Ph.D. Candidate
Department of Computer Sciences
University of Wisconsin–Madison
Google Scholar
GitHub
Email: yinglun@cs.wisc.edu
Office: Wisconsin Institute for Discovery, Room 4241
I am a final-year Ph.D. student in Computer Sciences at the University of Wisconsin–Madison, where I am fortunate to be advised by Robert Nowak. In 2021, I spent a wonderful summer at Microsoft Research NYC, working with Paul Mineiro, Dylan Foster, and John Langford.
I am on the 2022–2023 job market.
I work on interactive machine learning, where I develop efficient human-in-the-loop learning algorithms and systems to speed up the learning and training process. I build algorithmic, statistical, and computational foundations for active learning, bandits, reinforcement learning, and best action identification, and I pay particular attention to connecting deep learning and systems with these interactive learning paradigms. My research has been incorporated into leading ML libraries and commercial products. I also actively participate in interdisciplinary collaborations and use my expertise to help scientists in other domains.
Active Learning with Neural Networks: Insights from Nonparametric Statistics
Yinglun Zhu and Robert Nowak
Conference on Neural Information Processing Systems (NeurIPS), 2022
Efficient Active Learning with Abstention
Yinglun Zhu and Robert Nowak
Conference on Neural Information Processing Systems (NeurIPS), 2022
Contextual Bandits with Large Action Spaces: Made Practical
Yinglun Zhu, Dylan Foster, John Langford, and Paul Mineiro
International Conference on Machine Learning (ICML) 2022, [Code], [Spotlight talk, 6 min]
\(\bigstar\) Now incorporated into the leading machine learning library Vowpal Wabbit (see here for instructions) and commercially available in Azure Personalizer Service!
Contextual Bandits with Smooth Regret: Computational Efficiency in Continuous Action Spaces
Yinglun Zhu and Paul Mineiro
International Conference on Machine Learning (ICML) 2022, [Code]
\(\bigstar\) Selected for a full oral presentation (top 2.1%),
[Oral talk, 17 min]
Near Instance Optimal Model Selection for Pure Exploration Linear Bandits
Yinglun Zhu, Julian Katz-Samuels, and Robert Nowak
International Conference on Artificial Intelligence and Statistics (AISTATS) 2022, [Code]
Pareto Optimal Model Selection in Linear Bandits
Yinglun Zhu and Robert Nowak
International Conference on Artificial Intelligence and Statistics (AISTATS) 2022, [Code]
Pure Exploration in Kernel and Neural Bandits
Yinglun Zhu\(^\star\), Dongruo Zhou\(^\star\), Ruoxi Jiang\(^\star\), Quanquan Gu, Rebecca Willett, and Robert Nowak
Conference on Neural Information Processing Systems (NeurIPS) 2021, [Code]
On Regret with Multiple Best Arms
Yinglun Zhu and Robert Nowak
Conference on Neural Information Processing Systems (NeurIPS) 2020, [Code]
Robust Outlier Arm Identification
Yinglun Zhu, Sumeet Katariya, and Robert Nowak
International Conference on Machine Learning (ICML) 2020, [Code]
Co-organizer: SILO Seminar at UW–Madison (2022–Present)
Conference Reviewer:
Conference on Neural Information Processing Systems (NeurIPS)
\(\bigstar\) Outstanding Reviewer Award in 2021
International Conference on Machine Learning (ICML)
International Conference on Learning Representations (ICLR)
International Conference on Artificial Intelligence and Statistics (AISTATS)
International Symposium on Information Theory (ISIT)
Journal Reviewer:
Journal of Machine Learning Research (JMLR)
Transactions on Machine Learning Research (TMLR)
Machine Learning (ML)
Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Teaching Assistant, University of Wisconsin–Madison:
CS/ECE 761 Mathematical Foundations of Machine Learning, Spring 2020 (Head TA), Spring 2022
CS/ECE/ME 532 Matrix Methods in Machine Learning, Fall 2019
CS 412 Introduction to Numerical Methods, Fall 2018
CS/MATH 513 Numerical Linear Algebra, Spring 2018