Zhiwei Steven Wu

ORCID

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 199

Top Venues

CoRR

ICML

NeurIPS

Publications

Jiayun Wu, Jiashuo Liu, Peng Cui, Zhiwei Steven Wu
Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift. CoRR (2024)
Shengyuan Hu, Yiwei Fu, Zhiwei Steven Wu, Virginia Smith
Jogging the Memory of Unlearned Model Through Targeted Relearning Attack. CoRR (2024)
Yuqi Pan, Zhiwei Steven Wu, Haifeng Xu, Shuran Zheng
Differentially Private Bayesian Persuasion. CoRR (2024)
Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu
Orthogonal Causal Calibration. CoRR (2024)
Juntao Ren, Gokul Swamy, Zhiwei Steven Wu, J. Andrew Bagnell, Sanjiban Choudhury
Hybrid Inverse Reinforcement Learning. CoRR (2024)
Jingwu Tang, Gokul Swamy, Fei Fang, Zhiwei Steven Wu
Multi-Agent Imitation Learning: Value is Easy, Regret is Hard. CoRR (2024)
Ryan Steed, Diana Qing, Zhiwei Steven Wu
Quantifying Privacy Risks of Public Statistics to Residents of Subsidized Housing. CoRR (2024)
Huiying Zhong, Zhun Deng, Weijie J. Su, Zhiwei Steven Wu, Linjun Zhang
Provable Multi-Party Reinforcement Learning with Diverse Human Feedback. CoRR (2024)
Gokul Swamy, Christoph Dann, Rahul Kidambi, Zhiwei Steven Wu, Alekh Agarwal
A Minimaximalist Approach to Reinforcement Learning from Human Feedback. CoRR (2024)
David Wu, Gokul Swamy, J. Andrew Bagnell, Zhiwei Steven Wu, Sanjiban Choudhury
The Virtues of Pessimism in Inverse Reinforcement Learning. CoRR (2024)
Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
Fair Federated Learning via Bounded Group Loss. SaTML (2024)
Keegan Harris, Anish Agarwal, Chara Podimata, Zhiwei Steven Wu
Strategyproof Decision-Making in Panel Data Settings and Beyond. SIGMETRICS/Performance (Abstracts) (2024)
Shuai Tang, Sergül Aydöre, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Zhiwei Steven Wu
Improved Differentially Private Regression via Gradient Boosting. SaTML (2024)
Keegan Harris, Zhiwei Steven Wu, Maria-Florina Balcan
Regret Minimization in Stackelberg Games with Side Information. CoRR (2024)
Terrance Liu, Zhiwei Steven Wu
Multi-group Uncertainty Quantification for Long-form Text Generation. CoRR (2024)
Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable. CoRR (2024)
Ally Yalei Du, Dung Daniel T. Ngo, Zhiwei Steven Wu
Reconciling Model Multiplicity for Downstream Decision Making. CoRR (2024)
Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu
Predictive Performance Comparison of Decision Policies Under Confounding. CoRR (2024)
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
Greedy Algorithm Almost Dominates in Smoothed Contextual Bandits. SIAM J. Comput. 52 (2) (2023)
Vladimir Braverman, Joel Manning, Zhiwei Steven Wu, Samson Zhou
Private Data Stream Analysis for Universal Symmetric Norm Estimation. CoRR (2023)
Vladimir Braverman, Joel Manning, Zhiwei Steven Wu, Samson Zhou
Private Data Stream Analysis for Universal Symmetric Norm Estimation. APPROX/RANDOM (2023)
Shengyuan Hu, Dung Daniel T. Ngo, Shuran Zheng, Virginia Smith, Zhiwei Steven Wu
Federated Learning as a Network Effects Game. CoRR (2023)
Xinwei Zhang, Zhiqi Bu, Zhiwei Steven Wu, Mingyi Hong
Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach. CoRR (2023)
Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas
Improved Self-Normalized Concentration in Hilbert Spaces: Sublinear Regret for GP-UCB. CoRR (2023)
Martin Bertran, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu
Scalable Membership Inference Attacks via Quantile Regression. CoRR (2023)
Luke Guerdan, Amanda Coston, Zhiwei Steven Wu, Kenneth Holstein
Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making. CoRR (2023)
Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu
Counterfactual Prediction Under Outcome Measurement Error. FAccT (2023)
Mikhail Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu
Meta-Learning Adversarial Bandit Algorithms. CoRR (2023)
Pratiksha Thaker, Amrith Setlur, Zhiwei Steven Wu, Virginia Smith
Leveraging Public Representations for Private Transfer Learning. CoRR (2023)
Keegan Harris, Chara Podimata, Zhiwei Steven Wu
Strategic Apple Tasting. CoRR (2023)
Anish Agarwal, Keegan Harris, Justin Whitehouse, Zhiwei Steven Wu
Adaptive Principal Component Regression with Applications to Panel Data. CoRR (2023)
Luke Guerdan, Amanda Coston, Zhiwei Steven Wu, Kenneth Holstein
Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making. FAccT (2023)
Justin Whitehouse, Aaditya Ramdas, Zhiwei Steven Wu
On the Sublinear Regret of GP-UCB. NeurIPS (2023)
Ian Waudby-Smith, Zhiwei Steven Wu, Aaditya Ramdas
Nonparametric Extensions of Randomized Response for Private Confidence Sets. ICML (2023)
Dung Daniel T. Ngo, Keegan Harris, Anish Agarwal, Vasilis Syrgkanis, Zhiwei Steven Wu
Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration. CoRR (2023)
Shuai Tang, Sergül Aydöre, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Zhiwei Steven Wu
Improved Differentially Private Regression via Gradient Boosting. CoRR (2023)
Ryan Rogers, Gennady Samorodnitsky, Zhiwei Steven Wu, Aaditya Ramdas
Adaptive Privacy Composition for Accuracy-first Mechanisms. CoRR (2023)
Jiahao Zhang, Shuran Zheng, Renato Paes Leme, Zhiwei Steven Wu
Ex-post Individually Rational Bayesian Persuasion. CoRR (2023)
Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu
Counterfactual Prediction Under Outcome Measurement Error. CoRR (2023)
Xin Gu, Gautam Kamath, Zhiwei Steven Wu
Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance. CoRR (2023)
Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
Inverse Reinforcement Learning without Reinforcement Learning. CoRR (2023)
Terrance Liu, Jingwu Tang, Giuseppe Vietri, Zhiwei Steven Wu
Generating Private Synthetic Data with Genetic Algorithms. CoRR (2023)
Konwoo Kim, Gokul Swamy, Zuxin Liu, Ding Zhao, Sanjiban Choudhury, Zhiwei Steven Wu
Learning Shared Safety Constraints from Multi-task Demonstrations. CoRR (2023)
Shuai Tang, Zhiwei Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth
Membership Inference Attacks on Diffusion Models via Quantile Regression. CoRR (2023)
Gokul Swamy, David Wu, Sanjiban Choudhury, Drew Bagnell, Zhiwei Steven Wu
Inverse Reinforcement Learning without Reinforcement Learning. ICML (2023)
Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
Provably Fair Federated Learning via Bounded Group Loss. CoRR (2022)
Giuseppe Vietri, Cédric Archambeau, Sergül Aydore, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu
Private Synthetic Data for Multitask Learning and Marginal Queries. CoRR (2022)
Logan Stapleton, Hao Fei Cheng, Anna Kawakami, Venkatesh Sivaraman, Yanghuidi Cheng, Diana Qing, Adam Perer, Kenneth Holstein, Zhiwei Steven Wu, Haiyi Zhu
Extended Analysis of "How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions". CoRR (2022)
Anna Kawakami, Venkatesh Sivaraman, Hao Fei Cheng, Logan Stapleton, Yanghuidi Cheng, Diana Qing, Adam Perer, Zhiwei Steven Wu, Haiyi Zhu, Kenneth Holstein
Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support. CoRR (2022)
Justin Whitehouse, Aaditya Ramdas, Zhiwei Steven Wu, Ryan M. Rogers
Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints. NeurIPS (2022)
Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright, Alexandra Chouldechova, Ken Holstein, Zhiwei Steven Wu, Haiyi Zhu
Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders. FAccT (2022)
Keegan Harris, Anish Agarwal, Chara Podimata, Zhiwei Steven Wu
Strategyproof Decision-Making in Panel Data Settings and Beyond. CoRR (2022)
Wesley Hanwen Deng, Manish Nagireddy, Michelle Seng Ah Lee, Jatinder Singh, Zhiwei Steven Wu, Kenneth Holstein, Haiyi Zhu
Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits. FAccT (2022)
Maria-Florina Balcan, Keegan Harris, Mikhail Khodak, Zhiwei Steven Wu
Meta-Learning Adversarial Bandits. CoRR (2022)
Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
Game-Theoretic Algorithms for Conditional Moment Matching. CoRR (2022)
Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
Causal Imitation Learning under Temporally Correlated Noise. CoRR (2022)
Terrance Liu, Zhiwei Steven Wu
Private Synthetic Data with Hierarchical Structure. CoRR (2022)
Ziyu Liu, Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
On Privacy and Personalization in Cross-Silo Federated Learning. CoRR (2022)
Hao Fei Cheng, Logan Stapleton, Anna Kawakami, Venkatesh Sivaraman, Yanghuidi Cheng, Diana Qing, Adam Perer, Kenneth Holstein, Zhiwei Steven Wu, Haiyi Zhu
How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions. CHI (2022)
Keegan Harris, Ioannis Anagnostides, Gabriele Farina, Mikhail Khodak, Zhiwei Steven Wu, Tuomas Sandholm
Meta-Learning in Games. CoRR (2022)
Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu
Confidence-Ranked Reconstruction of Census Microdata from Published Statistics. CoRR (2022)
Dung Daniel T. Ngo, Giuseppe Vietri, Zhiwei Steven Wu
Improved Regret for Differentially Private Exploration in Linear MDP. CoRR (2022)
Anna Kawakami, Venkatesh Sivaraman, Logan Stapleton, Hao Fei Cheng, Adam Perer, Zhiwei Steven Wu, Haiyi Zhu, Kenneth Holstein
"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 (2022)
Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. ICML (2022)
Xinyan Hu, Dung Daniel T. Ngo, Aleksandrs Slivkins, Zhiwei Steven Wu
Incentivizing Combinatorial Bandit Exploration. CoRR (2022)
Zhun Deng, He Sun, Zhiwei Steven Wu, Linjun Zhang, David C. Parkes
Reinforcement Learning with Stepwise Fairness Constraints. CoRR (2022)
Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
Sequence Model Imitation Learning with Unobserved Contexts. NeurIPS (2022)
Wesley Hanwen Deng, Manish Nagireddy, Michelle Seng Ah Lee, Jatinder Singh, Zhiwei Steven Wu, Kenneth Holstein, Haiyi Zhu
Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits. CoRR (2022)
Xinyan Hu, Dung Daniel T. Ngo, Aleksandrs Slivkins, Zhiwei Steven Wu
Incentivizing Combinatorial Bandit Exploration. NeurIPS (2022)
Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas, Ryan Rogers
Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints. CoRR (2022)
Nil-Jana Akpinar, Manish Nagireddy, Logan Stapleton, Hao Fei Cheng, Haiyi Zhu, Zhiwei Steven Wu, Hoda Heidari
A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms. CoRR (2022)
Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
Sequence Model Imitation Learning with Unobserved Contexts. CoRR (2022)
Anna Kawakami, Venkatesh Sivaraman, Hao Fei Cheng, Logan Stapleton, Yanghuidi Cheng, Diana Qing, Adam Perer, Zhiwei Steven Wu, Haiyi Zhu, Kenneth Holstein
Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support. CHI (2022)
Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Zhiwei Steven Wu, Bo Li, Ding Zhao
Constrained Variational Policy Optimization for Safe Reinforcement Learning. CoRR (2022)
Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu
Private Synthetic Data for Multitask Learning and Marginal Queries. NeurIPS (2022)
Justin Whitehouse, Aaditya Ramdas, Ryan Rogers, Zhiwei Steven Wu
Fully Adaptive Composition in Differential Privacy. CoRR (2022)
Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization. CoRR (2022)
Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu
Bayesian Persuasion for Algorithmic Recourse. NeurIPS (2022)
Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright, Alexandra Chouldechova, Kenneth Holstein, Zhiwei Steven Wu, Haiyi Zhu
Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders. CoRR (2022)
Gokul Swamy, Nived Rajaraman, Matthew Peng, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu, Jiantao Jiao, Kannan Ramchandran
Minimax Optimal Online Imitation Learning via Replay Estimation. CoRR (2022)
Yahav Bechavod, Chara Podimata, Zhiwei Steven Wu, Juba Ziani
Information Discrepancy in Strategic Learning. ICML (2022)
Zheyuan Ryan Shi, Zhiwei Steven Wu, Rayid Ghani, Fei Fang
Bandit Data-Driven Optimization for Crowdsourcing Food Rescue Platforms. AAAI (2022)
Yishay Mansour, Alex Slivkins, Vasilis Syrgkanis, Zhiwei Steven Wu
Bayesian Exploration: Incentivizing Exploration in Bayesian Games. Oper. Res. 70 (2) (2022)
Ian Waudby-Smith, Zhiwei Steven Wu, Aaditya Ramdas
Locally private nonparametric confidence intervals and sequences. CoRR (2022)
Gokul Swamy, Sanjiban Choudhury, Zhiwei Steven Wu, J. Andrew Bagnell
Of Moments and Matching: Trade-offs and Treatments in Imitation Learning. CoRR (2021)
Hao Fei Cheng, Logan Stapleton, Ruiqi Wang, Paige Bullock, Alexandra Chouldechova, Zhiwei Steven Wu, Haiyi Zhu
Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems. CoRR (2021)
Keegan Harris, Dung Daniel T. Ngo, Logan Stapleton, Hoda Heidari, Zhiwei Steven Wu
Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses. CoRR (2021)
Hao Fei Cheng, Logan Stapleton, Ruiqi Wang, Paige Bullock, Alexandra Chouldechova, Zhiwei Steven Wu, Haiyi Zhu
Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems. CHI (2021)
Vikas K. Garg, Adam Tauman Kalai, Katrina Ligett, Zhiwei Steven Wu
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization. AISTATS (2021)
Keegan Harris, Hoda Heidari, Zhiwei Steven Wu
Stateful Strategic Regression. NeurIPS (2021)
Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith
Private Multi-Task Learning: Formulation and Applications to Federated Learning. CoRR (2021)
Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu
An Algorithmic Framework for Fairness Elicitation. FORC (2021)
Dung Daniel T. Ngo, Logan Stapleton, Vasilis Syrgkanis, Zhiwei Steven Wu
Incentivizing Compliance with Algorithmic Instruments. CoRR (2021)
Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu, Jinfeng Yi
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy. CoRR (2021)
Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu
Leveraging Public Data for Practical Private Query Release. CoRR (2021)
Yahav Bechavod, Katrina Ligett, Zhiwei Steven Wu, Juba Ziani
Gaming Helps! Learning from Strategic Interactions in Natural Dynamics. AISTATS (2021)
Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu
Private Hypothesis Selection. IEEE Trans. Inf. Theory 67 (3) (2021)
Hong Shen, Wesley H. Deng, Aditi Chattopadhyay, Zhiwei Steven Wu, Xu Wang, Haiyi Zhu
Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation. FAccT (2021)
Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu
A Critique of Strictly Batch Imitation Learning. CoRR (2021)
Keegan Harris, Hoda Heidari, Zhiwei Steven Wu
Stateful Strategic Regression. CoRR (2021)

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Publication Activity (10 Years)

Top Topics

Top Venues

Zhiwei Steven Wu