← Authors

Zhiwei Steven Wu

21 papers · 2014–2025 · 4 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (5) πŸŒ‰ Interdisciplinary Bridge 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (15) 🧭 Keyword Pioneer 🌈 Renaissance Researcher (6) 🌍 Conference Polyglot (4) πŸƒ Academic Marathon (11) 🌟 Keyword Trendsetter Combo (3) πŸ”₯ Unstoppable (5) πŸ’Ž Century Club (21) πŸ—ƒοΈ Keyword Collector (93) πŸ“ˆ Trend Setter ⚑ Prolific Year (6)

Conferences

ICML (7) NIPS (7) COLT (6) AAAI (1)

Top co-authors

Aaron Roth (6) Michael Kearns (4) Vasilis Syrgkanis (3) Fei Fang (2) Justin Whitehouse (2) Martin Bertran (1) Alekh Agarwal (1) Abhishek Shetty (1) Pratiksha Thaker (1) Rachel Cummings (1)

Research topics

Differential Privacy (1) Privacy (1)

Keywords

differential privacy (5) online learning (3) contextual bandit (3) regret bound (3) sample complexity (3) regret minimization (2) pac learning (2) greedy algorithm (2) distribution shift (2) robust learning (2) imitation learning (1) out-of-distribution generalization (1) machine unlearning (1) covariate shift (1) regret analysis (1) online convex optimization (1) algorithmic fairness (1) classification noise (1) agnostic learning (1) bayesian inference (1)

Papers

Time-Uniform Self-Normalized Concentration for Vector-Valued Processes (Extended Abstract) COLT 2025
Orthogonal Causal Calibration (Extended Abstract) COLT 2025
Regret Minimization in Stackelberg Games with Side Information NIPS 2024
Multi-Agent Imitation Learning: Value is Easy, Regret is Hard NIPS 2024
Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift NIPS 2024
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable NIPS 2024
Oracle-Efficient Differentially Private Learning with Public Data NIPS 2024
On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift NIPS 2024
Bandit Data-Driven Optimization for Crowdsourcing Food Rescue Platforms AAAI 2022
Locally Private Hypothesis Selection COLT 2020
Orthogonal Random Forest for Causal Inference ICML 2019
Locally Private Bayesian Inference for Count Models ICML 2019
Fair Regression: Quantitative Definitions and Reduction-Based Algorithms ICML 2019
The Externalities of Exploration and How Data Diversity Helps Exploitation COLT 2018
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem NIPS 2018
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness ICML 2018
Semiparametric Contextual Bandits ICML 2018
Meritocratic Fairness for Cross-Population Selection ICML 2017
Predicting with Distributions COLT 2017
Adaptive Learning with Robust Generalization Guarantees COLT 2016
Dual Query: Practical Private Query Release for High Dimensional Data ICML 2014