W., and Alessandro Acquisti (contributional order)
Science Vol 377, Issue 6609, 2022
Science GitHub

Sequence Model Imitation Learning with Unobserved Contexts

Gokul Swamy, Sanjiban Choudhury, J. Drew Bagnell, and Z. S. W. (contributional order)
The Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS'22)
arXiv

Minimax Optimal Online Imitation Learning via Replay Estimation

Gokul Swamy*, Nived Rajaraman*, Matthew Peng, Sanjiban Choudhury, J. Drew Bagnell, Z. S. W., Jiantao Jiao, and Kannan Ramchandran (contributional order)
The Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS'22)
arXiv

Bayesian Persuasion for Algorithmic Recourse

Keegan Harris, Valerie Chen, Joon Kim, Ameet Talwalkar, Hoda Heidari, and Z. S. W. (contributional order)
The Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS'22)
arXiv

Incentivizing Combinatorial Bandit Exploration

with Xinyan Hu, Daniel Ngo, and Alex Slivkins
The Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS'22)
arXiv

Private Synthetic Data for Multitask Learning and Marginal Queries

Giuseppe Vietri, Cedric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Ankit Siva, Shuai Tang, and Z. S. W. (contributional order)
The Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS'22)
arXiv

On Privacy and Personalization in Cross-Silo Federated Learning

Ken Liu, Shengyuan Hu, Z. S. W., and Virginia Smith (contributional order)
The Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS'22)
arXiv

Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints

Justin Whitehouse, Aaditya Ramdas, Z. S. W., and Ryan Rogers (contributional order)
The Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS'22)
arXiv

Causal Imitation Learning under Temporally Correlated Noise

Gokul Swamy, Sanjiban Choudhury, J. Drew Bagnell, and Z. S. W. (contributional order)
The 39th International Conference on Machine Learning (ICML'22 Long presentation)
arXiv

Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses

Keegan Harris, Daniel Ngo*, Logan Stapleton*, Hoda Heidari, Z. S. W. (contributional order)
The 39th International Conference on Machine Learning (ICML'22)
arXiv

Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization

Alberto Bietti, Chen-Yu Wei, Miro Dudik, John Langford, Z. S. W. (contributional order)
The 39th International Conference on Machine Learning (ICML'22)
arXiv

Information Discrepancy in Strategic Learning

with Yahav Bechavod, Chara Podimata, and Juba Ziani
The 39th International Conference on Machine Learning (ICML'22)
arXiv

Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy

Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Z. S. W., and Jinfeng Yi (contributional order)
The 39th International Conference on Machine Learning (ICML'22)
arXiv

Improved Regret for Differentially Private Exploration in Linear MDP

with Daniel Ngo and Giuseppe Vietri
The 39th International Conference on Machine Learning (ICML'22)
arXiv

Constrained Variational Policy Optimization for Safe Reinforcement Learning

Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Z. S. W., Bo Li, and Ding Zhao (contributional order)
The 39th International Conference on Machine Learning (ICML'22)
arXiv

How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions

Hao-Fei Cheng*, Logan Stapleton*, Anna Kawakami, Venkat Sivaraman, Yanghuidi Cheng, Diana Qing, Adam Perer, Ken Holstein, Z. S. W., and Haiyi Zhu (contributional order)
The 2022 ACM CHI Conference on Human Factors in Computing Systems (CHI'22)
ACM Link Extended Analysis Short Talk Blog

Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support

Anna Kawakami, Venkat Sivaraman, Hao-Fei Cheng, Logan Stapleton, Yanghuidi Cheng, Diana Qing, Adam Perer, Z. S. W., Haiyi Zhu, and Ken Holstein (contributional order)
The 2022 ACM CHI Conference on Human Factors in Computing Systems (CHI'22)
CHI Best Paper Honorable Mention
ACM Link arixiv Short Talk

'Why Do I Care What's Similar?' Probing Challenges in AI-Assisted Child Welfare Decision-Making through Worker-AI Interface Design Concepts

Anna Kawakami*, Venkat Sivaraman*, Hao-Fei Cheng, Logan Stapleton, Adam Perer, Z. S. W., Haiyi Zhu, and Ken Holstein (contributional order)
The 2022 ACM conference on Designing Interactive Systems (DIS'22)
ACM Link

Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits

Wesley Hanwen Deng, Manish Nagireddy, Michelle Seng Ah Lee, Jatinder Singh, Z. S. W., Ken Holstein, and Haiyi Zhu (contributional order)
The Fifth ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT'22)
ACM Link

Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders

Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright, Alexandra Chouldechova, Z. S. W., Ken Holstein, and Haiyi Zhu (contributional order)
The Fifth ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT'22)
ACM Link

Bandit Data-Driven Optimization for Crowdsourcing Food Rescue Platforms

Zheyuan Ryan Shi, Z. S. W., Rayid Ghani, and Fei Fang (contributional order)
The 36th AAAI Conference on Artificial Intelligence (AAAI'22)
arXiv

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)
Best paper awards from ICLR workshops: Distributed and Private Machine Learning and Synthetic Data Generation.
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

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)

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

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

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

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

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 2022
Invited to a special issue of Transactions on Economics and Computation for EC'16
arXiv OR Journal

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

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

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