I am an assistant professor in ECE/CSE departments at UC Riverside. In 2023, I got my Ph.D. in CS 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.

My research is in machine learning, with a focus on developing interactive learning paradigms (e.g., active learning, bandits, and RL) to improve data efficiency and compute efficiency. Recently, I became interested in connecting interactive machine learning with large models (e.g., LLMs), from both algorithmic and systemic perspectives. Research topics I would like to further explore include:

• Data selection, distillation, and generation

• Efficient training and inference algorithms and systems

• LLM-powered interactive learning agents and systems

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

• Theoretical foundations of interactive ML and large models

\(\bigstar\) Prospective Students. I am actively looking for Ph.D. students (Fall 2024/Spring 2025, fully-funded) and short-term visitors/interns who are generally interested in AI/ML and/or ML Systems. I'm happy to work with students with CS/ECE/Math/Stat/Physics or other STEM backgrounds. If you are interested in joining my lab, please (i) fill out this form and (ii) drop me an email with you CV and transcripts.

Teaching

• Spring 2024: EE 260 Large Models and Advances in AI

• Fall 2023: EE/CS 228 Introduction to Deep Learning

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]