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ORCIDZhiwei Steven Wu
Steven Wu 0001
Person information
- affiliation: Carnegie Mellon University, Institute for Software Research, Pittsburgh, PA, USA
- affiliation: University of Minnesota, Department of Computer Science and Engineering, Minneapolis, MN, USA
- affiliation: Microsoft Research, New York City, NY, USA
- affiliation (PhD 2017): University of Pennsylvania, Department of Computer and Information Science, Philadelphia, PA, USA
Other persons with the same name
- Steven Wu — disambiguation page
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2020 – today
- 2024
[j12]Satyapriya Krishna, Tessa Han, Alex Gu, Steven Wu, Shahin Jabbari, Himabindu Lakkaraju:
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective. Trans. Mach. Learn. Res. 2024 (2024)- [c104]Xinwei Zhang, Zhiqi Bu, Steven Wu, Mingyi Hong:
Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach. ICLR 2024 - [c103]Luke Guerdan, Amanda Coston, Ken Holstein, Steven Wu:
Predictive Performance Comparison of Decision Policies Under Confounding. ICML 2024 - [c102]Juntao Ren, Gokul Swamy, Steven Wu, Drew Bagnell, Sanjiban Choudhury:
Hybrid Inverse Reinforcement Learning. ICML 2024 - [c101]Gokul Swamy, Christoph Dann, Rahul Kidambi, Steven Wu, Alekh Agarwal:
A Minimaximalist Approach to Reinforcement Learning from Human Feedback. ICML 2024 - [c100]Shuai Tang, Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth:
Membership Inference Attacks on Diffusion Models via Quantile Regression. ICML 2024 - [c99]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: 33-56 - [c98]Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith:
Fair Federated Learning via Bounded Group Loss. SaTML 2024: 140-160 - [c97]Keegan Harris
, Anish Agarwal, Chara Podimata, Zhiwei Steven Wu:
Strategyproof Decision-Making in Panel Data Settings and Beyond. SIGMETRICS/Performance (Abstracts) 2024: 69-70 - [i130]Gokul Swamy, Christoph Dann, Rahul Kidambi, Zhiwei Steven Wu, Alekh Agarwal:
A Minimaximalist Approach to Reinforcement Learning from Human Feedback. CoRR abs/2401.04056 (2024) - [i129]Keegan Harris, Zhiwei Steven Wu, Maria-Florina Balcan:
Regret Minimization in Stackelberg Games with Side Information. CoRR abs/2402.08576 (2024) - [i128]Juntao Ren, Gokul Swamy, Zhiwei Steven Wu, J. Andrew Bagnell, Sanjiban Choudhury:
Hybrid Inverse Reinforcement Learning. CoRR abs/2402.08848 (2024) - [i127]Yuqi Pan, Zhiwei Steven Wu, Haifeng Xu, Shuran Zheng:
Differentially Private Bayesian Persuasion. CoRR abs/2402.15872 (2024) - [i126]Huiying Zhong, Zhun Deng, Weijie J. Su, Zhiwei Steven Wu, Linjun Zhang:
Provable Multi-Party Reinforcement Learning with Diverse Human Feedback. CoRR abs/2403.05006 (2024) - [i125]Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu:
Predictive Performance Comparison of Decision Policies Under Confounding. CoRR abs/2404.00848 (2024) - [i124]Ally Yalei Du, Dung Daniel T. Ngo, Zhiwei Steven Wu:
Reconciling Model Multiplicity for Downstream Decision Making. CoRR abs/2405.19667 (2024) - [i123]Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable. CoRR abs/2405.20272 (2024) - [i122]Jiayun Wu, Jiashuo Liu, Peng Cui, Zhiwei Steven Wu:
Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift. CoRR abs/2406.00661 (2024) - [i121]Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu:
Orthogonal Causal Calibration. CoRR abs/2406.01933 (2024) - [i120]Jingwu Tang, Gokul Swamy, Fei Fang, Zhiwei Steven Wu:
Multi-Agent Imitation Learning: Value is Easy, Regret is Hard. CoRR abs/2406.04219 (2024) - [i119]Shengyuan Hu, Yiwei Fu, Zhiwei Steven Wu, Virginia Smith:
Jogging the Memory of Unlearned Model Through Targeted Relearning Attack. CoRR abs/2406.13356 (2024) - [i118]Ryan Steed, Diana Qing, Zhiwei Steven Wu:
Quantifying Privacy Risks of Public Statistics to Residents of Subsidized Housing. CoRR abs/2407.04776 (2024) - [i117]Terrance Liu, Zhiwei Steven Wu:
Multi-group Uncertainty Quantification for Long-form Text Generation. CoRR abs/2407.21057 (2024) - 2023
- [j11]Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu:
Greedy Algorithm Almost Dominates in Smoothed Contextual Bandits. SIAM J. Comput. 52(2): 487-524 (2023) - [j10]Shengyuan Hu, Steven Wu, Virginia Smith:
Private Multi-Task Learning: Formulation and Applications to Federated Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c96]Zhun Deng, He Sun, Steven Wu, Linjun Zhang, David C. Parkes:
Reinforcement Learning with Stepwise Fairness Constraints. AISTATS 2023: 10594-10618 - [c95]Vladimir Braverman, Joel Manning, Zhiwei Steven Wu, Samson Zhou:
Private Data Stream Analysis for Universal Symmetric Norm Estimation. APPROX/RANDOM 2023: 45:1-45:24 - [c94]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: 688-704 - [c93]Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu:
Counterfactual Prediction Under Outcome Measurement Error. FAccT 2023: 1584-1598 - [c92]Keegan Harris, Ioannis Anagnostides, Gabriele Farina, Mikhail Khodak, Steven Wu, Tuomas Sandholm:
Meta-Learning in Games. ICLR 2023 - [c91]Terrance Liu, Jingwu Tang, Giuseppe Vietri, Steven Wu:
Generating Private Synthetic Data with Genetic Algorithms. ICML 2023: 22009-22027 - [c90]Gokul Swamy, David Wu, Sanjiban Choudhury, Drew Bagnell, Zhiwei Steven Wu:
Inverse Reinforcement Learning without Reinforcement Learning. ICML 2023: 33299-33318 - [c89]Ian Waudby-Smith, Zhiwei Steven Wu, Aaditya Ramdas:
Nonparametric Extensions of Randomized Response for Private Confidence Sets. ICML 2023: 36748-36789 - [c88]Justin Whitehouse, Aaditya Ramdas, Ryan Rogers, Steven Wu:
Fully-Adaptive Composition in Differential Privacy. ICML 2023: 36990-37007 - [c87]Anish Agarwal, Keegan Harris, Justin Whitehouse, Zhiwei Steven Wu:
Adaptive Principal Component Regression with Applications to Panel Data. NeurIPS 2023 - [c86]Martín Bertrán, Shuai Tang, Aaron Roth, Michael Kearns, Jamie Morgenstern, Steven Wu:
Scalable Membership Inference Attacks via Quantile Regression. NeurIPS 2023 - [c85]Keegan Harris, Chara Podimata, Zhiwei Steven Wu:
Strategic Apple Tasting. NeurIPS 2023 - [c84]Misha Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu:
Meta-Learning Adversarial Bandit Algorithms. NeurIPS 2023 - [c83]Konwoo Kim, Gokul Swamy, Zuxin Liu, Ding Zhao, Sanjiban Choudhury, Zhiwei Steven Wu:
Learning Shared Safety Constraints from Multi-task Demonstrations. NeurIPS 2023 - [c82]Ryan M. Rogers, Gennady Samorodnitsky, Zhiwei Steven Wu, Aaditya Ramdas:
Adaptive Privacy Composition for Accuracy-first Mechanisms. NeurIPS 2023 - [c81]Justin Whitehouse, Aaditya Ramdas, Zhiwei Steven Wu:
On the Sublinear Regret of GP-UCB. NeurIPS 2023 - [i116]Luke Guerdan, Amanda Coston, Zhiwei Steven Wu, Kenneth Holstein:
Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making. CoRR abs/2302.06503 (2023) - [i115]Shengyuan Hu, Dung Daniel T. Ngo, Shuran Zheng, Virginia Smith, Zhiwei Steven Wu:
Federated Learning as a Network Effects Game. CoRR abs/2302.08533 (2023) - [i114]Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu:
Counterfactual Prediction Under Outcome Measurement Error. CoRR abs/2302.11121 (2023) - [i113]Xin Gu, Gautam Kamath, Zhiwei Steven Wu:
Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance. CoRR abs/2303.01256 (2023) - [i112]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 abs/2303.03451 (2023) - [i111]Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu:
Inverse Reinforcement Learning without Reinforcement Learning. CoRR abs/2303.14623 (2023) - [i110]Terrance Liu, Jingwu Tang, Giuseppe Vietri, Zhiwei Steven Wu:
Generating Private Synthetic Data with Genetic Algorithms. CoRR abs/2306.03257 (2023) - [i109]Keegan Harris, Chara Podimata, Zhiwei Steven Wu:
Strategic Apple Tasting. CoRR abs/2306.06250 (2023) - [i108]Ryan Rogers, Gennady Samorodnitsky, Zhiwei Steven Wu, Aaditya Ramdas:
Adaptive Privacy Composition for Accuracy-first Mechanisms. CoRR abs/2306.13824 (2023) - [i107]Anish Agarwal, Keegan Harris, Justin Whitehouse, Zhiwei Steven Wu:
Adaptive Principal Component Regression with Applications to Panel Data. CoRR abs/2307.01357 (2023) - [i106]Mikhail Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu:
Meta-Learning Adversarial Bandit Algorithms. CoRR abs/2307.02295 (2023) - [i105]Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Scalable Membership Inference Attacks via Quantile Regression. CoRR abs/2307.03694 (2023) - [i104]Vladimir Braverman, Joel Manning, Zhiwei Steven Wu, Samson Zhou:
Private Data Stream Analysis for Universal Symmetric Norm Estimation. CoRR abs/2307.04249 (2023) - [i103]Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas:
Improved Self-Normalized Concentration in Hilbert Spaces: Sublinear Regret for GP-UCB. CoRR abs/2307.07539 (2023) - [i102]Konwoo Kim, Gokul Swamy, Zuxin Liu, Ding Zhao, Sanjiban Choudhury, Zhiwei Steven Wu:
Learning Shared Safety Constraints from Multi-task Demonstrations. CoRR abs/2309.00711 (2023) - [i101]Xinwei Zhang, Zhiqi Bu, Zhiwei Steven Wu, Mingyi Hong:
Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach. CoRR abs/2311.14632 (2023) - [i100]Jiahao Zhang, Shuran Zheng, Renato Paes Leme, Zhiwei Steven Wu:
Ex-post Individually Rational Bayesian Persuasion. CoRR abs/2312.04973 (2023) - [i99]Shuai Tang, Zhiwei Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth:
Membership Inference Attacks on Diffusion Models via Quantile Regression. CoRR abs/2312.05140 (2023) - [i98]Pratiksha Thaker, Amrith Setlur, Zhiwei Steven Wu, Virginia Smith:
Leveraging Public Representations for Private Transfer Learning. CoRR abs/2312.15551 (2023) - [i97]Dung Daniel T. Ngo, Keegan Harris, Anish Agarwal, Vasilis Syrgkanis, Zhiwei Steven Wu:
Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration. CoRR abs/2312.16307 (2023) - 2022
- [j9]Yishay Mansour, Alex Slivkins, Vasilis Syrgkanis, Zhiwei Steven Wu:
Bayesian Exploration: Incentivizing Exploration in Bayesian Games. Oper. Res. 70(2): 1105-1127 (2022) - [c80]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: 454-470 - [c79]Zheyuan Ryan Shi, Zhiwei Steven Wu, Rayid Ghani, Fei Fang:
Bandit Data-Driven Optimization for Crowdsourcing Food Rescue Platforms. AAAI 2022: 12154-12162 - [c78]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: 52:1-52:18 - [c77]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: 162:1-162:22 - [c76]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: 473-484 - [c75]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: 1162-1177 - [c74]Yahav Bechavod, Chara Podimata, Zhiwei Steven Wu, Juba Ziani:
Information Discrepancy in Strategic Learning. ICML 2022: 1691-1715 - [c73]Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu:
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. ICML 2022: 1945-1962 - [c72]Keegan Harris, Dung Daniel T. Ngo, Logan Stapleton, Hoda Heidari, Steven Wu:
Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses. ICML 2022: 8502-8522 - [c71]Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Zhiwei Steven Wu, Bo Li, Ding Zhao:
Constrained Variational Policy Optimization for Safe Reinforcement Learning. ICML 2022: 13644-13668 - [c70]Dung Daniel T. Ngo, Giuseppe Vietri, Steven Wu:
Improved Regret for Differentially Private Exploration in Linear MDP. ICML 2022: 16529-16552 - [c69]Gokul Swamy, Sanjiban Choudhury, Drew Bagnell, Steven Wu:
Causal Imitation Learning under Temporally Correlated Noise. ICML 2022: 20877-20890 - [c68]Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Steven Wu, Jinfeng Yi:
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy. ICML 2022: 26048-26067 - [c67]Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu:
Bayesian Persuasion for Algorithmic Recourse. NeurIPS 2022 - [c66]Xinyan Hu, Dung Daniel T. Ngo, Aleksandrs Slivkins, Zhiwei Steven Wu:
Incentivizing Combinatorial Bandit Exploration. NeurIPS 2022 - [c65]Ken Ziyu Liu, Shengyuan Hu, Steven Wu, Virginia Smith:
On Privacy and Personalization in Cross-Silo Federated Learning. NeurIPS 2022 - [c64]Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu:
Sequence Model Imitation Learning with Unobserved Contexts. NeurIPS 2022 - [c63]Gokul Swamy, Nived Rajaraman, Matthew Peng, Sanjiban Choudhury, J. Andrew Bagnell, Steven Wu, Jiantao Jiao, Kannan Ramchandran:
Minimax Optimal Online Imitation Learning via Replay Estimation. NeurIPS 2022 - [c62]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 - [c61]Justin Whitehouse, Aaditya Ramdas, Zhiwei Steven Wu, Ryan M. Rogers:
Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints. NeurIPS 2022 - [i96]Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Zhiwei Steven Wu, Bo Li, Ding Zhao:
Constrained Variational Policy Optimization for Safe Reinforcement Learning. CoRR abs/2201.11927 (2022) - [i95]Dung Daniel T. Ngo, Giuseppe Vietri, Zhiwei Steven Wu:
Improved Regret for Differentially Private Exploration in Linear MDP. CoRR abs/2202.01292 (2022) - [i94]Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu:
Causal Imitation Learning under Temporally Correlated Noise. CoRR abs/2202.01312 (2022) - [i93]Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, Himabindu Lakkaraju:
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective. CoRR abs/2202.01602 (2022) - [i92]Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu:
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization. CoRR abs/2202.05318 (2022) - [i91]Ian Waudby-Smith, Zhiwei Steven Wu, Aaditya Ramdas:
Locally private nonparametric confidence intervals and sequences. CoRR abs/2202.08728 (2022) - [i90]Justin Whitehouse, Aaditya Ramdas, Ryan Rogers, Zhiwei Steven Wu:
Fully Adaptive Composition in Differential Privacy. CoRR abs/2203.05481 (2022) - [i89]Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith:
Provably Fair Federated Learning via Bounded Group Loss. CoRR abs/2203.10190 (2022) - [i88]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 abs/2204.02310 (2022) - [i87]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 abs/2204.10233 (2022) - [i86]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 abs/2204.13872 (2022) - [i85]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 abs/2205.06922 (2022) - [i84]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 abs/2205.08928 (2022) - [i83]Maria-Florina Balcan, Keegan Harris, Mikhail Khodak, Zhiwei Steven Wu:
Meta-Learning Adversarial Bandits. CoRR abs/2205.14128 (2022) - [i82]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 abs/2205.15397 (2022) - [i81]Xinyan Hu, Dung Daniel T. Ngo, Aleksandrs Slivkins, Zhiwei Steven Wu:
Incentivizing Combinatorial Bandit Exploration. CoRR abs/2206.00494 (2022) - [i80]Terrance Liu, Zhiwei Steven Wu:
Private Synthetic Data with Hierarchical Structure. CoRR abs/2206.05942 (2022) - [i79]Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas, Ryan Rogers:
Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints. CoRR abs/2206.07234 (2022) - [i78]Ken Ziyu Liu, Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith:
On Privacy and Personalization in Cross-Silo Federated Learning. CoRR abs/2206.07902 (2022) - [i77]Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu:
Sequence Model Imitation Learning with Unobserved Contexts. CoRR abs/2208.02225 (2022) - [i76]Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu:
Game-Theoretic Algorithms for Conditional Moment Matching. CoRR abs/2208.09551 (2022) - [i75]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. CoRR abs/2209.07400 (2022) - [i74]Keegan Harris, Ioannis Anagnostides, Gabriele Farina, Mikhail Khodak, Zhiwei Steven Wu, Tuomas Sandholm:
Meta-Learning in Games. CoRR abs/2209.14110 (2022) - [i73]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 abs/2211.03128 (2022) - [i72]Zhun Deng, He Sun, Zhiwei Steven Wu, Linjun Zhang, David C. Parkes:
Reinforcement Learning with Stepwise Fairness Constraints. CoRR abs/2211.03994 (2022) - [i71]Keegan Harris, Anish Agarwal, Chara Podimata, Zhiwei Steven Wu:
Strategyproof Decision-Making in Panel Data Settings and Beyond. CoRR abs/2211.14236 (2022) - 2021
- [j8]Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu:
Private Hypothesis Selection. IEEE Trans. Inf. Theory 67(3): 1981-2000 (2021) - [c60]Yahav Bechavod, Katrina Ligett, Zhiwei Steven Wu, Juba Ziani:
Gaming Helps! Learning from Strategic Interactions in Natural Dynamics. AISTATS 2021: 1234-1242 - [c59]Vikas K. Garg, Adam Tauman Kalai, Katrina Ligett, Zhiwei Steven Wu:
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization. AISTATS 2021: 3574-3582 - [c58]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: 390:1-390:17 - [c57]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: 850-861 - [c56]Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu:
An Algorithmic Framework for Fairness Elicitation. FORC 2021: 2:1-2:19 - [c55]Marcel Neunhoeffer, Steven Wu, Cynthia Dwork:
Private Post-GAN Boosting. ICLR 2021 - [c54]Yingxue Zhou, Steven Wu, Arindam Banerjee:
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification. ICLR 2021 - [c53]Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, Himabindu Lakkaraju:
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations. ICML 2021: 110-119 - [c52]Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu:
Leveraging Public Data for Practical Private Query Release. ICML 2021: 6968-6977 - [c51]Dung Daniel T. Ngo, Logan Stapleton, Vasilis Syrgkanis, Steven Wu:
Incentivizing Compliance with Algorithmic Instruments. ICML 2021: 8045-8055 - [c50]Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Steven Wu:
Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap. ICML 2021: 10022-10032 - [c49]Terrance Liu, Giuseppe Vietri, Steven Wu:
Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods.