# Zhiwei Steven Wu

According to our database

Collaborative distances:

^{1}, Zhiwei Steven Wu authored at least 57 papers between 2015 and 2020.Collaborative distances:

## Timeline

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#### On csauthors.net:

## Bibliography

2020

Multidimensional Dynamic Pricing for Welfare Maximization.

ACM Trans. Economics and Comput., 2020

Understanding Gradient Clipping in Private SGD: A Geometric Perspective.

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

Private Query Release Assisted by Public Data.

CoRR, 2020

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

CoRR, 2020

Locally Private Hypothesis Selection.

CoRR, 2020

Privately Learning Markov Random Fields.

CoRR, 2020

Causal Feature Discovery through Strategic Modification.

CoRR, 2020

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

CoRR, 2020

Metric-Free Individual Fairness in Online Learning.

CoRR, 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

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

CoRR, 2019

Differentially Private Objective Perturbation: Beyond Smoothness and Convexity.

CoRR, 2019

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

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

Private Hypothesis Selection.

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.

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

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

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 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

Bayesian Exploration: Incentivizing Exploration in Bayesian Games.

Proceedings of the 2016 ACM Conference on Economics and Computation, 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