Zhiwei Steven Wu

Affiliations:
  • Carnegie Mellon University, Institute for Software Research, Pittsburgh, PA, USA
  • University of Minnesota, Department of Computer Science and Engineering, Minneapolis, MN, USA
  • Microsoft Research, New York City, NY, USA
  • University of Pennsylvania, Department of Computer and Information Science, Philadelphia, PA, USA (PhD 2017)


According to our database1, Zhiwei Steven Wu authored at least 104 papers between 2015 and 2022.

Collaborative distances:

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2022
Bayesian Exploration: Incentivizing Exploration in Bayesian Games.
Oper. Res., 2022

On Privacy and Personalization in Cross-Silo Federated Learning.
CoRR, 2022

Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints.
CoRR, 2022

Private Synthetic Data with Hierarchical Structure.
CoRR, 2022

Incentivizing Combinatorial Bandit Exploration.
CoRR, 2022

Minimax Optimal Online Imitation Learning via Replay Estimation.
CoRR, 2022

Meta-Learning Adversarial Bandits.
CoRR, 2022

Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders.
CoRR, 2022

Extended Analysis of "How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions".
CoRR, 2022

A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms.
CoRR, 2022

Provably Fair Federated Learning via Bounded Group Loss.
CoRR, 2022

Fully Adaptive Composition in Differential Privacy.
CoRR, 2022

Locally private nonparametric confidence intervals and sequences.
CoRR, 2022

Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization.
CoRR, 2022

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective.
CoRR, 2022

Causal Imitation Learning under Temporally Correlated Noise.
CoRR, 2022

Improved Regret for Differentially Private Exploration in Linear MDP.
CoRR, 2022

Constrained Variational Policy Optimization for Safe Reinforcement Learning.
CoRR, 2022

Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders.
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.
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.
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.
Proceedings of the CHI '22: CHI Conference on Human Factors in Computing Systems, New Orleans, LA, USA, 29 April 2022, 2022

"Why Do I Care What's Similar?" Probing Challenges in AI-Assisted Child Welfare Decision-Making through Worker-AI Interface Design Concepts.
Proceedings of the DIS '22: Designing Interactive Systems Conference, Virtual Event, Australia, June 13, 2022

2021
Private Hypothesis Selection.
IEEE Trans. Inf. Theory, 2021

Bayesian Persuasion for Algorithmic Recourse.
CoRR, 2021

A Critique of Strictly Batch Imitation Learning.
CoRR, 2021

Private Multi-Task Learning: Formulation and Applications to Federated Learning.
CoRR, 2021

Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses.
CoRR, 2021

Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy.
CoRR, 2021

Of Moments and Matching: Trade-offs and Treatments in Imitation Learning.
CoRR, 2021

Information Discrepancy in Strategic Learning.
CoRR, 2021

Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Stateful Strategic Regression.
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.
Proceedings of the 38th International Conference on Machine Learning, 2021

Incentivizing Compliance with Algorithmic Instruments.
Proceedings of the 38th International Conference on Machine Learning, 2021

Leveraging Public Data for Practical Private Query Release.
Proceedings of the 38th International Conference on Machine Learning, 2021

Towards the Unification and Robustness of Perturbation and Gradient Based Explanations.
Proceedings of the 38th International Conference on Machine Learning, 2021

Bypassing the Ambient Dimension: Private SGD with Gradient Subspace Identification.
Proceedings of the 9th International Conference on Learning Representations, 2021

Private Post-GAN Boosting.
Proceedings of the 9th International Conference on Learning Representations, 2021

An Algorithmic Framework for Fairness Elicitation.
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.
Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems.
Proceedings of the CHI '21: CHI Conference on Human Factors in Computing Systems, 2021

Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Gaming Helps! Learning from Strategic Interactions in Natural Dynamics.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Multidimensional Dynamic Pricing for Welfare Maximization.
ACM Trans. Economics and Comput., 2020

Bandit Data-driven Optimization: AI for Social Good and Beyond.
CoRR, 2020

Competing Bandits: The Perils of Exploration Under Competition.
CoRR, 2020

Private Stochastic Non-Convex Optimization: Adaptive Algorithms and Tighter Generalization Bounds.
CoRR, 2020

Greedy Algorithm almost Dominates in Smoothed Contextual Bandits.
CoRR, 2020

Causal Feature Discovery through Strategic Modification.
CoRR, 2020

Incentivizing Exploration with Selective Data Disclosure.
Proceedings of the EC '20: The 21st ACM Conference on Economics and Computation, 2020

Understanding Gradient Clipping in Private SGD: A Geometric Perspective.
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.
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.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Privately Learning Markov Random Fields.
Proceedings of the 37th International Conference on Machine Learning, 2020

New Oracle-Efficient Algorithms for Private Synthetic Data Release.
Proceedings of the 37th International Conference on Machine Learning, 2020

Private Reinforcement Learning with PAC and Regret Guarantees.
Proceedings of the 37th International Conference on Machine Learning, 2020

Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis.
Proceedings of the 37th International Conference on Machine Learning, 2020

Oracle Efficient Private Non-Convex Optimization.
Proceedings of the 37th International Conference on Machine Learning, 2020

Private Query Release Assisted by Public Data.
Proceedings of the 37th International Conference on Machine Learning, 2020

Locally Private Hypothesis Selection.
Proceedings of the Conference on Learning Theory, 2020

Keeping Designers in the Loop: Communicating Inherent Algorithmic Trade-offs Across Multiple Objectives.
Proceedings of the DIS '20: Designing Interactive Systems Conference 2020, 2020

2019
Logarithmic Query Complexity for Approximate Nash Computation in Large Games.
Theory Comput. Syst., 2019

Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM.
J. Priv. Confidentiality, 2019

Designing Interfaces to Help Stakeholders Comprehend, Navigate, and Manage Algorithmic Trade-Offs.
CoRR, 2019

Differentially Private Objective Perturbation: Beyond Smoothness and Convexity.
CoRR, 2019

Eliciting and Enforcing Subjective Individual Fairness.
CoRR, 2019

Competing Bandits: The Perils of Exploration under Competition.
CoRR, 2019

Bayesian Exploration with Heterogeneous Agents.
Proceedings of the World Wide Web Conference, 2019

Locally Private Gaussian Estimation.
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.
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.
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.
Proceedings of the 36th International Conference on Machine Learning, 2019

Orthogonal Random Forest for Causal Inference.
Proceedings of the 36th International Conference on Machine Learning, 2019

Fair Regression: Quantitative Definitions and Reduction-Based Algorithms.
Proceedings of the 36th International Conference on Machine Learning, 2019

How to Use Heuristics for Differential Privacy.
Proceedings of the 60th IEEE Annual Symposium on Foundations of Computer Science, 2019

An Empirical Study of Rich Subgroup Fairness for Machine Learning.
Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

The Perils of Exploration under Competition: A Computational Modeling Approach.
Proceedings of the 2019 ACM Conference on Economics and Computation, 2019

2018
Privacy-Preserving Distributed Deep Learning for Clinical Data.
CoRR, 2018

Incentivizing Exploration with Unbiased Histories.
CoRR, 2018

Orthogonal Random Forest for Heterogeneous Treatment Effect Estimation.
CoRR, 2018

Locally Private Bayesian Inference for Count Models.
CoRR, 2018

Strategic Classification from Revealed Preferences.
Proceedings of the 2018 ACM Conference on Economics and Computation, 2018

A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Competing Bandits: Learning Under Competition.
Proceedings of the 9th Innovations in Theoretical Computer Science Conference, 2018

Semiparametric Contextual Bandits.
Proceedings of the 35th International Conference on Machine Learning, 2018

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness.
Proceedings of the 35th International Conference on Machine Learning, 2018

The Externalities of Exploration and How Data Diversity Helps Exploitation.
Proceedings of the Conference On Learning Theory, 2018

2017
Fairness Incentives for Myopic Agents.
Proceedings of the 2017 ACM Conference on Economics and Computation, 2017

Meritocratic Fairness for Cross-Population Selection.
Proceedings of the 34th International Conference on Machine Learning, 2017

Predicting with Distributions.
Proceedings of the 30th Conference on Learning Theory, 2017

2016
Private Matchings and Allocations.
SIAM J. Comput., 2016

Private algorithms for the protected in social network search.
Proc. Natl. Acad. Sci. USA, 2016

Dual Query: Practical Private Query Release for High Dimensional Data.
J. Priv. Confidentiality, 2016

Jointly Private Convex Programming.
Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms, 2016

Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs.
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.
Proceedings of the 2016 ACM Conference on Innovations in Theoretical Computer Science, 2016

Adaptive Learning with Robust Generalization Guarantees.
Proceedings of the 29th Conference on Learning Theory, 2016

2015
Watch and learn: optimizing from revealed preferences feedback.
SIGecom Exch., 2015

Privacy for the Protected (Only).
CoRR, 2015

Privacy and Truthful Equilibrium Selection for Aggregative Games.
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).
Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms, 2015

Inducing Approximately Optimal Flow Using Truthful Mediators.
Proceedings of the Sixteenth ACM Conference on Economics and Computation, 2015

Accuracy for Sale: Aggregating Data with a Variance Constraint.
Proceedings of the 2015 Conference on Innovations in Theoretical Computer Science, 2015


  Loading...