Zhiwei Steven Wu

261 papers A* 68A 8B 1Journal 168Unranked 16

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Back to Blackwell: Closing the Loop on Intransitivity in Multi-Objective Preference Fine-Tuning. CoRR Jiahao Zhang, Lujing Zhang, Keltin Grimes, Zhuohao Yu, Gokul Swamy, Zhiwei Steven Wu
2026	J	`jnl`	Gained in Translation: Privileged Pairwise Judges Enhance Multilingual Reasoning. CoRR Lintang Sutawika, Gokul Swamy, Zhiwei Steven Wu, Graham Neubig
2026	J	`jnl`	Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration. Trans. Mach. Learn. Res. Dung Daniel T. Ngo, Keegan Harris, Anish Agarwal, Vasilis Syrgkanis, Zhiwei Steven Wu
2026	J	`jnl`	Many AI Analysts, One Dataset: Navigating the Agentic Data Science Multiverse. CoRR Martín Bertrán, Riccardo Fogliato, Zhiwei Steven Wu
2026	J	`jnl`	Rubrics as an Attack Surface: Stealthy Preference Drift in LLM Judges. CoRR Ruomeng Ding, Yifei Pang, He Sun, Yizhong Wang, Zhiwei Steven Wu, Zhun Deng
2025	J	`jnl`	All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning. CoRR Gokul Swamy, Sanjiban Choudhury, Wen Sun, Zhiwei Steven Wu, J. Andrew Bagnell
2025	J	`jnl`	Benchmarking Fraud Detectors on Private Graph Data. CoRR Alexander Goldberg, Giulia Fanti, Nihar B. Shah, Zhiwei Steven Wu
2025	J	`jnl`	Breaking the Gold Standard: Extracting Forgotten Data under Exact Unlearning in Large Language Models. CoRR Xiaoyu Wu, Yifei Pang, Terrance Liu, Zhiwei Steven Wu
2025	—	`conf`	Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance. SaTML Xin Gu, Gautam Kamath, Zhiwei Steven Wu
2025	A*	`conf`	Differentially Private Bayesian Persuasion. WWW Yuqi Pan, Zhiwei Steven Wu, Haifeng Xu, Shuran Zheng
2025	J	`jnl`	Dimension-Free Decision Calibration for Nonlinear Loss Functions. CoRR Jingwu Tang, Jiayun Wu, Zhiwei Steven Wu, Jiahao Zhang
2025	J	`jnl`	Discretization-free Multicalibration through Loss Minimization over Tree Ensembles. CoRR Hongyi Henry Jin, Zijun Ding, Dung Daniel T. Ngo, Zhiwei Steven Wu
2025	J	`jnl`	Doubly-Robust LLM-as-a-Judge: Externally Valid Estimation with Imperfect Personas. CoRR Luke Guerdan, Justin Whitehouse, Kimberly Le Truong, Kenneth Holstein, Zhiwei Steven Wu
2025	J	`jnl`	Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference. CoRR Terrance Liu, Matteo Boglioni, Yiwei Fu, Shengyuan Hu, Pratiksha Thaker, Zhiwei Steven Wu
2025	J	`jnl`	From Firewalls to Frontiers: AI Red-Teaming is a Domain-Specific Evolution of Cyber Red-Teaming. CoRR Anusha Sinha, Keltin Grimes, James Lucassen, Michael Feffer, Nathan M. VanHoudnos, Zhiwei Steven Wu, Hoda Heidari
2025	J	`jnl`	Generate-then-Verify: Reconstructing Data from Limited Published Statistics. CoRR Terrance Liu, Eileen Xiao, Pratiksha Thaker, Adam Smith, Zhiwei Steven Wu
2025	A*	`conf`	Inferentially-Private Private Information. WWW Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu
2025	J	`jnl`	Kandinsky Conformal Prediction: Beyond Class- and Covariate-Conditional Coverage. CoRR Konstantina Bairaktari, Jiayun Wu, Zhiwei Steven Wu
2025	J	`jnl`	MarkTune: Improving the Quality-Detectability Trade-off in Open-Weight LLM Watermarking. CoRR Yizhou Zhao, Zhiwei Steven Wu, Adam Block
2025	J	`jnl`	Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks. CoRR Luke Guerdan, Devansh Saxena, Stevie Chancellor, Zhiwei Steven Wu, Kenneth Holstein
2025	J	`jnl`	Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks. Proc. ACM Hum. Comput. Interact. Luke Guerdan, Devansh Saxena, Stevie Chancellor, Zhiwei Steven Wu, Kenneth Holstein
2025	J	`jnl`	Membership Inference Attacks for Unseen Classes. CoRR Pratiksha Thaker, Neil Kale, Zhiwei Steven Wu, Virginia Smith
2025	J	`jnl`	Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information. CoRR Maria-Florina Balcan, Martino Bernasconi, Matteo Castiglioni, Andrea Celli, Keegan Harris, Zhiwei Steven Wu
2025	J	`jnl`	Optimizing Canaries for Privacy Auditing with Metagradient Descent. CoRR Matteo Boglioni, Terrance Liu, Andrew Ilyas, Zhiwei Steven Wu
2025	A*	`conf`	Orthogonal Causal Calibration (Extended Abstract). COLT Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu
2025	J	`jnl`	Persona-Augmented Benchmarking: Evaluating LLMs Across Diverse Writing Styles. CoRR Kimberly Le Truong, Riccardo Fogliato, Hoda Heidari, Zhiwei Steven Wu
2025	J	`jnl`	Persuasive Prediction via Decision Calibration. CoRR Jingwu Tang, Jiahao Zhang, Fei Fang, Zhiwei Steven Wu
2025	—	`conf`	Position: LLM Unlearning Benchmarks are Weak Measures of Progress. SaTML Pratiksha Thaker, Shengyuan Hu, Neil Kale, Yash Maurya, Zhiwei Steven Wu, Virginia Smith
2025	A*	`conf`	Reconciling Model Multiplicity for Downstream Decision Making. ICLR Ally Yalei Du, Dung Daniel T. Ngo, Zhiwei Steven Wu
2025	J	`jnl`	The Cost of Shuffling in Private Gradient Based Optimization. CoRR Shuli Jiang, Pranay Sharma, Zhiwei Steven Wu, Gauri Joshi
2025	A*	`conf`	Time-Uniform Self-Normalized Concentration for Vector-Valued Processes (Extended Abstract). COLT Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas
2025	J	`jnl`	Validating LLM-as-a-Judge Systems in the Absence of Gold Labels. CoRR Luke Guerdan, Solon Barocas, Kenneth Holstein, Hanna M. Wallach, Zhiwei Steven Wu, Alexandra Chouldechova
2025	J	`jnl`	Winning the MIDST Challenge: New Membership Inference Attacks on Diffusion Models for Tabular Data Synthesis. CoRR Xiaoyu Wu, Yifei Pang, Terrance Liu, Zhiwei Steven Wu
2024	J	`jnl`	A Minimaximalist Approach to Reinforcement Learning from Human Feedback. CoRR Gokul Swamy, Christoph Dann, Rahul Kidambi, Zhiwei Steven Wu, Alekh Agarwal
2024	J	`jnl`	Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift. CoRR Jiayun Wu, Jiashuo Liu, Peng Cui, Zhiwei Steven Wu
2024	J	`jnl`	Differentially Private Bayesian Persuasion. CoRR Yuqi Pan, Zhiwei Steven Wu, Haifeng Xu, Shuran Zheng
2024	—	`conf`	Ex-Post Individually Rational Bayesian Persuasion. WINE Jiahao Zhang, Shuran Zheng, Renato Paes Leme, Zhiwei Steven Wu
2024	—	`conf`	Fair Federated Learning via Bounded Group Loss. SaTML Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
2024	J	`jnl`	Hybrid Inverse Reinforcement Learning. CoRR Juntao Ren, Gokul Swamy, Zhiwei Steven Wu, J. Andrew Bagnell, Sanjiban Choudhury
2024	—	`conf`	Improved Differentially Private Regression via Gradient Boosting. SaTML Shuai Tang, Sergül Aydöre, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Zhiwei Steven Wu
2024	J	`jnl`	Inferentially-Private Private Information. CoRR Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu
2024	J	`jnl`	Jogging the Memory of Unlearned Model Through Targeted Relearning Attack. CoRR Shengyuan Hu, Yiwei Fu, Zhiwei Steven Wu, Virginia Smith
2024	A*	`conf`	Multi-Agent Imitation Learning: Value is Easy, Regret is Hard. NeurIPS Jingwu Tang, Gokul Swamy, Fei Fang, Zhiwei Steven Wu
2024	J	`jnl`	Multi-Agent Imitation Learning: Value is Easy, Regret is Hard. CoRR Jingwu Tang, Gokul Swamy, Fei Fang, Zhiwei Steven Wu
2024	J	`jnl`	Multi-group Uncertainty Quantification for Long-form Text Generation. CoRR Terrance Liu, Zhiwei Steven Wu
2024	A*	`conf`	Oracle-Efficient Differentially Private Learning with Public Data. NeurIPS Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty, Zhiwei Steven Wu
2024	J	`jnl`	Oracle-Efficient Differentially Private Learning with Public Data. CoRR Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty, Zhiwei Steven Wu
2024	J	`jnl`	Orthogonal Causal Calibration. CoRR Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu
2024	J	`jnl`	Position: LLM Unlearning Benchmarks are Weak Measures of Progress. CoRR Pratiksha Thaker, Shengyuan Hu, Neil Kale, Yash Maurya, Zhiwei Steven Wu, Virginia Smith
2024	J	`jnl`	Predictive Performance Comparison of Decision Policies Under Confounding. CoRR Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu
2024	J	`jnl`	Provable Multi-Party Reinforcement Learning with Diverse Human Feedback. CoRR Huiying Zhong, Zhun Deng, Weijie J. Su, Zhiwei Steven Wu, Linjun Zhang
2024	J	`jnl`	Quantifying Privacy Risks of Public Statistics to Residents of Subsidized Housing. CoRR Ryan Steed, Diana Qing, Zhiwei Steven Wu
2024	J	`jnl`	Reconciling Model Multiplicity for Downstream Decision Making. CoRR Ally Yalei Du, Dung Daniel T. Ngo, Zhiwei Steven Wu
2024	J	`jnl`	Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable. CoRR Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu
2024	A*	`conf`	Regret Minimization in Stackelberg Games with Side Information. NeurIPS Keegan Harris, Zhiwei Steven Wu, Maria-Florina Balcan
2024	J	`jnl`	Regret Minimization in Stackelberg Games with Side Information. CoRR Keegan Harris, Zhiwei Steven Wu, Maria-Florina Balcan
2024	—	`conf`	Strategyproof Decision-Making in Panel Data Settings and Beyond. SIGMETRICS/Performance (Abstracts) Keegan Harris, Anish Agarwal, Chara Podimata, Zhiwei Steven Wu
2023	A*	`conf`	Adaptive Principal Component Regression with Applications to Panel Data. NeurIPS Anish Agarwal, Keegan Harris, Justin Whitehouse, Zhiwei Steven Wu
2023	J	`jnl`	Adaptive Principal Component Regression with Applications to Panel Data. CoRR Anish Agarwal, Keegan Harris, Justin Whitehouse, Zhiwei Steven Wu
2023	A*	`conf`	Adaptive Privacy Composition for Accuracy-first Mechanisms. NeurIPS Ryan M. Rogers, Gennady Samorodnitsky, Zhiwei Steven Wu, Aaditya Ramdas
2023	J	`jnl`	Adaptive Privacy Composition for Accuracy-first Mechanisms. CoRR Ryan Rogers, Gennady Samorodnitsky, Zhiwei Steven Wu, Aaditya Ramdas
2023	J	`jnl`	Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance. CoRR Xin Gu, Gautam Kamath, Zhiwei Steven Wu
2023	—	`conf`	Counterfactual Prediction Under Outcome Measurement Error. FAccT Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu
2023	J	`jnl`	Counterfactual Prediction Under Outcome Measurement Error. CoRR Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu
2023	J	`jnl`	Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach. CoRR Xinwei Zhang, Zhiqi Bu, Zhiwei Steven Wu, Mingyi Hong
2023	J	`jnl`	Ex-post Individually Rational Bayesian Persuasion. CoRR Jiahao Zhang, Shuran Zheng, Renato Paes Leme, Zhiwei Steven Wu
2023	J	`jnl`	Federated Learning as a Network Effects Game. CoRR Shengyuan Hu, Dung Daniel T. Ngo, Shuran Zheng, Virginia Smith, Zhiwei Steven Wu
2023	J	`jnl`	Generating Private Synthetic Data with Genetic Algorithms. CoRR Terrance Liu, Jingwu Tang, Giuseppe Vietri, Zhiwei Steven Wu
2023	J	`jnl`	Greedy Algorithm Almost Dominates in Smoothed Contextual Bandits. SIAM J. Comput. Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
2023	—	`conf`	Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making. FAccT Luke Guerdan, Amanda Coston, Zhiwei Steven Wu, Kenneth Holstein
2023	J	`jnl`	Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making. CoRR Luke Guerdan, Amanda Coston, Zhiwei Steven Wu, Kenneth Holstein
2023	J	`jnl`	Improved Differentially Private Regression via Gradient Boosting. CoRR Shuai Tang, Sergül Aydöre, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Zhiwei Steven Wu
2023	J	`jnl`	Improved Self-Normalized Concentration in Hilbert Spaces: Sublinear Regret for GP-UCB. CoRR Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas
2023	J	`jnl`	Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration. CoRR Dung Daniel T. Ngo, Keegan Harris, Anish Agarwal, Vasilis Syrgkanis, Zhiwei Steven Wu
2023	A*	`conf`	Inverse Reinforcement Learning without Reinforcement Learning. ICML Gokul Swamy, David Wu, Sanjiban Choudhury, Drew Bagnell, Zhiwei Steven Wu
2023	J	`jnl`	Inverse Reinforcement Learning without Reinforcement Learning. CoRR Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
2023	A*	`conf`	Learning Shared Safety Constraints from Multi-task Demonstrations. NeurIPS Konwoo Kim, Gokul Swamy, Zuxin Liu, Ding Zhao, Sanjiban Choudhury, Zhiwei Steven Wu
2023	J	`jnl`	Learning Shared Safety Constraints from Multi-task Demonstrations. CoRR Konwoo Kim, Gokul Swamy, Zuxin Liu, Ding Zhao, Sanjiban Choudhury, Zhiwei Steven Wu
2023	J	`jnl`	Leveraging Public Representations for Private Transfer Learning. CoRR Pratiksha Thaker, Amrith Setlur, Zhiwei Steven Wu, Virginia Smith
2023	J	`jnl`	Membership Inference Attacks on Diffusion Models via Quantile Regression. CoRR Shuai Tang, Zhiwei Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth
2023	A*	`conf`	Meta-Learning Adversarial Bandit Algorithms. NeurIPS Misha Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu
2023	J	`jnl`	Meta-Learning Adversarial Bandit Algorithms. CoRR Mikhail Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu
2023	A*	`conf`	Nonparametric Extensions of Randomized Response for Private Confidence Sets. ICML Ian Waudby-Smith, Zhiwei Steven Wu, Aaditya Ramdas
2023	A*	`conf`	On the Sublinear Regret of GP-UCB. NeurIPS Justin Whitehouse, Aaditya Ramdas, Zhiwei Steven Wu
2023	A	`conf`	Private Data Stream Analysis for Universal Symmetric Norm Estimation. APPROX/RANDOM Vladimir Braverman, Joel Manning, Zhiwei Steven Wu, Samson Zhou
2023	J	`jnl`	Private Data Stream Analysis for Universal Symmetric Norm Estimation. CoRR Vladimir Braverman, Joel Manning, Zhiwei Steven Wu, Samson Zhou
2023	J	`jnl`	Scalable Membership Inference Attacks via Quantile Regression. CoRR Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu
2023	A*	`conf`	Strategic Apple Tasting. NeurIPS Keegan Harris, Chara Podimata, Zhiwei Steven Wu
2023	J	`jnl`	Strategic Apple Tasting. CoRR Keegan Harris, Chara Podimata, Zhiwei Steven Wu
2022	A	`conf`	"Why Do I Care What's Similar?" Probing Challenges in AI-Assisted Child Welfare Decision-Making through Worker-AI Interface Design Concepts. Conference on Designing Interactive Systems Anna Kawakami, Venkatesh Sivaraman, Logan Stapleton, Hao Fei Cheng, Adam Perer, Zhiwei Steven Wu, Haiyi Zhu, Kenneth Holstein
2022	J	`jnl`	A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms. CoRR Nil-Jana Akpinar, Manish Nagireddy, Logan Stapleton, Hao Fei Cheng, Haiyi Zhu, Zhiwei Steven Wu, Hoda Heidari
2022	A*	`conf`	Bandit Data-Driven Optimization for Crowdsourcing Food Rescue Platforms. AAAI Zheyuan Ryan Shi, Zhiwei Steven Wu, Rayid Ghani, Fei Fang
2022	J	`jnl`	Bayesian Exploration: Incentivizing Exploration in Bayesian Games. Oper. Res. Yishay Mansour, Alex Slivkins, Vasilis Syrgkanis, Zhiwei Steven Wu
2022	A*	`conf`	Bayesian Persuasion for Algorithmic Recourse. NeurIPS Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu
2022	A*	`conf`	Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints. NeurIPS Justin Whitehouse, Aaditya Ramdas, Zhiwei Steven Wu, Ryan M. Rogers
2022	J	`jnl`	Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints. CoRR Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas, Ryan Rogers
2022	J	`jnl`	Causal Imitation Learning under Temporally Correlated Noise. CoRR Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
2022	J	`jnl`	Confidence-Ranked Reconstruction of Census Microdata from Published Statistics. CoRR Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu
2022	A*	`conf`	Constrained Variational Policy Optimization for Safe Reinforcement Learning. ICML Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Zhiwei Steven Wu, Bo Li, Ding Zhao
2022	J	`jnl`	Constrained Variational Policy Optimization for Safe Reinforcement Learning. CoRR Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Zhiwei Steven Wu, Bo Li, Ding Zhao
2022	—	`conf`	Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits. FAccT Wesley Hanwen Deng, Manish Nagireddy, Michelle Seng Ah Lee, Jatinder Singh, Zhiwei Steven Wu, Kenneth Holstein, Haiyi Zhu
2022	J	`jnl`	Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits. CoRR Wesley Hanwen Deng, Manish Nagireddy, Michelle Seng Ah Lee, Jatinder Singh, Zhiwei Steven Wu, Kenneth Holstein, Haiyi Zhu
2022	J	`jnl`	Extended Analysis of "How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions". CoRR Logan Stapleton, Hao Fei Cheng, Anna Kawakami, Venkatesh Sivaraman, Yanghuidi Cheng, Diana Qing, Adam Perer, Kenneth Holstein, Zhiwei Steven Wu, Haiyi Zhu
2022	J	`jnl`	Fully Adaptive Composition in Differential Privacy. CoRR Justin Whitehouse, Aaditya Ramdas, Ryan Rogers, Zhiwei Steven Wu
2022	J	`jnl`	Game-Theoretic Algorithms for Conditional Moment Matching. CoRR Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
2022	A*	`conf`	How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions. CHI Hao Fei Cheng, Logan Stapleton, Anna Kawakami, Venkatesh Sivaraman, Yanghuidi Cheng, Diana Qing, Adam Perer, Kenneth Holstein, Zhiwei Steven Wu, Haiyi Zhu
2022	—	`conf`	Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders. FAccT Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright, Alexandra Chouldechova, Ken Holstein, Zhiwei Steven Wu, Haiyi Zhu
2022	J	`jnl`	Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders. CoRR Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright, Alexandra Chouldechova, Kenneth Holstein, Zhiwei Steven Wu, Haiyi Zhu
2022	J	`jnl`	Improved Regret for Differentially Private Exploration in Linear MDP. CoRR Dung Daniel T. Ngo, Giuseppe Vietri, Zhiwei Steven Wu
2022	A*	`conf`	Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support. CHI Anna Kawakami, Venkatesh Sivaraman, Hao Fei Cheng, Logan Stapleton, Yanghuidi Cheng, Diana Qing, Adam Perer, Zhiwei Steven Wu, Haiyi Zhu, Kenneth Holstein
2022	J	`jnl`	Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support. CoRR Anna Kawakami, Venkatesh Sivaraman, Hao Fei Cheng, Logan Stapleton, Yanghuidi Cheng, Diana Qing, Adam Perer, Zhiwei Steven Wu, Haiyi Zhu, Kenneth Holstein
2022	A*	`conf`	Incentivizing Combinatorial Bandit Exploration. NeurIPS Xinyan Hu, Dung Daniel T. Ngo, Aleksandrs Slivkins, Zhiwei Steven Wu
2022	J	`jnl`	Incentivizing Combinatorial Bandit Exploration. CoRR Xinyan Hu, Dung Daniel T. Ngo, Aleksandrs Slivkins, Zhiwei Steven Wu
2022	A*	`conf`	Information Discrepancy in Strategic Learning. ICML Yahav Bechavod, Chara Podimata, Zhiwei Steven Wu, Juba Ziani
2022	J	`jnl`	Locally private nonparametric confidence intervals and sequences. CoRR Ian Waudby-Smith, Zhiwei Steven Wu, Aaditya Ramdas
2022	J	`jnl`	Meta-Learning Adversarial Bandits. CoRR Maria-Florina Balcan, Keegan Harris, Mikhail Khodak, Zhiwei Steven Wu
2022	J	`jnl`	Meta-Learning in Games. CoRR Keegan Harris, Ioannis Anagnostides, Gabriele Farina, Mikhail Khodak, Zhiwei Steven Wu, Tuomas Sandholm
2022	J	`jnl`	Minimax Optimal Online Imitation Learning via Replay Estimation. CoRR Gokul Swamy, Nived Rajaraman, Matthew Peng, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu, Jiantao Jiao, Kannan Ramchandran
2022	J	`jnl`	On Privacy and Personalization in Cross-Silo Federated Learning. CoRR Ken Ziyu Liu, Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
2022	A*	`conf`	Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. ICML Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu
2022	J	`jnl`	Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization. CoRR Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu
2022	A*	`conf`	Private Synthetic Data for Multitask Learning and Marginal Queries. NeurIPS Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu
2022	J	`jnl`	Private Synthetic Data for Multitask Learning and Marginal Queries. CoRR Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu
2022	J	`jnl`	Private Synthetic Data with Hierarchical Structure. CoRR Terrance Liu, Zhiwei Steven Wu
2022	J	`jnl`	Provably Fair Federated Learning via Bounded Group Loss. CoRR Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
2022	J	`jnl`	Reinforcement Learning with Stepwise Fairness Constraints. CoRR Zhun Deng, He Sun, Zhiwei Steven Wu, Linjun Zhang, David C. Parkes
2022	A*	`conf`	Sequence Model Imitation Learning with Unobserved Contexts. NeurIPS Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
2022	J	`jnl`	Sequence Model Imitation Learning with Unobserved Contexts. CoRR Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
2022	J	`jnl`	Strategyproof Decision-Making in Panel Data Settings and Beyond. CoRR Keegan Harris, Anish Agarwal, Chara Podimata, Zhiwei Steven Wu
2021	J	`jnl`	A Critique of Strictly Batch Imitation Learning. CoRR Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
2021	—	`conf`	An Algorithmic Framework for Fairness Elicitation. FORC Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu
2021	J	`jnl`	Bayesian Persuasion for Algorithmic Recourse. CoRR Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu
2021	A	`conf`	Gaming Helps! Learning from Strategic Interactions in Natural Dynamics. AISTATS Yahav Bechavod, Katrina Ligett, Zhiwei Steven Wu, Juba Ziani
2021	J	`jnl`	Incentivizing Compliance with Algorithmic Instruments. CoRR Dung Daniel T. Ngo, Logan Stapleton, Vasilis Syrgkanis, Zhiwei Steven Wu
2021	J	`jnl`	Information Discrepancy in Strategic Learning. CoRR Yahav Bechavod, Chara Podimata, Zhiwei Steven Wu, Juba Ziani
2021	J	`jnl`	Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods. CoRR Terrance Liu, Giuseppe Vietri, Zhiwei Steven Wu
2021	A	`conf`	Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization. AISTATS Vikas K. Garg, Adam Tauman Kalai, Katrina Ligett, Zhiwei Steven Wu
2021	A*	`conf`	Leveraging Public Data for Practical Private Query Release. ICML Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu
2021	J	`jnl`	Leveraging Public Data for Practical Private Query Release. CoRR Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu
2021	J	`jnl`	Of Moments and Matching: Trade-offs and Treatments in Imitation Learning. CoRR Gokul Swamy, Sanjiban Choudhury, Zhiwei Steven Wu, J. Andrew Bagnell
2021	J	`jnl`	Private Hypothesis Selection. IEEE Trans. Inf. Theory Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu
2021	J	`jnl`	Private Multi-Task Learning: Formulation and Applications to Federated Learning. CoRR Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
2021	A*	`conf`	Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems. CHI Hao Fei Cheng, Logan Stapleton, Ruiqi Wang, Paige Bullock, Alexandra Chouldechova, Zhiwei Steven Wu, Haiyi Zhu
2021	J	`jnl`	Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems. CoRR Hao Fei Cheng, Logan Stapleton, Ruiqi Wang, Paige Bullock, Alexandra Chouldechova, Zhiwei Steven Wu, Haiyi Zhu
2021	A*	`conf`	Stateful Strategic Regression. NeurIPS Keegan Harris, Hoda Heidari, Zhiwei Steven Wu
2021	J	`jnl`	Stateful Strategic Regression. CoRR Keegan Harris, Hoda Heidari, Zhiwei Steven Wu
2021	J	`jnl`	Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses. CoRR Keegan Harris, Dung Daniel T. Ngo, Logan Stapleton, Hoda Heidari, Zhiwei Steven Wu
2021	J	`jnl`	Towards the Unification and Robustness of Perturbation and Gradient Based Explanations. CoRR Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Zhiwei Steven Wu, Himabindu Lakkaraju
2021	J	`jnl`	Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy. CoRR Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu, Jinfeng Yi
2021	—	`conf`	Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation. FAccT Hong Shen, Wesley H. Deng, Aditi Chattopadhyay, Zhiwei Steven Wu, Xu Wang, Haiyi Zhu
2020	J	`jnl`	Bandit Data-driven Optimization: AI for Social Good and Beyond. CoRR Zheyuan Ryan Shi, Zhiwei Steven Wu, Rayid Ghani, Fei Fang
2020	J	`jnl`	Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification. CoRR Yingxue Zhou, Zhiwei Steven Wu, Arindam Banerjee
2020	J	`jnl`	Causal Feature Discovery through Strategic Modification. CoRR Yahav Bechavod, Katrina Ligett, Zhiwei Steven Wu, Juba Ziani
2020	J	`jnl`	Competing Bandits: The Perils of Exploration Under Competition. CoRR Guy Aridor, Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu
2020	A*	`conf`	Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms. NeurIPS Xiangyi Chen, Tiancong Chen, Haoran Sun, Zhiwei Steven Wu, Mingyi Hong
2020	J	`jnl`	Greedy Algorithm almost Dominates in Smoothed Contextual Bandits. CoRR Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
2020	A*	`conf`	Incentivizing Exploration with Selective Data Disclosure. EC Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu
2020	A	`conf`	Keeping Designers in the Loop: Communicating Inherent Algorithmic Trade-offs Across Multiple Objectives. Conference on Designing Interactive Systems Bowen Yu, Ye Yuan, Loren Terveen, Zhiwei Steven Wu, Jodi Forlizzi, Haiyi Zhu
2020	J	`jnl`	Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization. CoRR Vikas K. Garg, Adam Kalai, Katrina Ligett, Zhiwei Steven Wu
2020	A*	`conf`	Locally Private Hypothesis Selection. COLT Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
2020	J	`jnl`	Locally Private Hypothesis Selection. CoRR Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
2020	A*	`conf`	Metric-Free Individual Fairness in Online Learning. NeurIPS Yahav Bechavod, Christopher Jung, Zhiwei Steven Wu
2020	J	`jnl`	Metric-Free Individual Fairness in Online Learning. CoRR Yahav Bechavod, Christopher Jung, Zhiwei Steven Wu
2020	J	`jnl`	Multidimensional Dynamic Pricing for Welfare Maximization. ACM Trans. Economics and Comput. Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu
2020	A*	`conf`	New Oracle-Efficient Algorithms for Private Synthetic Data Release. ICML Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Zhiwei Steven Wu
2020	J	`jnl`	New Oracle-Efficient Algorithms for Private Synthetic Data Release. CoRR Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Zhiwei Steven Wu
2020	A*	`conf`	Oracle Efficient Private Non-Convex Optimization. ICML Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu
2020	J	`jnl`	Private Post-GAN Boosting. CoRR Marcel Neunhoeffer, Zhiwei Steven Wu, Cynthia Dwork
2020	A*	`conf`	Private Query Release Assisted by Public Data. ICML Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, Zhiwei Steven Wu
2020	J	`jnl`	Private Query Release Assisted by Public Data. CoRR Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, Zhiwei Steven Wu
2020	A*	`conf`	Private Reinforcement Learning with PAC and Regret Guarantees. ICML Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Zhiwei Steven Wu
2020	J	`jnl`	Private Reinforcement Learning with PAC and Regret Guarantees. CoRR Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Zhiwei Steven Wu
2020	J	`jnl`	Private Stochastic Non-Convex Optimization: Adaptive Algorithms and Tighter Generalization Bounds. CoRR Yingxue Zhou, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu, Arindam Banerjee
2020	A*	`conf`	Privately Learning Markov Random Fields. ICML Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu
2020	J	`jnl`	Privately Learning Markov Random Fields. CoRR Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu
2020	A*	`conf`	Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis. ICML Vidyashankar Sivakumar, Zhiwei Steven Wu, Arindam Banerjee
2020	J	`jnl`	Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis. CoRR Vidyashankar Sivakumar, Zhiwei Steven Wu, Arindam Banerjee
2020	A*	`conf`	Understanding Gradient Clipping in Private SGD: A Geometric Perspective. NeurIPS Xiangyi Chen, Zhiwei Steven Wu, Mingyi Hong
2020	J	`jnl`	Understanding Gradient Clipping in Private SGD: A Geometric Perspective. CoRR Xiangyi Chen, Zhiwei Steven Wu, Mingyi Hong
2020	J	`jnl`	Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation. CoRR Hong Shen, Wesley Deng, Aditi Chattopadhyay, Zhiwei Steven Wu, Xu Wang, Haiyi Zhu
2019	J	`jnl`	Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM. J. Priv. Confidentiality Zhiwei Steven Wu, Aaron Roth, Katrina Ligett, Bo Waggoner, Seth Neel
2019	—	`conf`	An Empirical Study of Rich Subgroup Fairness for Machine Learning. FAT Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
2019	A*	`conf`	Bayesian Exploration with Heterogeneous Agents. WWW Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu
2019	J	`jnl`	Bayesian Exploration with Heterogeneous Agents. CoRR Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu
2019	J	`jnl`	Competing Bandits: The Perils of Exploration under Competition. CoRR Guy Aridor, Kevin Liu, Aleksandrs Slivkins, Zhiwei Steven Wu
2019	J	`jnl`	Designing Interfaces to Help Stakeholders Comprehend, Navigate, and Manage Algorithmic Trade-Offs. CoRR Bowen Yu, Ye Yuan, Loren Terveen, Zhiwei Steven Wu, Haiyi Zhu
2019	J	`jnl`	Differentially Private Objective Perturbation: Beyond Smoothness and Convexity. CoRR Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu
2019	J	`jnl`	Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms. CoRR Xiangyi Chen, Tiancong Chen, Haoran Sun, Zhiwei Steven Wu, Mingyi Hong
2019	J	`jnl`	Eliciting and Enforcing Subjective Individual Fairness. CoRR Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu
2019	A*	`conf`	Equal Opportunity in Online Classification with Partial Feedback. NeurIPS Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
2019	J	`jnl`	Equal Opportunity in Online Classification with Partial Feedback. CoRR Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
2019	A*	`conf`	Fair Regression: Quantitative Definitions and Reduction-Based Algorithms. ICML Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu
2019	J	`jnl`	Fair Regression: Quantitative Definitions and Reduction-based Algorithms. CoRR Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu
2019	A*	`conf`	How to Use Heuristics for Differential Privacy. FOCS Seth Neel, Aaron Roth, Zhiwei Steven Wu
2019	A*	`conf`	Locally Private Bayesian Inference for Count Models. ICML Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, Hanna M. Wallach
2019	A*	`conf`	Locally Private Gaussian Estimation. NeurIPS Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu
2019	J	`jnl`	Logarithmic Query Complexity for Approximate Nash Computation in Large Games. Theory Comput. Syst. Paul W. Goldberg, Francisco J. Marmolejo Cossío, Zhiwei Steven Wu
2019	A*	`conf`	Orthogonal Random Forest for Causal Inference. ICML Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu
2019	A*	`conf`	Private Hypothesis Selection. NeurIPS Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu
2019	J	`jnl`	Private Hypothesis Selection. CoRR Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu
2019	A*	`conf`	Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond. NeurIPS Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar, Zhiwei Steven Wu
2019	J	`jnl`	Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond. CoRR Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar, Zhiwei Steven Wu
2019	A*	`conf`	The Perils of Exploration under Competition: A Computational Modeling Approach. EC Guy Aridor, Kevin Liu, Aleksandrs Slivkins, Zhiwei Steven Wu
2018	A*	`conf`	A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem. NeurIPS Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
2018	J	`jnl`	A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem. CoRR Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
2018	J	`jnl`	An Empirical Study of Rich Subgroup Fairness for Machine Learning. CoRR Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
2018	A	`conf`	Competing Bandits: Learning Under Competition. ITCS Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu
2018	J	`jnl`	How to Use Heuristics for Differential Privacy. CoRR Seth Neel, Aaron Roth, Zhiwei Steven Wu
2018	J	`jnl`	Incentivizing Exploration with Unbiased Histories. CoRR Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu
2018	J	`jnl`	Locally Private Bayesian Inference for Count Models. CoRR Aaron Schein, Zhiwei Steven Wu, Mingyuan Zhou, Hanna M. Wallach
2018	J	`jnl`	Locally Private Gaussian Estimation. CoRR Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu
2018	J	`jnl`	Orthogonal Random Forest for Heterogeneous Treatment Effect Estimation. CoRR Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu
2018	A*	`conf`	Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. ICML Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
2018	J	`jnl`	Privacy-Preserving Distributed Deep Learning for Clinical Data. CoRR Brett K. Beaulieu-Jones, William Yuan, Samuel G. Finlayson, Zhiwei Steven Wu
2018	A*	`conf`	Semiparametric Contextual Bandits. ICML Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
2018	J	`jnl`	Semiparametric Contextual Bandits. CoRR Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
2018	A*	`conf`	Strategic Classification from Revealed Preferences. EC Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, Zhiwei Steven Wu
2018	A*	`conf`	The Externalities of Exploration and How Data Diversity Helps Exploitation. COLT Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
2018	J	`jnl`	The Externalities of Exploration and How Data Diversity Helps Exploitation. CoRR Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
2017	—	`conf`	Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM. NIPS Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
2017	J	`jnl`	Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM. CoRR Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
2017	J	`jnl`	Competing Bandits: Learning under Competition. CoRR Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu
2017	A*	`conf`	Fairness Incentives for Myopic Agents. EC Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu
2017	J	`jnl`	Fairness Incentives for Myopic Agents. CoRR Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu
2017	A*	`conf`	Meritocratic Fairness for Cross-Population Selection. ICML Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu
2017	A*	`conf`	Multidimensional Dynamic Pricing for Welfare Maximization. EC Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu
2017	A*	`conf`	Predicting with Distributions. COLT Michael J. Kearns, Zhiwei Steven Wu
2017	J	`jnl`	Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. CoRR Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
2017	J	`jnl`	Strategic Classification from Revealed Preferences. CoRR Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, Zhiwei Steven Wu
2016	A*	`conf`	Adaptive Learning with Robust Generalization Guarantees. COLT Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu
2016	J	`jnl`	Adaptive Learning with Robust Generalization Guarantees. CoRR Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu
2016	A*	`conf`	Bayesian Exploration: Incentivizing Exploration in Bayesian Games. EC Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis, Zhiwei Steven Wu
2016	J	`jnl`	Bayesian Exploration: Incentivizing Exploration in Bayesian Games. CoRR Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis, Zhiwei Steven Wu
2016	A	`conf`	Coordination Complexity: Small Information Coordinating Large Populations. ITCS Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, Zhiwei Steven Wu
2016	J	`jnl`	Dual Query: Practical Private Query Release for High Dimensional Data. J. Priv. Confidentiality Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu
2016	A*	`conf`	Jointly Private Convex Programming. SODA Justin Hsu, Zhiyi Huang, Aaron Roth, Zhiwei Steven Wu
2016	—	`conf`	Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs. NIPS Shahin Jabbari, Ryan M. Rogers, Aaron Roth, Zhiwei Steven Wu
2016	B	`conf`	Logarithmic Query Complexity for Approximate Nash Computation in Large Games. SAGT Paul W. Goldberg, Francisco J. Marmolejo Cossío, Zhiwei Steven Wu
2016	J	`jnl`	Logarithmic Query Complexity for Approximate Nash Computation in Large Games. CoRR Paul W. Goldberg, Francisco J. Marmolejo Cossío, Zhiwei Steven Wu
2016	J	`jnl`	Multidimensional Dynamic Pricing for Welfare Maximization. CoRR Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu
2016	J	`jnl`	Predicting with Distributions. CoRR Michael J. Kearns, Zhiwei Steven Wu
2016	J	`jnl`	Private Matchings and Allocations. SIAM J. Comput. Justin Hsu, Zhiyi Huang, Aaron Roth, Tim Roughgarden, Zhiwei Steven Wu
2016	J	`jnl`	Private algorithms for the protected in social network search. Proc. Natl. Acad. Sci. USA Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev
2016	A*	`conf`	Watch and learn: optimizing from revealed preferences feedback. STOC Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu
2015	A	`conf`	Accuracy for Sale: Aggregating Data with a Variance Constraint. ITCS Rachel Cummings, Katrina Ligett, Aaron Roth, Zhiwei Steven Wu, Juba Ziani
2015	A*	`conf`	Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy). SODA Sampath Kannan, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu
2015	J	`jnl`	Coordination Complexity: Small Information Coordinating Large Populations. CoRR Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, Zhiwei Steven Wu
2015	A*	`conf`	Inducing Approximately Optimal Flow Using Truthful Mediators. EC Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu
2015	J	`jnl`	Inducing Approximately Optimal Flow Using Truthful Mediators. CoRR Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu
2015	J	`jnl`	Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs. CoRR Shahin Jabbari, Ryan M. Rogers, Aaron Roth, Zhiwei Steven Wu
2015	—	`conf`	Privacy and Truthful Equilibrium Selection for Aggregative Games. WINE Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu
2015	J	`jnl`	Privacy for the Protected (Only). CoRR Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev
2015	J	`jnl`	Watch and Learn: Optimizing from Revealed Preferences Feedback. CoRR Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu
2015	J	`jnl`	Watch and learn: optimizing from revealed preferences feedback. SIGecom Exch. Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu
2014	J	`jnl`	Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy). CoRR Sampath Kannan, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu
2014	A*	`conf`	Dual Query: Practical Private Query Release for High Dimensional Data. ICML Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu
2014	J	`jnl`	Dual Query: Practical Private Query Release for High Dimensional Data. CoRR Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu
2014	J	`jnl`	Jointly Private Convex Programming. CoRR Justin Hsu, Zhiyi Huang, Aaron Roth, Zhiwei Steven Wu
2014	J	`jnl`	Privacy and Truthful Equilibrium Selection for Aggregative Games. CoRR Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu
2014	A*	`conf`	Private matchings and allocations. STOC Justin Hsu, Zhiyi Huang, Aaron Roth, Tim Roughgarden, Zhiwei Steven Wu
2013	J	`jnl`	Private Matchings and Allocations. CoRR Justin Hsu, Zhiyi Huang, Aaron Roth, Tim Roughgarden, Zhiwei Steven Wu