I am an assistant professor in ECE, CSE, and STAT departments at UC Riverside. I received my Ph.D. in CS from the University of Wisconsin–Madison in 2023, where I was advised by Robert Nowak. I also spent time as a research intern at Microsoft Research NYC, where I was mentored by 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 agents

• Test-time scaling and alignment

• Multimodal learning and data selection

• Efficient algorithms for training and inference

\(\bigstar\) Prospective Students. I am looking for Ph.D. students and visitors/interns who are generally interested in ML/RL/LLMs. 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.

Ph.D. Students

Jiancheng Zhang (Fall 2024 - present), Bowen Zuo (Fall 2024 - present)

Selected Preprints

• Strategic Scaling of Test-Time Compute: A Bandit Learning Approach
  Bowen Zuo and Yinglun Zhu
  

Publications

• Chain-of-region: Visual Language Models Need Details for Diagram Analysis
  Xue Li, Yiyou Sun, Wei Cheng, Yinglun Zhu, and Haifeng Chen
  International Conference on Learning Representations (ICLR) 2025

• Efficient Sparse PCA via Block-Diagonalization
  Alberto Del Pia, Dekun Zhou, and Yinglun Zhu
  International Conference on Learning Representations (ICLR) 2025

• 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]

Teaching

• Spring 2025: EE 260 Large Models and Advances in AI

• Winter 2025: EE 114 Probability, Random Variables, and Random Processes in Electrical Engineering

• 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