Zhiwei Steven Wu

According to our database, Zhiwei Steven Wu authored at least 155 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective.

Satyapriya Krishna, Tessa Han, Alex Gu, Steven Wu, Shahin Jabbari, Himabindu Lakkaraju

Trans. Mach. Learn. Res., 2024

Inferentially-Private Private Information.

Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu

CoRR, 2024

Position: LLM Unlearning Benchmarks are Weak Measures of Progress.

Pratiksha Thaker, Shengyuan Hu, Neil Kale, Yash Maurya, Zhiwei Steven Wu, Virginia Smith

CoRR, 2024

Decision-Focused Uncertainty Quantification.

Santiago Cortes-Gomez, Carlos Miguel Patiño, Yewon Byun, Steven Wu, Eric Horvitz, Bryan Wilder

CoRR, 2024

Multi-group Uncertainty Quantification for Long-form Text Generation.

Terrance Liu, Zhiwei Steven Wu

CoRR, 2024

Quantifying Privacy Risks of Public Statistics to Residents of Subsidized Housing.

Ryan Steed, Diana Qing, Zhiwei Steven Wu

CoRR, 2024

Jogging the Memory of Unlearned Model Through Targeted Relearning Attack.

Shengyuan Hu, Yiwei Fu, Zhiwei Steven Wu, Virginia Smith

CoRR, 2024

Multi-Agent Imitation Learning: Value is Easy, Regret is Hard.

Jingwu Tang, Gokul Swamy, Fei Fang, Zhiwei Steven Wu

CoRR, 2024

Orthogonal Causal Calibration.

Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu

CoRR, 2024

Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift.

Jiayun Wu, Jiashuo Liu, Peng Cui, Zhiwei Steven Wu

CoRR, 2024

Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable.

Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu

CoRR, 2024

Reconciling Model Multiplicity for Downstream Decision Making.

Ally Yalei Du, Dung Daniel T. Ngo, Zhiwei Steven Wu

CoRR, 2024

Provable Multi-Party Reinforcement Learning with Diverse Human Feedback.

Huiying Zhong, Zhun Deng, Weijie J. Su, Zhiwei Steven Wu, Linjun Zhang

CoRR, 2024

Differentially Private Bayesian Persuasion.

Yuqi Pan, Zhiwei Steven Wu, Haifeng Xu, Shuran Zheng

CoRR, 2024

Regret Minimization in Stackelberg Games with Side Information.

Keegan Harris, Zhiwei Steven Wu, Maria-Florina Balcan

CoRR, 2024

Strategyproof Decision-Making in Panel Data Settings and Beyond.

Keegan Harris, Anish Agarwal, Chara Podimata, Zhiwei Steven Wu

Proceedings of the Abstracts of the 2024 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, 2024

Improved Differentially Private Regression via Gradient Boosting.

Shuai Tang, Sergül Aydöre, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Zhiwei Steven Wu

Proceedings of the IEEE Conference on Secure and Trustworthy Machine Learning, 2024

Fair Federated Learning via Bounded Group Loss.

Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith

Proceedings of the IEEE Conference on Secure and Trustworthy Machine Learning, 2024

Membership Inference Attacks on Diffusion Models via Quantile Regression.

Shuai Tang, Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth

Proceedings of the Forty-first International Conference on Machine Learning, 2024

A Minimaximalist Approach to Reinforcement Learning from Human Feedback.

Gokul Swamy, Christoph Dann, Rahul Kidambi, Steven Wu, Alekh Agarwal

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Hybrid Inverse Reinforcement Learning.

Juntao Ren, Gokul Swamy, Steven Wu, Drew Bagnell, Sanjiban Choudhury

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Predictive Performance Comparison of Decision Policies Under Confounding.

Luke Guerdan, Amanda Coston, Ken Holstein, Steven Wu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach.

Xinwei Zhang, Zhiqi Bu, Steven Wu, Mingyi Hong

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Greedy Algorithm Almost Dominates in Smoothed Contextual Bandits.

Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu

SIAM J. Comput., April, 2023

Private Multi-Task Learning: Formulation and Applications to Federated Learning.

Shengyuan Hu, Steven Wu, Virginia Smith

Trans. Mach. Learn. Res., 2023

Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration.

Dung Daniel T. Ngo, Keegan Harris, Anish Agarwal, Vasilis Syrgkanis, Zhiwei Steven Wu

CoRR, 2023

Leveraging Public Representations for Private Transfer Learning.

Pratiksha Thaker, Amrith Setlur, Zhiwei Steven Wu, Virginia Smith

CoRR, 2023

Ex-post Individually Rational Bayesian Persuasion.

Jiahao Zhang, Shuran Zheng, Renato Paes Leme, Zhiwei Steven Wu

CoRR, 2023

Improved Self-Normalized Concentration in Hilbert Spaces: Sublinear Regret for GP-UCB.

Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas

CoRR, 2023

Meta-Learning Adversarial Bandit Algorithms.

Mikhail Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu

CoRR, 2023

Inverse Reinforcement Learning without Reinforcement Learning.

Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu

CoRR, 2023

Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance.

Xin Gu, Gautam Kamath, Zhiwei Steven Wu

CoRR, 2023

Federated Learning as a Network Effects Game.

Shengyuan Hu, Dung Daniel T. Ngo, Shuran Zheng, Virginia Smith, Zhiwei Steven Wu

CoRR, 2023

On the Sublinear Regret of GP-UCB.

Justin Whitehouse, Aaditya Ramdas, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Adaptive Privacy Composition for Accuracy-first Mechanisms.

Ryan M. Rogers, Gennady Samorodnitsky, Zhiwei Steven Wu, Aaditya Ramdas

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Shared Safety Constraints from Multi-task Demonstrations.

Konwoo Kim, Gokul Swamy, Zuxin Liu, Ding Zhao, Sanjiban Choudhury, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Meta-Learning Adversarial Bandit Algorithms.

Misha Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Strategic Apple Tasting.

Keegan Harris, Chara Podimata, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Scalable Membership Inference Attacks via Quantile Regression.

Martín Bertrán, Shuai Tang, Aaron Roth, Michael Kearns, Jamie Morgenstern, Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Adaptive Principal Component Regression with Applications to Panel Data.

Anish Agarwal, Keegan Harris, Justin Whitehouse, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fully-Adaptive Composition in Differential Privacy.

Justin Whitehouse, Aaditya Ramdas, Ryan Rogers, Steven Wu

Proceedings of the International Conference on Machine Learning, 2023

Nonparametric Extensions of Randomized Response for Private Confidence Sets.

Ian Waudby-Smith, Zhiwei Steven Wu, Aaditya Ramdas

Proceedings of the International Conference on Machine Learning, 2023

Inverse Reinforcement Learning without Reinforcement Learning.

Gokul Swamy, David Wu, Sanjiban Choudhury, Drew Bagnell, Zhiwei Steven Wu

Proceedings of the International Conference on Machine Learning, 2023

Generating Private Synthetic Data with Genetic Algorithms.

Terrance Liu, Jingwu Tang, Giuseppe Vietri, Steven Wu

Proceedings of the International Conference on Machine Learning, 2023

Meta-Learning in Games.

Keegan Harris, Ioannis Anagnostides, Gabriele Farina, Mikhail Khodak, Steven Wu, Tuomas Sandholm

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making.

Luke Guerdan, Amanda Coston, Zhiwei Steven Wu, Kenneth Holstein

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

Counterfactual Prediction Under Outcome Measurement Error.

Luke Guerdan, Amanda Coston, Kenneth Holstein, Zhiwei Steven Wu

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

Private Data Stream Analysis for Universal Symmetric Norm Estimation.

Vladimir Braverman, Joel Manning, Zhiwei Steven Wu, Samson Zhou

Proceedings of the Approximation, 2023

Reinforcement Learning with Stepwise Fairness Constraints.

Zhun Deng, He Sun, Steven Wu, Linjun Zhang, David C. Parkes

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Bayesian Exploration: Incentivizing Exploration in Bayesian Games.

Yishay Mansour, Alex Slivkins, Vasilis Syrgkanis, Zhiwei Steven Wu

Oper. Res., 2022

Confidence-Ranked Reconstruction of Census Microdata from Published Statistics.

Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu

CoRR, 2022

Game-Theoretic Algorithms for Conditional Moment Matching.

Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu

CoRR, 2022

Private Synthetic Data with Hierarchical Structure.

Terrance Liu, Zhiwei Steven Wu

CoRR, 2022

Meta-Learning Adversarial Bandits.

Maria-Florina Balcan, Keegan Harris, Mikhail Khodak, Zhiwei Steven Wu

CoRR, 2022

Extended Analysis of "How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions".

Logan Stapleton, Hao Fei Cheng, Anna Kawakami, Venkatesh Sivaraman, Yanghuidi Cheng, Diana Qing, Adam Perer, Kenneth Holstein, Zhiwei Steven Wu, Haiyi Zhu

CoRR, 2022

A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms.

Nil-Jana Akpinar, Manish Nagireddy, Logan Stapleton, Hao Fei Cheng, Haiyi Zhu, Zhiwei Steven Wu, Hoda Heidari

CoRR, 2022

Provably Fair Federated Learning via Bounded Group Loss.

Shengyuan Hu, Zhiwei Steven Wu, Virginia Smith

CoRR, 2022

Locally private nonparametric confidence intervals and sequences.

Ian Waudby-Smith, Zhiwei Steven Wu, Aaditya Ramdas

CoRR, 2022

Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization.

Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu

CoRR, 2022

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective.

Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, Himabindu Lakkaraju

CoRR, 2022

Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints.

Justin Whitehouse, Aaditya Ramdas, Zhiwei Steven Wu, Ryan M. Rogers

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Private Synthetic Data for Multitask Learning and Marginal Queries.

Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Minimax Optimal Online Imitation Learning via Replay Estimation.

Gokul Swamy, Nived Rajaraman, Matthew Peng, Sanjiban Choudhury, J. Andrew Bagnell, Steven Wu, Jiantao Jiao, Kannan Ramchandran

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Sequence Model Imitation Learning with Unobserved Contexts.

Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On Privacy and Personalization in Cross-Silo Federated Learning.

Ken Ziyu Liu, Shengyuan Hu, Steven Wu, Virginia Smith

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Incentivizing Combinatorial Bandit Exploration.

Xinyan Hu, Dung Daniel T. Ngo, Aleksandrs Slivkins, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Bayesian Persuasion for Algorithmic Recourse.

Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy.

Xinwei Zhang, Xiangyi Chen, Mingyi Hong, Steven Wu, Jinfeng Yi

Proceedings of the International Conference on Machine Learning, 2022

Causal Imitation Learning under Temporally Correlated Noise.

Gokul Swamy, Sanjiban Choudhury, Drew Bagnell, Steven Wu

Proceedings of the International Conference on Machine Learning, 2022

Improved Regret for Differentially Private Exploration in Linear MDP.

Dung Daniel T. Ngo, Giuseppe Vietri, Steven Wu

Proceedings of the International Conference on Machine Learning, 2022

Constrained Variational Policy Optimization for Safe Reinforcement Learning.

Zuxin Liu, Zhepeng Cen, Vladislav Isenbaev, Wei Liu, Zhiwei Steven Wu, Bo Li, Ding Zhao

Proceedings of the International Conference on Machine Learning, 2022

Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses.

Keegan Harris, Dung Daniel T. Ngo, Logan Stapleton, Hoda Heidari, Steven Wu

Proceedings of the International Conference on Machine Learning, 2022

Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning.

Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu

Proceedings of the International Conference on Machine Learning, 2022

Information Discrepancy in Strategic Learning.

Yahav Bechavod, Chara Podimata, Zhiwei Steven Wu, Juba Ziani

Proceedings of the International Conference on Machine Learning, 2022

Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders.

Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright, Alexandra Chouldechova, Ken Holstein, Zhiwei Steven Wu, Haiyi Zhu

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits.

Wesley Hanwen Deng, Manish Nagireddy, Michelle Seng Ah Lee, Jatinder Singh, Zhiwei Steven Wu, Kenneth Holstein, Haiyi Zhu

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support.

Anna Kawakami, Venkatesh Sivaraman, Hao Fei Cheng, Logan Stapleton, Yanghuidi Cheng, Diana Qing, Adam Perer, Zhiwei Steven Wu, Haiyi Zhu, Kenneth Holstein

Proceedings of the CHI '22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April 2022, 2022

How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions.

Hao Fei Cheng, Logan Stapleton, Anna Kawakami, Venkatesh Sivaraman, Yanghuidi Cheng, Diana Qing, Adam Perer, Kenneth Holstein, Zhiwei Steven Wu, Haiyi Zhu

Proceedings of the CHI '22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April 2022, 2022

Bandit Data-Driven Optimization for Crowdsourcing Food Rescue Platforms.

Zheyuan Ryan Shi, Zhiwei Steven Wu, Rayid Ghani, Fei Fang

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

"Why Do I Care What's Similar?" Probing Challenges in AI-Assisted Child Welfare Decision-Making through Worker-AI Interface Design Concepts.

Anna Kawakami, Venkatesh Sivaraman, Logan Stapleton, Hao Fei Cheng, Adam Perer, Zhiwei Steven Wu, Haiyi Zhu, Kenneth Holstein

Proceedings of the DIS '22: Designing Interactive Systems Conference, Virtual Event, Australia, June 13, 2022

2021

Private Hypothesis Selection.

Mark Bun, Gautam Kamath, Thomas Steinke, Zhiwei Steven Wu

IEEE Trans. Inf. Theory, 2021

A Critique of Strictly Batch Imitation Learning.

Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu

CoRR, 2021

Of Moments and Matching: Trade-offs and Treatments in Imitation Learning.

Gokul Swamy, Sanjiban Choudhury, Zhiwei Steven Wu, J. Andrew Bagnell

CoRR, 2021

Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods.

Terrance Liu, Giuseppe Vietri, Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Stateful Strategic Regression.

Keegan Harris, Hoda Heidari, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap.

Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Steven Wu

Proceedings of the 38th International Conference on Machine Learning, 2021

Incentivizing Compliance with Algorithmic Instruments.

Dung Daniel T. Ngo, Logan Stapleton, Vasilis Syrgkanis, Steven Wu

Proceedings of the 38th International Conference on Machine Learning, 2021

Leveraging Public Data for Practical Private Query Release.

Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu

Proceedings of the 38th International Conference on Machine Learning, 2021

Towards the Unification and Robustness of Perturbation and Gradient Based Explanations.

Sushant Agarwal, Shahin Jabbari, Chirag Agarwal, Sohini Upadhyay, Steven Wu, Himabindu Lakkaraju

Proceedings of the 38th International Conference on Machine Learning, 2021

Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification.

Yingxue Zhou, Steven Wu, Arindam Banerjee

Proceedings of the 9th International Conference on Learning Representations, 2021

Private Post-GAN Boosting.

Marcel Neunhoeffer, Steven Wu, Cynthia Dwork

Proceedings of the 9th International Conference on Learning Representations, 2021

An Algorithmic Framework for Fairness Elicitation.

Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu

Proceedings of the 2nd Symposium on Foundations of Responsible Computing, 2021

Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation.

Hong Shen, Wesley H. Deng, Aditi Chattopadhyay, Zhiwei Steven Wu, Xu Wang, Haiyi Zhu

Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems.

Hao Fei Cheng, Logan Stapleton, Ruiqi Wang, Paige Bullock, Alexandra Chouldechova, Zhiwei Steven Wu, Haiyi Zhu

Proceedings of the CHI '21: CHI Conference on Human Factors in Computing Systems, 2021

Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization.

Vikas K. Garg, Adam Tauman Kalai, Katrina Ligett, Zhiwei Steven Wu

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Gaming Helps! Learning from Strategic Interactions in Natural Dynamics.

Yahav Bechavod, Katrina Ligett, Zhiwei Steven Wu, Juba Ziani

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Multidimensional Dynamic Pricing for Welfare Maximization.

Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu

ACM Trans. Economics and Comput., 2020

Bandit Data-driven Optimization: AI for Social Good and Beyond.

Zheyuan Ryan Shi, Zhiwei Steven Wu, Rayid Ghani, Fei Fang

CoRR, 2020

Competing Bandits: The Perils of Exploration Under Competition.

Guy Aridor, Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu

CoRR, 2020

Private Stochastic Non-Convex Optimization: Adaptive Algorithms and Tighter Generalization Bounds.

Yingxue Zhou, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu, Arindam Banerjee

CoRR, 2020

Causal Feature Discovery through Strategic Modification.

Yahav Bechavod, Katrina Ligett, Zhiwei Steven Wu, Juba Ziani

CoRR, 2020

Incentivizing Exploration with Selective Data Disclosure.

Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu

Proceedings of the EC '20: The 21st ACM Conference on Economics and Computation, 2020

Understanding Gradient Clipping in Private SGD: A Geometric Perspective.

Xiangyi Chen, Zhiwei Steven Wu, Mingyi Hong

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms.

Xiangyi Chen, Tiancong Chen, Haoran Sun, Zhiwei Steven Wu, Mingyi Hong

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Metric-Free Individual Fairness in Online Learning.

Yahav Bechavod, Christopher Jung, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Privately Learning Markov Random Fields.

Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Zhiwei Steven Wu

Proceedings of the 37th International Conference on Machine Learning, 2020

New Oracle-Efficient Algorithms for Private Synthetic Data Release.

Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Zhiwei Steven Wu

Proceedings of the 37th International Conference on Machine Learning, 2020

Private Reinforcement Learning with PAC and Regret Guarantees.

Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Zhiwei Steven Wu

Proceedings of the 37th International Conference on Machine Learning, 2020

Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis.

Vidyashankar Sivakumar, Zhiwei Steven Wu, Arindam Banerjee

Proceedings of the 37th International Conference on Machine Learning, 2020

Oracle Efficient Private Non-Convex Optimization.

Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu

Proceedings of the 37th International Conference on Machine Learning, 2020

Private Query Release Assisted by Public Data.

Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, Zhiwei Steven Wu

Proceedings of the 37th International Conference on Machine Learning, 2020

Locally Private Hypothesis Selection.

Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang

Proceedings of the Conference on Learning Theory, 2020

Keeping Designers in the Loop: Communicating Inherent Algorithmic Trade-offs Across Multiple Objectives.

Bowen Yu, Ye Yuan, Loren Terveen, Zhiwei Steven Wu, Jodi Forlizzi, Haiyi Zhu

Proceedings of the DIS '20: Designing Interactive Systems Conference 2020, 2020

2019

Logarithmic Query Complexity for Approximate Nash Computation in Large Games.

Paul W. Goldberg, Francisco J. Marmolejo Cossío, Zhiwei Steven Wu

Theory Comput. Syst., 2019

Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM.

Zhiwei Steven Wu, Aaron Roth, Katrina Ligett, Bo Waggoner, Seth Neel

J. Priv. Confidentiality, 2019

Designing Interfaces to Help Stakeholders Comprehend, Navigate, and Manage Algorithmic Trade-Offs.

Bowen Yu, Ye Yuan, Loren Terveen, Zhiwei Steven Wu, Haiyi Zhu

CoRR, 2019

Differentially Private Objective Perturbation: Beyond Smoothness and Convexity.

Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu

CoRR, 2019

Eliciting and Enforcing Subjective Individual Fairness.

Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu

CoRR, 2019

Competing Bandits: The Perils of Exploration under Competition.

Guy Aridor, Kevin Liu, Aleksandrs Slivkins, Zhiwei Steven Wu

CoRR, 2019

Bayesian Exploration with Heterogeneous Agents.

Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu

Proceedings of the World Wide Web Conference, 2019

Locally Private Gaussian Estimation.

Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Equal Opportunity in Online Classification with Partial Feedback.

Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond.

Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Locally Private Bayesian Inference for Count Models.

Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, Hanna M. Wallach

Proceedings of the 36th International Conference on Machine Learning, 2019

Orthogonal Random Forest for Causal Inference.

Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu

Proceedings of the 36th International Conference on Machine Learning, 2019

Fair Regression: Quantitative Definitions and Reduction-Based Algorithms.

Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu

Proceedings of the 36th International Conference on Machine Learning, 2019

How to Use Heuristics for Differential Privacy.

Seth Neel, Aaron Roth, Zhiwei Steven Wu

Proceedings of the 60th IEEE Annual Symposium on Foundations of Computer Science, 2019

An Empirical Study of Rich Subgroup Fairness for Machine Learning.

Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu

Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

The Perils of Exploration under Competition: A Computational Modeling Approach.

Guy Aridor, Kevin Liu, Aleksandrs Slivkins, Zhiwei Steven Wu

Proceedings of the 2019 ACM Conference on Economics and Computation, 2019

2018

Privacy-Preserving Distributed Deep Learning for Clinical Data.

Brett K. Beaulieu-Jones, William Yuan, Samuel G. Finlayson, Zhiwei Steven Wu

CoRR, 2018

Incentivizing Exploration with Unbiased Histories.

Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu

CoRR, 2018

Orthogonal Random Forest for Heterogeneous Treatment Effect Estimation.

Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu

CoRR, 2018

Locally Private Bayesian Inference for Count Models.

Aaron Schein, Zhiwei Steven Wu, Mingyuan Zhou, Hanna M. Wallach

CoRR, 2018

Strategic Classification from Revealed Preferences.

Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, Zhiwei Steven Wu

Proceedings of the 2018 ACM Conference on Economics and Computation, 2018

A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem.

Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Competing Bandits: Learning Under Competition.

Yishay Mansour, Aleksandrs Slivkins, Zhiwei Steven Wu

Proceedings of the 9th Innovations in Theoretical Computer Science Conference, 2018

Semiparametric Contextual Bandits.

Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis

Proceedings of the 35th International Conference on Machine Learning, 2018

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness.

Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu

Proceedings of the 35th International Conference on Machine Learning, 2018

The Externalities of Exploration and How Data Diversity Helps Exploitation.

Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu

Proceedings of the Conference On Learning Theory, 2018

2017

Fairness Incentives for Myopic Agents.

Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu

Proceedings of the 2017 ACM Conference on Economics and Computation, 2017

Meritocratic Fairness for Cross-Population Selection.

Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu

Proceedings of the 34th International Conference on Machine Learning, 2017

Predicting with Distributions.

Michael J. Kearns, Zhiwei Steven Wu

Proceedings of the 30th Conference on Learning Theory, 2017

2016

Private Matchings and Allocations.

Justin Hsu, Zhiyi Huang, Aaron Roth, Tim Roughgarden, Zhiwei Steven Wu

SIAM J. Comput., 2016

Private algorithms for the protected in social network search.

Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev

Proc. Natl. Acad. Sci. USA, 2016

Dual Query: Practical Private Query Release for High Dimensional Data.

Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu

J. Priv. Confidentiality, 2016

Jointly Private Convex Programming.

Justin Hsu, Zhiyi Huang, Aaron Roth, Zhiwei Steven Wu

Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms, 2016

Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs.

Shahin Jabbari, Ryan M. Rogers, Aaron Roth, Zhiwei Steven Wu

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Coordination Complexity: Small Information Coordinating Large Populations.

Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, Zhiwei Steven Wu

Proceedings of the 2016 ACM Conference on Innovations in Theoretical Computer Science, 2016

Adaptive Learning with Robust Generalization Guarantees.

Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu

Proceedings of the 29th Conference on Learning Theory, 2016

2015

Watch and learn: optimizing from revealed preferences feedback.

Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu

SIGecom Exch., 2015

Privacy for the Protected (Only).

Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev

CoRR, 2015

Privacy and Truthful Equilibrium Selection for Aggregative Games.

Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu

Proceedings of the Web and Internet Economics - 11th International Conference, 2015

Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy).

Sampath Kannan, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu

Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms, 2015

Inducing Approximately Optimal Flow Using Truthful Mediators.

Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu

Proceedings of the Sixteenth ACM Conference on Economics and Computation, 2015

Accuracy for Sale: Aggregating Data with a Variance Constraint.

Rachel Cummings, Katrina Ligett, Aaron Roth, Zhiwei Steven Wu, Juba Ziani

Proceedings of the 2015 Conference on Innovations in Theoretical Computer Science, 2015