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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
- 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)- [c72]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 - [c71]Zheyuan Ryan Shi, Zhiwei Steven Wu, Rayid Ghani, Fei Fang:
Bandit Data-Driven Optimization for Crowdsourcing Food Rescue Platforms. AAAI 2022: 12154-12162 - [c70]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 - [c69]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 - [c68]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 - [c67]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 - [c66]Yahav Bechavod, Chara Podimata, Zhiwei Steven Wu, Juba Ziani:
Information Discrepancy in Strategic Learning. ICML 2022: 1691-1715 - [c65]Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu:
Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. ICML 2022: 1945-1962 - [c64]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 - [c63]Dung Daniel T. Ngo, Giuseppe Vietri, Steven Wu:
Improved Regret for Differentially Private Exploration in Linear MDP. ICML 2022: 16529-16552 - [c62]Gokul Swamy, Sanjiban Choudhury, Drew Bagnell, Steven Wu:
Causal Imitation Learning under Temporally Correlated Noise. ICML 2022: 20877-20890 - [c61]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 - [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 Aydore, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu:
Private Synthetic Data for Multitask Learning and Marginal Queries. CoRR 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]