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 focuses on machine learning, reinforcement learning, and foundation models (e.g., LLMs), with an emphasis on developing efficient and reliable learning algorithms and systems for large-scale, multimodal problems. I am particularly interested in exploring the following research areas:

• LLM-powered learning and decision making/RL agents

• Efficient training and inference algorithms and systems

• (Multimodal) Data selection, distillation, and generation

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

• Foundations of learning algorithms and foundation models

\(\bigstar\) Prospective Students. I am looking for multiple Ph.D. students (Fall 2025, fully-funded) and visitors/interns who are generally interested in ML/RL/LLM 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 your CV and transcripts.

Teaching

• Fall 2024: EE/CS 228 Introduction to Deep Learning

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

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

Publications

• Efficient Sequential Decision Making with Large Language Models
  Dingyang Chen, Qi Zhang, and Yinglun Zhu
  Conference on Empirical Methods in Natural Language Processing (EMNLP) 2024

• An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models
  Gantavya Bhatt, Yifang Chen, Arnav M. Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeffrey Bilmes, Simon S. Du, Kevin Jamieson, Jordan T. Ash, and Robert Nowak
  Findings of the Association for Computational Linguistics: ACL (ACL Findings) 2024

• LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
  Jifan Zhang, Yifang Chen, Gregory Canal, Arnav Das, Gantavya Bhatt, Stephen Mussmann, Yinglun Zhu, Jeffrey Bilmes, Simon Du, Kevin Jamieson, and Robert Nowak
  Journal of Data-Centric Machine Learning Research 2024
  \(\bigstar\) Also selected for a Poster Award at Midwest ML Symposium 2024

• 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]
  \(\bigstar\) 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%)

• 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]