I am an assistant professor in the Department of Electrical and Computer Engineering at the University of California, Riverside. In 2023, I got my Ph.D. in Computer Sciences at the University of Wisconsin–Madison, where I was 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 work on interactive machine learning (e.g., active learning, bandits, and reinforcement learning), where my goal is to develop efficient human-in-the-loop learning algorithms and systems. Recently, I became interested in connecting interactive machine learning with foundation models (e.g., large language models), from both algorithmic and systemic perspectives. Specifically, some research topics I would like to further explore include:

• AI safety and alignment (e.g., using reinforcement learning)

• LLM-powered interactive learning agents and systems

• Efficient training and inference for large models

• Testing and evaluation systems for large models

• Theoretical foundations of transformer / attention / interactive ML

\(\bigstar\) I am actively looking for Ph.D. students and visitors (in-person or remote) who are interested in the above mentioned directions. Please (i) fill out this form and (ii) send me an email with your CV/transcripts if you are interested in working with me!

\(\bigstar\) Prof. Cong Liu and I are jointly hiring Ph.D. students who are generally interested in Machine Learning Systems. Please (i) fill out this form and (ii) send both of us an email with your CV/transcripts if you are interested in working on MLSys with us!

Publications

• Infinite Action Contextual Bandits with Reusable Data Exhaust
  Mark Rucker, Yinglun Zhu, and Paul Mineiro
  International Conference on Machine Learning (ICML) 2023

• 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 available at the leading machine learning library Vowpal Wabbit (see here for instructions) and commercially incorporated into Microsoft Azure Personalizer!

• 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]

Professional Services

• Co-organizer: SILO Seminar at UW–Madison (2022 - 2023)

• 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 Journal (MLJ)

  • Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

Teaching Experiences

• Instructor, University of California, Riverside:
  

  • CS/ECE 228 Introduction to Deep Learning, Fall 2023

• 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