I am an assistant professor in the ECE department at UC Riverside.

My research broadly lies in machine learning and AI. My current interests include LLMs, RL, agents, test-time training and scaling, and interactive learning.

Previously, I received my Ph.D. in CS from UW–Madison, where I was advised by Robert Nowak. During my Ph.D., I worked on theoretical and algorithmic foundations of interactive machine learning.

Selected Work [Full List]

• Closing the Reflection Gap: A Free Calibration Bonus for Agentic RL
  Yinglun Zhu
  Preprint (under review) 2026

• Test-Time Matching: Unlocking Compositional Reasoning in Multimodal Models
  Yinglun Zhu, Jiancheng Zhang, and Fuzhi Tang
  International Conference on Learning Representations (ICLR) 2026, [News] [Blog] [Code]

• Strategic Scaling of Test-Time Compute: A Bandit Learning Approach
  Bowen Zuo and Yinglun Zhu
  International Conference on Learning Representations (ICLR) 2026

• Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance
  Jiancheng Zhang, Meiqing Li, Qi Zhang, and Yinglun Zhu
  International Conference on Machine Learning (ICML) 2026

• Online Finetuning Decision Transformers with Pure RL Gradients
  Junkai Luo and Yinglun Zhu
  Preprint (under review) 2026

• Towards Multimodal Active Learning: Efficient Learning with Limited Data Pairs
  Jiancheng Zhang and Yinglun Zhu
  Transactions on Machine Learning Research (TMLR) 2026

• Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations
  Bowen Zuo, Dongruo Zhou, and Yinglun Zhu
  Annual Meeting of the Association for Computational Linguistics (ACL Findings) 2026

• LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems
  Yufei Li, Zexin Li, Yinglun Zhu, and Cong Liu
  Real-Time Systems Symposium (RTSS) 2025
  ★ Outstanding Paper Award

• 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, [Code]

• Interactive Machine Learning: From Theory to Scale
  Yinglun Zhu
  Ph.D. Dissertation, University of Wisconsin–Madison, 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]
  ★ Incorporated into the leading machine learning library Vowpal Wabbit; see here for instructions

• Contextual Bandits with Smooth Regret: Computational Efficiency in Continuous Action Spaces
  Yinglun Zhu and Paul Mineiro
  International Conference on Machine Learning (ICML) 2022, [Code]
  ★ 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]

Group

Ph.D. Students: Jiancheng Zhang (Fall 2024 - present), Bowen Zuo (Fall 2024 - present)

Teaching

• EE 269 Foundation Models and Generative AI: S24, S25, S26

• EE/CS 228 Introduction to Deep Learning: F23, F24, W26

• EE 114 Probability, Random Variables, and Random Processes in Electrical Engineering: W25, F25