Daniel Ngo

I am a fifth-year Ph.D. student at the University of Minnesota, co-advised by Professors Steven Wu and Maria Gini. I’m currently visiting Carnegie Mellon University at Software and Societal Systems Department.

My research interests lie in the intersection of machine learning, algorithmic game theory and econometrics. I am particularly interested in online machine learning and exploration-exploitation tradeoff, and their impact in socioeconomic environments. My current work focuses on bandit algorithms, differential privacy, federated learning, and strategic learning.

I received a B.S in Computer Science and Mathematics from Dickinson College, where I worked on a Genetic Algorithm honor project with Professor Grant Braught.

I am happy to talk about bandit algorithms and incentivizing exploration. Feel free to contact me at ngo00054 [at] umn.edu.

For more detail, please see my CV [CV]


Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration [arxiv]
Daniel Ngo, Keegan Harris, Anish Agarwal, Vasilis Syrgkanis, Zhiwei Steven Wu

Federated Learning as a Network Effects Game [arxiv]
Shengyuan Hu, Daniel Ngo, Shuran Zheng, Virginia Smith, Zhiwei Steven Wu

Incentivizing Combinatorial Bandit Exploration [arxiv]
Proceedings of the Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022)
Xinyan Hu, Daniel Ngo, Aleksandrs Slivkins, Zhiwei Steven Wu

Improved Regret for Differentially Private Exploration in Linear MDP [arxiv]
Proceedings of the Thirty-ninth International Conference on Machine Learning (ICML 2022)
Daniel Ngo, Giuseppe Vietri, Zhiwei Steven Wu

Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses [arxiv]
Proceedings of the Thirty-ninth International Conference on Machine Learning (ICML 2022)
Keegan Harris, Daniel Ngo, Logan Stapleton, Hoda Heidari, Zhiwei Steven Wu

Incentivizing Compliance with Algorithmic Instruments [arxiv]
Proceedings of the Thirty-eighth International Conference on Machine Learning (ICML 2021)
Daniel Ngo, Logan Stapleton, Vasilis Syrgkanis, Zhiwei Steven Wu

Attentional autoencoder for weighted implicit collaborative filtering
Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems
Hoang-Vu Dang, Dung Ngo