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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
- 2025
[j13]Guy Aridor
, Yishay Mansour, Aleksandrs Slivkins, Steven Wu:
Competing Bandits: The Perils of Exploration Under Competition. ACM Trans. Economics and Comput. 13(1): 3:1-3:47 (2025)- [c115]Santiago Cortes-Gomez, Carlos Miguel Patiño, Yewon Byun, Steven Wu, Eric Horvitz, Bryan Wilder:
Utility-Directed Conformal Prediction: A Decision-Aware Framework for Actionable Uncertainty Quantification. ICLR 2025 - [c114]Ally Yalei Du, Dung Daniel T. Ngo, Zhiwei Steven Wu:
Reconciling Model Multiplicity for Downstream Decision Making. ICLR 2025 - [c113]Alexander Goldberg, Giulia Fanti, Nihar B. Shah, Steven Wu:
Benchmarking Fraud Detectors on Private Graph Data. KDD (1) 2025: 378-389 - [c112]Pratiksha Thaker, Shengyuan Hu, Neil Kale, Yash Maurya, Zhiwei Steven Wu, Virginia Smith:
Position: LLM Unlearning Benchmarks are Weak Measures of Progress. SaTML 2025: 520-533 - [c111]Xin Gu, Gautam Kamath, Zhiwei Steven Wu:
Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance. SaTML 2025: 879-900 - [c110]Yuqi Pan, Zhiwei Steven Wu, Haifeng Xu, Shuran Zheng:
Differentially Private Bayesian Persuasion. WWW 2025: 1425-1440 - [c109]Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu:
Inferentially-Private Private Information. WWW 2025: 2579-2595 - [i143]Maria-Florina Balcan, Martino Bernasconi, Matteo Castiglioni, Andrea Celli, Keegan Harris, Zhiwei Steven Wu:
Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information. CoRR abs/2502.00204 (2025) - [i142]Shuli Jiang, Pranay Sharma, Zhiwei Steven Wu, Gauri Joshi:
The Cost of Shuffling in Private Gradient Based Optimization. CoRR abs/2502.03652 (2025) - [i141]Konstantina Bairaktari, Jiayun Wu, Zhiwei Steven Wu:
Kandinsky Conformal Prediction: Beyond Class- and Covariate-Conditional Coverage. CoRR abs/2502.17264 (2025) - [i140]Gokul Swamy, Sanjiban Choudhury, Wen Sun, Zhiwei Steven Wu, J. Andrew Bagnell:
All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning. CoRR abs/2503.01067 (2025) - [i139]Luke Guerdan, Solon Barocas, Kenneth Holstein, Hanna M. Wallach, Zhiwei Steven Wu, Alexandra Chouldechova:
Validating LLM-as-a-Judge Systems in the Absence of Gold Labels. CoRR abs/2503.05965 (2025) - [i138]Xiaoyu Wu, Yifei Pang, Terrance Liu, Zhiwei Steven Wu:
Winning the MIDST Challenge: New Membership Inference Attacks on Diffusion Models for Tabular Data Synthesis. CoRR abs/2503.12008 (2025) - [i137]Jingwu Tang, Jiayun Wu, Zhiwei Steven Wu, Jiahao Zhang:
Dimension-Free Decision Calibration for Nonlinear Loss Functions. CoRR abs/2504.15615 (2025) - [i136]Terrance Liu, Eileen Xiao, Pratiksha Thaker, Adam Smith, Zhiwei Steven Wu:
Generate-then-Verify: Reconstructing Data from Limited Published Statistics. CoRR abs/2504.21199 (2025) - 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) - [c108]Xinwei Zhang, Zhiqi Bu, Steven Wu, Mingyi Hong:
Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach. ICLR 2024 - [c107]Luke Guerdan, Amanda Coston, Ken Holstein, Steven Wu:
Predictive Performance Comparison of Decision Policies Under Confounding. ICML 2024 - [c106]Juntao Ren, Gokul Swamy, Steven Wu, Drew Bagnell, Sanjiban Choudhury:
Hybrid Inverse Reinforcement Learning. ICML 2024 - [c105]Gokul Swamy, Christoph Dann, Rahul Kidambi, Steven Wu, Alekh Agarwal:
A Minimaximalist Approach to Reinforcement Learning from Human Feedback. ICML 2024 - [c104]Shuai Tang, Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth:
Membership Inference Attacks on Diffusion Models via Quantile Regression. ICML 2024 - [c103]Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty, Zhiwei Steven Wu:
Oracle-Efficient Differentially Private Learning with Public Data. NeurIPS 2024 - [c102]Keegan Harris, Zhiwei Steven Wu, Maria-Florina Balcan:
Regret Minimization in Stackelberg Games with Side Information. NeurIPS 2024 - [c101]Jingwu Tang, Gokul Swamy, Fei Fang, Zhiwei Steven Wu:
Multi-Agent Imitation Learning: Value is Easy, Regret is Hard. NeurIPS 2024 - [c100]Jiayun Wu, Jiashuo Liu, Peng Cui, Steven Wu:
Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift. NeurIPS 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 - [i135]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) - [i134]Keegan Harris, Zhiwei Steven Wu, Maria-Florina Balcan:
Regret Minimization in Stackelberg Games with Side Information. CoRR abs/2402.08576 (2024) - [i133]Juntao Ren, Gokul Swamy, Zhiwei Steven Wu, J. Andrew Bagnell, Sanjiban Choudhury:
Hybrid Inverse Reinforcement Learning. CoRR abs/2402.08848 (2024) - [i132]Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty, Zhiwei Steven Wu:
Oracle-Efficient Differentially Private Learning with Public Data. CoRR abs/2402.09483 (2024) - [i131]Yuqi Pan, Zhiwei Steven Wu, Haifeng Xu, Shuran Zheng:
Differentially Private Bayesian Persuasion. CoRR abs/2402.15872 (2024) - [i130]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) - [i129]Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu:
Predictive Performance Comparison of Decision Policies Under Confounding. CoRR abs/2404.00848 (2024) - [i128]Ally Yalei Du, Dung Daniel T. Ngo, Zhiwei Steven Wu:
Reconciling Model Multiplicity for Downstream Decision Making. CoRR abs/2405.19667 (2024) - [i127]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) - [i126]Jiayun Wu, Jiashuo Liu, Peng Cui, Zhiwei Steven Wu:
Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift. CoRR abs/2406.00661 (2024) - [i125]Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu:
Orthogonal Causal Calibration. CoRR abs/2406.01933 (2024) - [i124]Jingwu Tang, Gokul Swamy, Fei Fang, Zhiwei Steven Wu:
Multi-Agent Imitation Learning: Value is Easy, Regret is Hard. CoRR abs/2406.04219 (2024) - [i123]Shengyuan Hu, Yiwei Fu, Zhiwei Steven Wu, Virginia Smith:
Jogging the Memory of Unlearned Model Through Targeted Relearning Attack. CoRR abs/2406.13356 (2024) - [i122]Ryan Steed, Diana Qing, Zhiwei Steven Wu:
Quantifying Privacy Risks of Public Statistics to Residents of Subsidized Housing. CoRR abs/2407.04776 (2024) - [i121]Terrance Liu, Zhiwei Steven Wu:
Multi-group Uncertainty Quantification for Long-form Text Generation. CoRR abs/2407.21057 (2024) - [i120]Santiago Cortes-Gomez, Carlos Miguel Patiño, Yewon Byun, Steven Wu, Eric Horvitz, Bryan Wilder:
Decision-Focused Uncertainty Quantification. CoRR abs/2410.01767 (2024) - [i119]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) - [i118]Xiaoyu Wu, Jiaru Zhang, Steven Wu:
Revealing the Unseen: Guiding Personalized Diffusion Models to Expose Training Data. CoRR abs/2410.03039 (2024) - [i117]Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu:
Inferentially-Private Private Information. CoRR abs/2410.17095 (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
[c104] [c99] [i127] [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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of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-06-08 15:44 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
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since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
