Zhiwei Steven Wu

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How to Use Heuristics for Differential Privacy

Seth Neel, Aaron Roth, Zhiwei Steven Wu, 2019, 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS).

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

Zhiwei Steven Wu, Vidyashankar Sivakumar, Qilong Gu, 2019, NeurIPS.

Logarithmic Query Complexity for Approximate Nash Computation in Large Games

Paul W. Goldberg, Zhiwei Steven Wu, Francisco J. Marmolejo Cossío, 2016, SAGT.

Coordination Complexity: Small Information Coordinating Large Populations

Jaikumar Radhakrishnan, Aaron Roth, Zhiwei Steven Wu, 2015, ITCS.

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

Zhiwei Steven Wu, Katrina Ligett, Vikas K. Garg, 2020, AISTATS.

Private algorithms for the protected in social network search

Zhiwei Steven Wu, Grigory Yaroslavtsev, Michael Kearns, 2016, Proceedings of the National Academy of Sciences.

An Empirical Study of Rich Subgroup Fairness for Machine Learning

Seth Neel, Aaron Roth, Zhiwei Steven Wu, 2018, FAT.

Eliciting and Enforcing Subjective Individual Fairness pdf

Christopher Jung, Seth Neel, Aaron Roth, 2019, ArXiv.

New Oracle-Efficient Algorithms for Private Synthetic Data Release

Thomas Steinke, Zhiwei Steven Wu, Mark Bun, 2020, ICML.

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

Rayid Ghani, Zhiwei Steven Wu, Fei Fang, 2020, ArXiv.

Fairness Incentives for Myopic Agents

Sampath Kannan, Aaron Roth, Zhiwei Steven Wu, 2017, EC.

Privacy and Truthful Equilibrium Selection for Aggregative Games

Aaron Roth, Zhiwei Steven Wu, Michael Kearns, 2014, WINE.

Bayesian Exploration: Incentivizing Exploration in Bayesian Games

Yishay Mansour, Zhiwei Steven Wu, Aleksandrs Slivkins, 2016, EC.

Inducing Approximately Optimal Flow Using Truthful Mediators

Aaron Roth, Zhiwei Steven Wu, Jonathan Ullman, 2015, EC.

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

Zhiwei Steven Wu, Arindam Banerjee, Yingxue Zhou, 2020, ArXiv.

Greedy Algorithm almost Dominates in Smoothed Contextual Bandits pdf

Zhiwei Steven Wu, Aleksandrs Slivkins, Jennifer Wortman Vaughan, 2020, ArXiv.

Incentivizing Exploration with Selective Data Disclosure

Nicole Immorlica, Zhiwei Steven Wu, Aleksandrs Slivkins, 2018, EC.

Adaptive Learning with Robust Generalization Guarantees pdf

Aaron Roth, Zhiwei Steven Wu, Kobbi Nissim, 2016, COLT.

Private Post-GAN Boosting pdf

Zhiwei Steven Wu, Cynthia Dwork, Marcel Neunhoeffer, 2020, ICLR.

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

Zhiwei Steven Wu, Xiangyi Chen, Mingyi Hong, 2020, ArXiv.

Privacy-preserving generative deep neural networks support clinical data sharing

Casey S. Greene, Zhiwei Steven Wu, Christopher J. Williams, 2017 .

Metric-Free Individual Fairness in Online Learning pdf

Christopher Jung, Zhiwei Steven Wu, Yahav Bechavod, 2020, NeurIPS.

An Algorithmic Framework for Fairness Elicitation

Christopher Jung, Seth Neel, Aaron Roth, 2020, FORC.

Multidimensional Dynamic Pricing for Welfare Maximization

Aaron Roth, Zhiwei Steven Wu, Aleksandrs Slivkins, 2020, ACM Trans. Economics and Comput..

Private Hypothesis Selection

Thomas Steinke, Zhiwei Steven Wu, Mark Bun, 2019, IEEE Transactions on Information Theory.

The Perils of Exploration under Competition: A Computational Modeling Approach

Zhiwei Steven Wu, Aleksandrs Slivkins, Kevin Liu, 2019, EC.

Locally Private Bayesian Inference for Count Models pdf

Zhiwei Steven Wu, Mingyuan Zhou, Hanna M. Wallach, 2018, ICML.

Fair Regression: Quantitative Definitions and Reduction-based Algorithms

Miroslav Dudík, Zhiwei Steven Wu, Alekh Agarwal, 2019, ICML.

Equal Opportunity in Online Classification with Partial Feedback

Aaron Roth, Zhiwei Steven Wu, Katrina Ligett, 2019, NeurIPS.

Privacy-Preserving Distributed Deep Learning for Clinical Data pdf

Zhiwei Steven Wu, Samuel G. Finlayson, Brett K. Beaulieu-Jones, 2018, ArXiv.

The Externalities of Exploration and How Data Diversity Helps Exploitation pdf

Zhiwei Steven Wu, Aleksandrs Slivkins, Jennifer Wortman Vaughan, 2018, COLT.

Gaming Helps! Learning from Strategic Interactions in Natural Dynamics pdf

Zhiwei Steven Wu, Katrina Ligett, Juba Ziani, 2021, AISTATS.

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

Sampath Kannan, Aaron Roth, Zhiwei Steven Wu, 2014, SODA.

Orthogonal Random Forest for Heterogeneous Treatment Effect Estimation pdf

Zhiwei Steven Wu, Vasilis Syrgkanis, Miruna Oprescu, 2018, ArXiv.

Semiparametric Contextual Bandits pdf

Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis, 2018, ICML.

Incentivizing Exploration with Unbiased Histories pdf

Nicole Immorlica, Zhiwei Steven Wu, Aleksandrs Slivkins, 2018, ArXiv.

Locally Private Gaussian Estimation

Janardhan Kulkarni, Zhiwei Steven Wu, Jieming Mao, 2018, NeurIPS.

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

Haiyi Zhu, Zhiwei Steven Wu, Loren Terveen, 2019, ArXiv.

Jointly Private Convex Programming

Justin Hsu, Aaron Roth, Zhiwei Steven Wu, 2014, SODA.

Private matchings and allocations

Tim Roughgarden, Justin Hsu, Aaron Roth, 2013, SIAM J. Comput..

Watch and learn: optimizing from revealed preferences feedback pdf

Aaron Roth, Zhiwei Steven Wu, Jonathan Ullman, 2015, SECO.

Privately Learning Markov Random Fields

Janardhan Kulkarni, Zhiwei Steven Wu, Huanyu Zhang, 2020, ICML.

Predicting with Distributions pdf

Zhiwei Steven Wu, Michael Kearns, Michael Kearns, 2016, COLT.

Logarithmic Query Complexity for Approximate Nash Computation in Large Games

Paul W. Goldberg, Zhiwei Steven Wu, Francisco J. Marmolejo Cossío, 2018, Theory of Computing Systems.

Orthogonal Random Forest for Causal Inference

Zhiwei Steven Wu, Vasilis Syrgkanis, Miruna Oprescu, 2018, ICML.

Strategic Classification from Revealed Preferences

Aaron Roth, Zhiwei Steven Wu, Bo Waggoner, 2017, EC.

Dual Query: Practical Private Query Release for High Dimensional Data

Marco Gaboardi, Justin Hsu, Aaron Roth, 2014, ICML.

Competing Bandits: Learning Under Competition

Yishay Mansour, Zhiwei Steven Wu, Aleksandrs Slivkins, 2017, ITCS.

Privacy for the Protected (Only) pdf

Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev, 2015, ArXiv.

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

Sampath Kannan, Aaron Roth, Zhiwei Steven Wu, 2018, NeurIPS.

Competing Bandits: The Perils of Exploration under Competition pdf

Zhiwei Steven Wu, Aleksandrs Slivkins, Kevin Liu, 2019, ArXiv.

Private Query Release Assisted by Public Data

Raef Bassily, Shay Moran, Aleksandar Nikolov, 2020, ICML.

Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis

Zhiwei Steven Wu, Vidyashankar Sivakumar, Arindam Banerjee, 2020, ICML.

Meritocratic Fairness for Cross-Population Selection

Aaron Roth, Zhiwei Steven Wu, Michael Kearns, 2017, ICML.

Private Reinforcement Learning with PAC and Regret Guarantees pdf

Akshay Krishnamurthy, Zhiwei Steven Wu, Borja Balle, 2020, ICML.

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

Zhiwei Steven Wu, Mingyi Hong, Haoran Sun, 2019, NeurIPS.

Incentivizing Bandit Exploration: Recommendations as Instruments

Nicole Immorlica, Zhiwei Steven Wu, Vasilis Syrgkanis, 2020 .

Bayesian Exploration with Heterogeneous Agents

Nicole Immorlica, Zhiwei Steven Wu, Aleksandrs Slivkins, 2019, WWW.

Accuracy for Sale: Aggregating Data with a Variance Constraint

Aaron Roth, Zhiwei Steven Wu, Katrina Ligett, 2015, ITCS.

Data Privacy Beyond Differential Privacy

Zhiwei Steven Wu, 2017 .

Understanding Gradient Clipping in Private SGD: A Geometric Perspective pdf

Zhiwei Steven Wu, Mingyi Hong, Xiangyi Chen, 2020, NeurIPS.

Locally Private Hypothesis Selection

Aleksandar Nikolov, Janardhan Kulkarni, Zhiwei Steven Wu, 2020, COLT.

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

Seth Neel, Aaron Roth, Zhiwei Steven Wu, 2017, ICML.

Multidimensional Dynamic Pricing for Welfare Maximization

Aaron Roth, Zhiwei Steven Wu, Aleksandrs Slivkins, 2016, EC.

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

Zhiwei Steven Wu, Loren Terveen, Ye Yuan, 2020, Conference on Designing Interactive Systems.

Differentially Private Objective Perturbation: Beyond Smoothness and Convexity pdf

Seth Neel, Aaron Roth, Zhiwei Steven Wu, 2019, ArXiv.

Information Discrepancy in Strategic Learning pdf

Zhiwei Steven Wu, Juba Ziani, Chara Podimata, 2021, ArXiv.

An Algorithmic Framework for Fairness Elicitation

Seth Neel, Aaron Roth, Zhiwei Steven Wu, 2020, FORC.

Stateful Strategic Regression pdf

Zhiwei Steven Wu, Hoda Heidari, Keegan Harris, 2021, NeurIPS.

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

Zhiwei Steven Wu, J. Andrew Bagnell, Sanjiban Choudhury, 2021, ArXiv.

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

Chirag Agarwal, Zhiwei Steven Wu, Himabindu Lakkaraju, 2021, ICML.

Private Post-GAN Boosting pdf

Zhiwei Steven Wu, Cynthia Dwork, Marcel Neunhoeffer, 2020, ICLR.

Leveraging Public Data for Practical Private Query Release

Thomas Steinke, Zhiwei Steven Wu, Jonathan Ullman, 2021, ICML.

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

Zhiwei Steven Wu, Virginia Smith, Shengyuan Hu, 2021, ArXiv.

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

Zhiwei Steven Wu, Terrance Liu, Giuseppe Vietri, 2021, NeurIPS.

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

Zhiwei Steven Wu, Hoda Heidari, Logan Stapleton, 2021, ArXiv.

Bayesian Persuasion for Algorithmic Recourse pdf

Ameet Talwalkar, Zhiwei Steven Wu, Hoda Heidari, 2021, ArXiv.

A Critique of Strictly Batch Imitation Learning pdf

Zhiwei Steven Wu, J. Andrew Bagnell, Sanjiban Choudhury, 2021, ArXiv.

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

Jinfeng Yi, Zhiwei Steven Wu, Xinwei Zhang, 2021, ICML.

Differentially Private Objective Perturbation: Beyond Smoothness and Convexity

Zhiwei Steven Wu, Aaron Roth, Seth Neel, 2019, ArXiv.

Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing

Zhiwei Steven Wu, Brett K. Beaulieu-Jones, James Brian Byrd, 2017, bioRxiv.

D S ] 2 J un 2 01 6 Adaptive Learning with Robust Generalization Guarantees

Zhiwei Steven Wu, Aaron Roth, Rachel Cummings, 2018 .

G T ] 1 J ul 2 01 8 Bayesian Exploration : Incentivizing Exploration in Bayesian Games *

Zhiwei Steven Wu, Y. Mansour, Vasilis Syrgkanis, 2018 .

The Unfair Externalities of Exploration

Zhiwei Steven Wu, Jennifer Wortman Vaughan, Alex Slivkins, 2017 .

Understanding Gradient Clipping in Private SGD: A Geometric Perspective

Zhiwei Steven Wu, Mingyi Hong, Xiangyi Chen, 2020, NeurIPS.

Strategic Classification from Revealed Preferences

Zhiwei Steven Wu, Aaron Roth, Zachary Schutzman, 2017, EC.

Soliciting Stakeholders’ Fairness Notions in Child Maltreatment Predictive Systems

Zhiwei Steven Wu, A. Chouldechova, Ruiqi Wang, 2021, CHI.

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

Zhiwei Steven Wu, A. Banerjee, Mingyi Hong, 2020, ArXiv.

Composition of Learning Models: Predicting with Distributions

Zhiwei Steven Wu, M. Kearns, 2016 .

Oracle Efficient Private Non-Convex Optimization

Seth Neel, Aaron Roth, Zhiwei Steven Wu, 2020, ICML.

How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions

Zhiwei Steven Wu, Kenneth Holstein, Haiyi Zhu, 2022, CHI.

On Privacy and Personalization in Cross-Silo Federated Learning

Zhiwei Steven Wu, Virginia Smith, Shengyuan Hu, 2022, 2206.07902.

Meta-Learning Adversarial Bandits

Zhiwei Steven Wu, Maria-Florina Balcan, M. Khodak, 2022, ArXiv.

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

Zhiwei Steven Wu, Kenneth Holstein, Haiyi Zhu, 2022, FAccT.

Incentivizing Combinatorial Bandit Exploration

Zhiwei Steven Wu, Aleksandrs Slivkins, Xinyan Hu, 2022, NeurIPS.

Bandit Data-Driven Optimization for Crowdsourcing Food Rescue Platforms

Zhiwei Steven Wu, Zheyuan Ryan Shi, R. Ghani, 2022, AAAI.

Private Synthetic Data with Hierarchical Structure

Zhiwei Steven Wu, Terrance Liu, 2022, ArXiv.

“Why Do I Care What’s Similar?” Probing Challenges in AI-Assisted Child Welfare Decision-Making through Worker-AI Interface Design Concepts

Zhiwei Steven Wu, Kenneth Holstein, Haiyi Zhu, 2022, Conference on Designing Interactive Systems.

Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses

Zhiwei Steven Wu, Hoda Heidari, Logan Stapleton, 2021, ICML.

Sequence Model Imitation Learning with Unobserved Contexts

Zhiwei Steven Wu, J. Bagnell, Sanjiban Choudhury, 2022, 2208.02225.

Game-Theoretic Algorithms for Conditional Moment Matching

Zhiwei Steven Wu, J. Bagnell, Sanjiban Choudhury, 2022, ArXiv.

Meta-Learning in Games

Zhiwei Steven Wu, M. Khodak, T. Sandholm, 2022, ArXiv.

Private Synthetic Data for Multitask Learning and Marginal Queries

Zhiwei Steven Wu, M. Kearns, C. Archambeau, 2022, NeurIPS.

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

Zhiwei Steven Wu, Kenneth Holstein, A. Chouldechova, 2022, FAccT.

Fair Federated Learning via Bounded Group Loss

Zhiwei Steven Wu, Virginia Smith, Shengyuan Hu, 2022, 2203.10190.

Bayesian Persuasion for Algorithmic Recourse

Zhiwei Steven Wu, Joon Sik Kim, Ameet S. Talwalkar, 2021, NeurIPS.

Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints

Zhiwei Steven Wu, Ryan M. Rogers, Aaditya Ramdas, 2022, ArXiv.

On Privacy and Personalization in Cross-Silo Federated Learning

Zhiwei Steven Wu, Li-Yu Daisy Liu, Virginia Smith, 2022, NeurIPS.

Sequence Model Imitation Learning with Unobserved Contexts

Zhiwei Steven Wu, J. Bagnell, Sanjiban Choudhury, 2022, NeurIPS.

Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification

Zhiwei Steven Wu, A. Banerjee, Yingxue Zhou, 2020, ICLR.

Policy impacts of statistical uncertainty and privacy

Zhiwei Steven Wu, A. Acquisti, Terrance Liu, 2022, Science.

Policy impacts of statistical uncertainty and privacy Reforms could help funding formulas address unequal distribution of uncertainty from data error and privacy protections

Zhiwei Steven Wu, A. Acquisti, Terrance Liu, 2022 .

Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints

Zhiwei Steven Wu, Ryan M. Rogers, Aaditya Ramdas, 2022, ArXiv.

Strategyproof Decision-Making in Panel Data Settings and Beyond

Zhiwei Steven Wu, A. Agarwal, Chara Podimata, 2022, ArXiv.

Fair Federated Learning via Bounded Group Loss

Zhiwei Steven Wu, Virginia Smith, Shengyuan Hu, 2022, 2203.10190.

Constrained Variational Policy Optimization for Safe Reinforcement Learning

Zhiwei Steven Wu, Bo Li, Ding Zhao, 2022, ICML.

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

Zhiwei Steven Wu, Kenneth Holstein, Haiyi Zhu, 2022, FAccT.

Reinforcement Learning with Stepwise Fairness Constraints

Zhiwei Steven Wu, David C. Parkes, Linjun Zhang, 2022, AISTATS.

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

Zhiwei Steven Wu, Kenneth Holstein, Haiyi Zhu, 2022, CHI.

Provably Fair Federated Learning via Bounded Group Loss

Zhiwei Steven Wu, Virginia Smith, Shengyuan Hu, 2022, ArXiv.

Counterfactual Prediction Under Outcome Measurement Error

Zhiwei Steven Wu, Luke M. Guerdan, Kenneth Holstein, 2023, ArXiv.

Locally Private Hypothesis Selection

Zhiwei Steven Wu, Gautam Kamath, Janardhan Kulkarni, 2020, COLT.

Towards the Unification and Robustness of Perturbation and Gradient Based Explanations

Zhiwei Steven Wu, S. Jabbari, Himabindu Lakkaraju, 2021, ICML.

Locally private nonparametric confidence intervals and sequences

Zhiwei Steven Wu, Aaditya Ramdas, Ian Waudby-Smith, 2022, ArXiv.

Federated Learning as a Network Effects Game

Zhiwei Steven Wu, Virginia Smith, Shengyuan Hu, 2023, ArXiv.

Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization

Zhiwei Steven Wu, J. Langford, Miroslav Dudík, 2022, ArXiv.

Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning

Zhiwei Steven Wu, J. Langford, Miroslav Dudík, 2022, ICML.

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

Zhiwei Steven Wu, Virginia Smith, Shengyuan Hu, 2021, Trans. Mach. Learn. Res..

Information Discrepancy in Strategic Learning

Zhiwei Steven Wu, Juba Ziani, Yahav Bechavod, 2021, ICML.

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

Zhiwei Steven Wu, J. Forlizzi, L. Terveen, 2019, Conference on Designing Interactive Systems.

A nonparametric extension of randomized response for private confidence sets

Zhiwei Steven Wu, Aaditya Ramdas, Ian Waudby-Smith, 2022, 2202.08728.

Counterfactual Prediction Under Outcome Measurement Error

Zhiwei Steven Wu, Luke M. Guerdan, Kenneth Holstein, 2023, FAccT.

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

Seth Neel, Aaron Roth, Zhiwei Steven Wu, 2017, NIPS.

Fully Adaptive Composition in Differential Privacy

Zhiwei Steven Wu, Ryan M. Rogers, Aaditya Ramdas, 2022, ICML.

Strategyproof Decision-Making in Panel Data Settings and Beyond

Zhiwei Steven Wu, A. Agarwal, Chara Podimata, 2022, ArXiv.

Strategic Apple Tasting

Zhiwei Steven Wu, Chara Podimata, Keegan Harris, 2023, 2306.06250.

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

Zhiwei Steven Wu, Luke M. Guerdan, Kenneth Holstein, 2023, FAccT.

Private Data Stream Analysis for Universal Symmetric Norm Estimation

Zhiwei Steven Wu, V. Braverman, Samson Zhou, 2023, APPROX/RANDOM.

Improved Regret for Differentially Private Exploration in Linear MDP

Zhiwei Steven Wu, G. Vietri, Dung Daniel Ngo, 2022, International Conference on Machine Learning.

Adaptive Principal Component Regression with Applications to Panel Data

Zhiwei Steven Wu, A. Agarwal, J. Whitehouse, 2023, ArXiv.

Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints

Zhiwei Steven Wu, Ryan M. Rogers, Aaditya Ramdas, 2022, Neural Information Processing Systems.

Causal Imitation Learning under Temporally Correlated Noise

Zhiwei Steven Wu, J. Bagnell, Sanjiban Choudhury, 2022, ICML.

Confidence-ranked reconstruction of census microdata from published statistics

Zhiwei Steven Wu, C. Dwork, Travis Dick, 2022, Proceedings of the National Academy of Sciences of the United States of America.

Leveraging Public Data for Practical Private Query Release

Zhiwei Steven Wu, Jonathan Ullman, G. Vietri, 2021, ICML.

Meta-Learning in Games

Zhiwei Steven Wu, M. Khodak, T. Sandholm, 2022, ICLR.

Bandit Data-Driven Optimization∗

Zhiwei Steven Wu, R. Ghani, Fei Fang, 2022 .

Causal Feature Discovery through Strategic Modification

Zhiwei Steven Wu, Katrina Ligett, Juba Ziani, 2020, ArXiv.

Nonparametric Extensions of Randomized Response for Private Confidence Sets

Zhiwei Steven Wu, Aaditya Ramdas, Ian Waudby-Smith, 2022, ICML.

Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance

Zhiwei Steven Wu, Gautam Kamath, Xin Gu, 2023, ArXiv.

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

Zhiwei Steven Wu, Haiyi Zhu, Hong Shen, 2020, FAccT.

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

Zhiwei Steven Wu, L. Terveen, Haiyi Zhu, 2019, ArXiv.

Competing Bandits: The Perils of Exploration Under Competition

Zhiwei Steven Wu, Y. Mansour, Aleksandrs Slivkins, 2019, ArXiv.

Inverse Reinforcement Learning without Reinforcement Learning

Zhiwei Steven Wu, J. Bagnell, Sanjiban Choudhury, 2023, ICML.

Meta-Learning Adversarial Bandit Algorithms

Zhiwei Steven Wu, R. Meir, Maria-Florina Balcan, 2023, NeurIPS.

Competing Bandits: e Perils of Exploration under Competition∗

Zhiwei Steven Wu, Y. Mansour, Aleksandrs Slivkins, 2020 .

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

Zhiwei Steven Wu, Virginia Smith, Shengyuan Hu, 2021, Trans. Mach. Learn. Res..

Improved Differentially Private Regression via Gradient Boosting

Zhiwei Steven Wu, Yu-Xiang Wang, Aaron Roth, 2023, 2024 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML).

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

Zhiwei Steven Wu, Kenneth Holstein, Haiyi Zhu, 2022, CHI.

Generating Private Synthetic Data with Genetic Algorithms

Zhiwei Steven Wu, G. Vietri, Terrance Liu, 2023, ICML.

Gaming Helps! Learning from Strategic Interactions in Natural Dynamics

Zhiwei Steven Wu, Katrina Ligett, Juba Ziani, 2021, AISTATS.

Adaptive Principal Component Regression with Applications to Panel Data

Zhiwei Steven Wu, J. Whitehouse, Keegan Harris, 2023, NeurIPS.