Wu, Zhiwei Steven

21 publications

COLT 2025 Orthogonal Causal Calibration (Extended Abstract) Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu

COLT 2025 Time-Uniform Self-Normalized Concentration for Vector-Valued Processes (Extended Abstract) Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas

NeurIPS 2024 Bridging Multicalibration and Out-of-Distribution Generalization Beyond Covariate Shift Jiayun Wu, Jiashuo Liu, Peng Cui, Zhiwei Steven Wu

NeurIPS 2024 Multi-Agent Imitation Learning: Value Is Easy, Regret Is Hard Jingwu Tang, Gokul Swamy, Fei Fang, Zhiwei Steven Wu

NeurIPS 2024 On the Benefits of Public Representations for Private Transfer Learning Under Distribution Shift Pratiksha Thaker, Amrith Setlur, Zhiwei Steven Wu, Virginia Smith

NeurIPS 2024 Oracle-Efficient Differentially Private Learning with Public Data Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty, Zhiwei Steven Wu

NeurIPS 2024 Reconstruction Attacks on Machine Unlearning: Simple Models Are Vulnerable Martin Bertran, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu

NeurIPS 2024 Regret Minimization in Stackelberg Games with Side Information Keegan Harris, Zhiwei Steven Wu, Maria-Florina Balcan

AAAI 2022 Bandit Data-Driven Optimization for Crowdsourcing Food Rescue Platforms Zheyuan Ryan Shi, Zhiwei Steven Wu, Rayid Ghani, Fei Fang

COLT 2020 Locally Private Hypothesis Selection Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang

ICML 2019 Fair Regression: Quantitative Definitions and Reduction-Based Algorithms Alekh Agarwal, Miroslav Dudik, Zhiwei Steven Wu

ICML 2019 Locally Private Bayesian Inference for Count Models Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, Hanna Wallach

ICML 2019 Orthogonal Random Forest for Causal Inference Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu

NeurIPS 2018 A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem Sampath Kannan, Jamie H Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu

ICML 2018 Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu

ICML 2018 Semiparametric Contextual Bandits Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis

COLT 2018 The Externalities of Exploration and How Data Diversity Helps Exploitation Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu

ICML 2017 Meritocratic Fairness for Cross-Population Selection Michael Kearns, Aaron Roth, Zhiwei Steven Wu

COLT 2017 Predicting with Distributions Michael Kearns, Zhiwei Steven Wu

COLT 2016 Adaptive Learning with Robust Generalization Guarantees Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu

ICML 2014 Dual Query: Practical Private Query Release for High Dimensional Data Marco Gaboardi, Emilio Jesus Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu