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 processes. 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.

Publications

• 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], [Oral talk, 17 min (top 2.1%)]

• 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]

Professional Services

• Co-organizer: SILO Seminar at UW–Madison (2022–Present)

• Conference Reviewer:

  • Conference on Neural Information Processing Systems (NeurIPS)
    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)

Teaching Experiences

• 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