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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 - [i132]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) - [i131]Keegan Harris, Zhiwei Steven Wu, Maria-Florina Balcan:
Regret Minimization in Stackelberg Games with Side Information. CoRR abs/2402.08576 (2024) - [i130]Juntao Ren, Gokul Swamy, Zhiwei Steven Wu, J. Andrew Bagnell, Sanjiban Choudhury:
Hybrid Inverse Reinforcement Learning. CoRR abs/2402.08848 (2024) - [i129]Yuqi Pan, Zhiwei Steven Wu, Haifeng Xu, Shuran Zheng:
Differentially Private Bayesian Persuasion. CoRR abs/2402.15872 (2024) - [i128]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) - [i127]Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu:
Predictive Performance Comparison of Decision Policies Under Confounding. CoRR abs/2404.00848 (2024) - [i126]Ally Yalei Du, Dung Daniel T. Ngo, Zhiwei Steven Wu:
Reconciling Model Multiplicity for Downstream Decision Making. CoRR abs/2405.19667 (2024) - [i125]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) - [i124]Jiayun Wu, Jiashuo Liu, Peng Cui, Zhiwei Steven Wu:
Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift. CoRR abs/2406.00661 (2024) - [i123]Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu:
Orthogonal Causal Calibration. CoRR abs/2406.01933 (2024) - [i122]Jingwu Tang, Gokul Swamy, Fei Fang, Zhiwei Steven Wu:
Multi-Agent Imitation Learning: Value is Easy, Regret is Hard. CoRR abs/2406.04219 (2024) - [i121]Shengyuan Hu, Yiwei Fu, Zhiwei Steven Wu, Virginia Smith:
Jogging the Memory of Unlearned Model Through Targeted Relearning Attack. CoRR abs/2406.13356 (2024) - [i120]Ryan Steed, Diana Qing, Zhiwei Steven Wu:
Quantifying Privacy Risks of Public Statistics to Residents of Subsidized Housing. CoRR abs/2407.04776 (2024) - [i119]Terrance Liu, Zhiwei Steven Wu:
Multi-group Uncertainty Quantification for Long-form Text Generation. CoRR abs/2407.21057 (2024) - [i118]Santiago Cortes-Gomez, Carlos Miguel Patiño, Yewon Byun, Steven Wu, Eric Horvitz, Bryan Wilder:
Decision-Focused Uncertainty Quantification. CoRR abs/2410.01767 (2024) - [i117]Pratiksha Thaker, Shengyuan Hu, Neil Kale, Yash Maurya, Zhiwei Steven Wu, Virginia Smith:
Position: LLM Unlearning Benchmarks are Weak Measures of Progress. CoRR abs/2410.02879 (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. NeurIPS 2021: 690-702 - [c48]Keegan Harris, Hoda Heidari, Zhiwei Steven Wu:
Stateful Strategic Regression. NeurIPS 2021: 28728-28741 - [i70]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 abs/2102.01196 (2021) - [i69]Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu:
Leveraging Public Data for Practical Private Query Release. CoRR abs/2102.08598 (2021) - [i68]Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Zhiwei Steven Wu, Himabindu Lakkaraju:
Towards the Unification and Robustness of Perturbation and Gradient Based Explanations. CoRR abs/2102.10618 (2021) - [i67]Yahav Bechavod, Chara Podimata, Zhiwei Steven Wu, Juba Ziani:
Information Discrepancy in Strategic Learning. CoRR abs/2103.01028 (2021) - [i66]Gokul Swamy, Sanjiban Choudhury, Zhiwei Steven Wu, J. Andrew Bagnell:
Of Moments and Matching: Trade-offs and Treatments in Imitation Learning. CoRR abs/2103.03236 (2021) - [i65]Keegan Harris, Hoda Heidari, Zhiwei Steven Wu:
Stateful Strategic Regression. CoRR abs/2106.03827 (2021) - [i64]Terrance Liu, Giuseppe Vietri, Zhiwei Steven Wu:
Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods. CoRR abs/2106.07153 (2021) - [i63]Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu, Jinfeng Yi:
Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy. CoRR abs/2106.13673 (2021) - [i62]Keegan Harris, Dung Daniel T. Ngo, Logan Stapleton, Hoda Heidari, Zhiwei Steven Wu:
Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses. CoRR abs/2107.05762 (2021) - [i61]Dung Daniel T. Ngo, Logan Stapleton, Vasilis Syrgkanis, Zhiwei Steven Wu:
Incentivizing Compliance with Algorithmic Instruments. CoRR abs/2107.10093 (2021) - [i60]Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith:
Private Multi-Task Learning: Formulation and Applications to Federated Learning. CoRR abs/2108.12978 (2021) - [i59]Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu:
A Critique of Strictly Batch Imitation Learning. CoRR abs/2110.02063 (2021) - [i58]Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu:
Bayesian Persuasion for Algorithmic Recourse. CoRR abs/2112.06283 (2021) - 2020
- [j7]Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu:
Multidimensional Dynamic Pricing for Welfare Maximization. ACM Trans. Economics and Comput. 8(1): 6:1-6:35 (2020) - [c47]Bowen Yu, Ye Yuan, Loren Terveen, Zhiwei Steven Wu, Jodi Forlizzi, Haiyi Zhu:
Keeping Designers in the Loop: Communicating Inherent Algorithmic Trade-offs Across Multiple Objectives. Conference on Designing Interactive Systems 2020: 1245-1257 - [c46]Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang:
Locally Private Hypothesis Selection. COLT 2020: 1785-1816 - [c45]Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, Zhiwei Steven Wu:
Private Query Release Assisted by Public Data. ICML 2020: 695-703 - [c44]Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu:
Oracle Efficient Private Non-Convex Optimization. ICML 2020: 7243-7252 - [c43]Vidyashankar Sivakumar, Zhiwei Steven Wu, Arindam Banerjee:
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis. ICML 2020: 9026-9035 - [c42]Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Zhiwei Steven Wu:
Private Reinforcement Learning with PAC and Regret Guarantees. ICML 2020: 9754-9764 - [c41]Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Zhiwei Steven Wu:
New Oracle-Efficient Algorithms for Private Synthetic Data Release. ICML 2020: 9765-9774 - [c40]Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu:
Privately Learning Markov Random Fields. ICML 2020: 11129-11140 - [c39]Yahav Bechavod, Christopher Jung, Zhiwei Steven Wu:
Metric-Free Individual Fairness in Online Learning. NeurIPS 2020 - [c38]Xiangyi Chen, Tiancong Chen, Haoran Sun, Zhiwei Steven Wu, Mingyi Hong:
Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms. NeurIPS 2020 - [c37]Xiangyi Chen, Zhiwei Steven Wu, Mingyi Hong:
Understanding Gradient Clipping in Private SGD: A Geometric Perspective. NeurIPS 2020 - [c36]Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu:
Incentivizing Exploration with Selective Data Disclosure. EC 2020: 647-648 - [i57]Yahav Bechavod, Christopher Jung, Zhiwei Steven Wu:
Metric-Free Individual Fairness in Online Learning. CoRR abs/2002.05474 (2020) - [i56]Vikas K. Garg, Adam Kalai, Katrina Ligett, Zhiwei Steven Wu:
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization. CoRR abs/2002.05660 (2020) - [i55]Yahav Bechavod, Katrina Ligett, Zhiwei Steven Wu, Juba Ziani:
Causal Feature Discovery through Strategic Modification. CoRR abs/2002.07024 (2020) - [i54]Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu:
Privately Learning Markov Random Fields. CoRR abs/2002.09463 (2020) - [i53]Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang:
Locally Private Hypothesis Selection. CoRR abs/2002.09465 (2020) - [i52]Vidyashankar Sivakumar, Zhiwei Steven Wu, Arindam Banerjee:
Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis. CoRR abs/2002.11332 (2020) - [i51]Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, Zhiwei Steven Wu:
Private Query Release Assisted by Public Data. CoRR abs/2004.10941 (2020) - [i50]Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu:
Greedy Algorithm almost Dominates in Smoothed Contextual Bandits. CoRR abs/2005.10624 (2020) - [i49]Yingxue Zhou, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu, Arindam Banerjee:
Private Stochastic Non-Convex Optimization: Adaptive Algorithms and Tighter Generalization Bounds. CoRR abs/2006.13501 (2020) - [i48]Xiangyi Chen, Zhiwei Steven Wu, Mingyi Hong:
Understanding Gradient Clipping in Private SGD: A Geometric Perspective. CoRR abs/2006.15429 (2020) - [i47]Yingxue Zhou, Zhiwei Steven Wu, Arindam Banerjee:
Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification. CoRR abs/2007.03813 (2020) - [i46]Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Zhiwei Steven Wu:
New Oracle-Efficient Algorithms for Private Synthetic Data Release. CoRR abs/2007.05453 (2020) - [i45]Guy Aridor, Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu:
Competing Bandits: The Perils of Exploration Under Competition. CoRR abs/2007.10144 (2020) - [i44]Marcel Neunhoeffer, Zhiwei Steven Wu, Cynthia Dwork:
Private Post-GAN Boosting. CoRR abs/2007.11934 (2020) - [i43]Zheyuan Ryan Shi, Zhiwei Steven Wu, Rayid Ghani, Fei Fang:
Bandit Data-driven Optimization: AI for Social Good and Beyond. CoRR abs/2008.11707 (2020) - [i42]Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Zhiwei Steven Wu:
Private Reinforcement Learning with PAC and Regret Guarantees. CoRR abs/2009.09052 (2020) - [i41]Hong Shen, Wesley Deng, Aditi Chattopadhyay, Zhiwei Steven Wu, Xu Wang, Haiyi Zhu:
Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation. CoRR abs/2010.11411 (2020)
2010 – 2019
- 2019
- [j6]Zhiwei Steven Wu, Aaron Roth, Katrina Ligett, Bo Waggoner, Seth Neel:
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM. J. Priv. Confidentiality 9(2) (2019) - [j5]Paul W. Goldberg, Francisco J. Marmolejo Cossío, Zhiwei Steven Wu:
Logarithmic Query Complexity for Approximate Nash Computation in Large Games. Theory Comput. Syst. 63(1): 26-53 (2019) - [c35]Guy Aridor, Kevin Liu, Aleksandrs Slivkins, Zhiwei Steven Wu:
The Perils of Exploration under Competition: A Computational Modeling Approach. EC 2019: 171-172 - [c34]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
An Empirical Study of Rich Subgroup Fairness for Machine Learning. FAT 2019: 100-109 - [c33]Seth Neel, Aaron Roth, Zhiwei Steven Wu:
How to Use Heuristics for Differential Privacy. FOCS 2019: 72-93 - [c32]Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu:
Fair Regression: Quantitative Definitions and Reduction-Based Algorithms. ICML 2019: 120-129 - [c31]Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu:
Orthogonal Random Forest for Causal Inference. ICML 2019: 4932-4941 - [c30]Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, Hanna M. Wallach:
Locally Private Bayesian Inference for Count Models. ICML 2019: 5638-5648 - [c29]Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu:
Private Hypothesis Selection. NeurIPS 2019: 156-167 - [c28]Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu:
Locally Private Gaussian Estimation. NeurIPS 2019: 2980-2989 - [c27]Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
Equal Opportunity in Online Classification with Partial Feedback. NeurIPS 2019: 8972-8982 - [c26]Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar, Zhiwei Steven Wu:
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond. NeurIPS 2019: 12578-12588 - [c25]Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu:
Bayesian Exploration with Heterogeneous Agents. WWW 2019: 751-761 - [i40]Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
Equal Opportunity in Online Classification with Partial Feedback. CoRR abs/1902.02242 (2019) - [i39]Guy Aridor, Kevin Liu, Aleksandrs Slivkins, Zhiwei Steven Wu:
Competing Bandits: The Perils of Exploration under Competition. CoRR abs/1902.05590 (2019) - [i38]Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu:
Bayesian Exploration with Heterogeneous Agents. CoRR abs/1902.07119 (2019) - [i37]Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu:
Eliciting and Enforcing Subjective Individual Fairness. CoRR abs/1905.10660 (2019) - [i36]Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu:
Fair Regression: Quantitative Definitions and Reduction-based Algorithms. CoRR abs/1905.12843 (2019) - [i35]Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu:
Private Hypothesis Selection. CoRR abs/1905.13229 (2019) - [i34]Xiangyi Chen, Tiancong Chen, Haoran Sun, Zhiwei Steven Wu, Mingyi Hong:
Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms. CoRR abs/1906.01736 (2019) - [i33]Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu:
Differentially Private Objective Perturbation: Beyond Smoothness and Convexity. CoRR abs/1909.01783 (2019) - [i32]Bowen Yu, Ye Yuan, Loren Terveen, Zhiwei Steven Wu, Haiyi Zhu:
Designing Interfaces to Help Stakeholders Comprehend, Navigate, and Manage Algorithmic Trade-Offs. CoRR abs/1910.03061 (2019) - [i31]Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar, Zhiwei Steven Wu:
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond. CoRR abs/1910.04930 (2019) - 2018
- [c24]Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu:
The Externalities of Exploration and How Data Diversity Helps Exploitation. COLT 2018: 1724-1738 - [c23]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. ICML 2018: 2569-2577 - [c22]Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis:
Semiparametric Contextual Bandits. ICML 2018: 2781-2790 - [c21]Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu:
Competing Bandits: Learning Under Competition. ITCS 2018: 48:1-48:27 - [c20]Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem. NeurIPS 2018: 2231-2241 - [c19]Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, Zhiwei Steven Wu:
Strategic Classification from Revealed Preferences. EC 2018: 55-70 - [i30]Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem. CoRR abs/1801.03423 (2018) - [i29]Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis:
Semiparametric Contextual Bandits. CoRR abs/1803.04204 (2018) - [i28]Aaron Schein, Zhiwei Steven Wu, Mingyuan Zhou, Hanna M. Wallach:
Locally Private Bayesian Inference for Count Models. CoRR abs/1803.08471 (2018) - [i27]Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu:
The Externalities of Exploration and How Data Diversity Helps Exploitation. CoRR abs/1806.00543 (2018) - [i26]Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu:
Orthogonal Random Forest for Heterogeneous Treatment Effect Estimation. CoRR abs/1806.03467 (2018) - [i25]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
An Empirical Study of Rich Subgroup Fairness for Machine Learning. CoRR abs/1808.08166 (2018) - [i24]Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu:
Incentivizing Exploration with Unbiased Histories. CoRR abs/1811.06026 (2018) - [i23]Seth Neel, Aaron Roth, Zhiwei Steven Wu:
How to Use Heuristics for Differential Privacy. CoRR abs/1811.07765 (2018) - [i22]Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu:
Locally Private Gaussian Estimation. CoRR abs/1811.08382 (2018) - [i21]Brett K. Beaulieu-Jones, William Yuan, Samuel G. Finlayson, Zhiwei Steven Wu:
Privacy-Preserving Distributed Deep Learning for Clinical Data. CoRR abs/1812.01484 (2018) - 2017
- [c18]Michael J. Kearns, Zhiwei Steven Wu:
Predicting with Distributions. COLT 2017: 1214-1241 - [c17]Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu:
Meritocratic Fairness for Cross-Population Selection. ICML 2017: 1828-1836 - [c16]Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM. NIPS 2017: 2566-2576 - [c15]Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu:
Fairness Incentives for Myopic Agents. EC 2017: 369-386 - [c14]Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu:
Multidimensional Dynamic Pricing for Welfare Maximization. EC 2017: 519-536 - [i20]Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu:
Competing Bandits: Learning under Competition. CoRR abs/1702.08533 (2017) - [i19]Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu:
Fairness Incentives for Myopic Agents. CoRR abs/1705.02321 (2017) - [i18]Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM. CoRR abs/1705.10829 (2017) - [i17]Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, Zhiwei Steven Wu:
Strategic Classification from Revealed Preferences. CoRR abs/1710.07887 (2017) - [i16]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. CoRR abs/1711.05144 (2017) - 2016
- [j4]Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu:
Dual Query: Practical Private Query Release for High Dimensional Data. J. Priv. Confidentiality 7(2) (2016) - [j3]Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev:
Private algorithms for the protected in social network search. Proc. Natl. Acad. Sci. USA 113(4): 913-918 (2016) - [j2]Justin Hsu, Zhiyi Huang, Aaron Roth, Tim Roughgarden, Zhiwei Steven Wu:
Private Matchings and Allocations. SIAM J. Comput. 45(6): 1953-1984 (2016) - [c13]Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu:
Adaptive Learning with Robust Generalization Guarantees. COLT 2016: 772-814 - [c12]Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, Zhiwei Steven Wu:
Coordination Complexity: Small Information Coordinating Large Populations. ITCS 2016: 281-290 - [c11]Shahin Jabbari, Ryan M. Rogers, Aaron Roth, Zhiwei Steven Wu:
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs. NIPS 2016: 1570-1578 - [c10]Paul W. Goldberg, Francisco J. Marmolejo Cossío, Zhiwei Steven Wu:
Logarithmic Query Complexity for Approximate Nash Computation in Large Games. SAGT 2016: 3-14 - [c9]Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis, Zhiwei Steven Wu:
Bayesian Exploration: Incentivizing Exploration in Bayesian Games. EC 2016: 661 - [c8]Justin Hsu, Zhiyi Huang, Aaron Roth, Zhiwei Steven Wu:
Jointly Private Convex Programming. SODA 2016: 580-599 - [c7]Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Watch and learn: optimizing from revealed preferences feedback. STOC 2016: 949-962 - [i15]Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis, Zhiwei Steven Wu:
Bayesian Exploration: Incentivizing Exploration in Bayesian Games. CoRR abs/1602.07570 (2016) - [i14]Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu:
Adaptive Learning with Robust Generalization Guarantees. CoRR abs/1602.07726 (2016) - [i13]Michael J. Kearns, Zhiwei Steven Wu:
Predicting with Distributions. CoRR abs/1606.01275 (2016) - [i12]Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu:
Multidimensional Dynamic Pricing for Welfare Maximization. CoRR abs/1607.05397 (2016) - [i11]Paul W. Goldberg, Francisco J. Marmolejo Cossío, Zhiwei Steven Wu:
Logarithmic Query Complexity for Approximate Nash Computation in Large Games. CoRR abs/1610.08906 (2016) - 2015
- [j1]Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Watch and learn: optimizing from revealed preferences feedback. SIGecom Exch. 14(1): 101-104 (2015) - [c6]Rachel Cummings, Katrina Ligett, Aaron Roth, Zhiwei Steven Wu, Juba Ziani:
Accuracy for Sale: Aggregating Data with a Variance Constraint. ITCS 2015: 317-324 - [c5]Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Inducing Approximately Optimal Flow Using Truthful Mediators. EC 2015: 471-488 - [c4]Sampath Kannan, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy). SODA 2015: 1890-1903 - [c3]Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu:
Privacy and Truthful Equilibrium Selection for Aggregative Games. WINE 2015: 286-299 - [i10]Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Inducing Approximately Optimal Flow Using Truthful Mediators. CoRR abs/1502.04019 (2015) - [i9]Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Watch and Learn: Optimizing from Revealed Preferences Feedback. CoRR abs/1504.01033 (2015) - [i8]Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev:
Privacy for the Protected (Only). CoRR abs/1506.00242 (2015) - [i7]Shahin Jabbari, Ryan M. Rogers, Aaron Roth, Zhiwei Steven Wu:
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs. CoRR abs/1506.02162 (2015) - [i6]Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, Zhiwei Steven Wu:
Coordination Complexity: Small Information Coordinating Large Populations. CoRR abs/1508.03735 (2015) - 2014
- [c2]Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu:
Dual Query: Practical Private Query Release for High Dimensional Data. ICML 2014: 1170-1178 - [c1]Justin Hsu, Zhiyi Huang, Aaron Roth, Tim Roughgarden, Zhiwei Steven Wu:
Private matchings and allocations. STOC 2014: 21-30 - [i5]Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu:
Dual Query: Practical Private Query Release for High Dimensional Data. CoRR abs/1402.1526 (2014) - [i4]Sampath Kannan, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy). CoRR abs/1407.2640 (2014) - [i3]Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu:
Privacy and Truthful Equilibrium Selection for Aggregative Games. CoRR abs/1407.7740 (2014) - [i2]Justin Hsu, Zhiyi Huang, Aaron Roth, Zhiwei Steven Wu:
Jointly Private Convex Programming. CoRR abs/1411.0998 (2014) - 2013
- [i1]Justin Hsu, Zhiyi Huang, Aaron Roth, Tim Roughgarden, Zhiwei Steven Wu:
Private Matchings and Allocations. CoRR abs/1311.2828 (2013)
Coauthor Index
J. Andrew Bagnell
aka: Drew Bagnell
aka: Drew Bagnell
Kenneth Holstein
aka: Ken Holstein
aka: Ken Holstein
Michael Kearns
aka: Michael J. Kearns
aka: Michael J. Kearns
[c100] [c99] [i125] [c86] [i112] [i105] [i99] [c62] [i75] [i73] [c56] [j7] [c44] [j6] [c34] [c33] [c27] [i40] [i37] [i33] [c23] [c20] [c19] [i30] [i25] [i23] [c17] [c16] [c15] [c14] [i19] [i18] [i17] [i16] [j4] [j3] [j2] [c13] [c12] [c11] [c8] [c7] [i14] [i12] [j1] [c6] [c5] [c4] [c3] [i10] [i9] [i8] [i7] [i6] [c2] [c1] [i5] [i4] [i3] [i2] [i1]
Aleksandrs Slivkins
aka: Alex Slivkins
aka: Alex Slivkins
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