| 2026 | ACL | Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations. | Bowen Zuo, Dongruo Zhou, Yinglun Zhu |
| 2025 | ICLR | Chain-of-region: Visual Language Models Need Details for Diagram Analysis. | Xue Li, Yiyou Sun, Wei Cheng, Yinglun Zhu, Haifeng Chen |
| 2025 | ICLR | Efficient Sparse PCA via Block-Diagonalization. | Alberto Del Pia, Dekun Zhou, Yinglun Zhu |
| 2025 | RTSS | Lemix: Unified Scheduling for Llm Training and Inference on Multi-Gpu Systems. | Yufei Li, Zexin Li, Yinglun Zhu, Cong Liu |
| 2024 | ACL | An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. | Gantavya Bhatt, Yifang Chen, Arnav Mohanty Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeff A. Bilmes, Simon S. Du, Kevin Jamieson, Jordan T. Ash, Robert D. Nowak |
| 2024 | EMNLP | Efficient Sequential Decision Making with Large Language Models. | Dingyang Chen, Qi Zhang, Yinglun Zhu |
| 2023 | ICML | Infinite Action Contextual Bandits with Reusable Data Exhaust. | Mark Rucker, Yinglun Zhu, Paul Mineiro |
| 2022 | AISTATS | Near Instance Optimal Model Selection for Pure Exploration Linear Bandits. | Yinglun Zhu, Julian Katz-Samuels, Robert D. Nowak |
| 2022 | AISTATS | Pareto Optimal Model Selection in Linear Bandits. | Yinglun Zhu, Robert D. Nowak |
| 2022 | ICML | Contextual Bandits with Large Action Spaces: Made Practical. | Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro |
| 2022 | ICML | Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces. | Yinglun Zhu, Paul Mineiro |
| 2020 | ICML | Robust Outlier Arm Identification. | Yinglun Zhu, Sumeet Katariya, Robert D. Nowak |