ReabsNet: Detecting and Revising Adversarial Examples. - Jiefeng Chen 001; Zihang Meng; Changtian Sun; Wei Tang; Yinglun Zhu (CoRR, 2017)

Robust Outlier Arm Identification. - Yinglun Zhu; Sumeet Katariya; Robert D. Nowak (ICML, 2020)

On Regret with Multiple Best Arms. - Yinglun Zhu; Robert Nowak 001 (NeurIPS, 2020)

Pure Exploration in Kernel and Neural Bandits. - Yinglun Zhu; Dongruo Zhou; Ruoxi Jiang; Quanquan Gu; Rebecca Willett; Robert Nowak 001 (NeurIPS, 2021)

Near Instance Optimal Model Selection for Pure Exploration Linear Bandits. - Yinglun Zhu; Julian Katz-Samuels; Robert D. Nowak (AISTATS, 2022)

Pareto Optimal Model Selection in Linear Bandits. - Yinglun Zhu; Robert D. Nowak (AISTATS, 2022)

Contextual Bandits with Large Action Spaces: Made Practical. - Yinglun Zhu; Dylan J. Foster; John Langford 001; Paul Mineiro (ICML, 2022)

Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces. - Yinglun Zhu; Paul Mineiro (ICML, 2022)

Active Learning with Neural Networks: Insights from Nonparametric Statistics. - Yinglun Zhu; Robert Nowak 001 (NeurIPS, 2022)

Efficient Active Learning with Abstention. - Yinglun Zhu; Robert Nowak 001 (NeurIPS, 2022)

Infinite Action Contextual Bandits with Reusable Data Exhaust. - Mark Rucker; Yinglun Zhu; Paul Mineiro (ICML, 2023)

LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning. - Jifan Zhang; Yifang Chen 001; Gregory Canal; Stephen Mussmann; Yinglun Zhu; Simon Shaolei Du; Kevin G. Jamieson; Robert D. Nowak (CoRR, 2023)

An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. - Gantavya Bhatt; Yifang Chen 001; Arnav Mohanty Das; Jifan Zhang; Sang T. Truong; Stephen Mussmann; Yinglun Zhu; Jeff A. Bilmes; Simon S. Du; Kevin G. Jamieson; Jordan T. Ash; Robert D. Nowak (ACL (Findings), 2024)

Efficient Sequential Decision Making with Large Language Models. - Dingyang Chen; Qi Zhang; Yinglun Zhu (EMNLP, 2024)

Chain-of-region: Visual Language Models Need Details for Diagram Analysis. - Xue Li; Yiyou Sun; Wei Cheng 002; Yinglun Zhu; Haifeng Chen (ICLR, 2025)

Efficient Sparse PCA via Block-Diagonalization. - Alberto Del Pia; Dekun Zhou; Yinglun Zhu (ICLR, 2025)

Mixtraining: A Better Trade-Off Between Compute and Performance. - Zexin Li; Jiancheng Zhang; Yinglun Zhu; Cong Liu (CoRR, 2025)

Strategic Scaling of Test-Time Compute: A Bandit Learning Approach. - Bowen Zuo; Yinglun Zhu (CoRR, 2025)

LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems. - Yufei Li; Zexin Li; Yinglun Zhu; Cong Liu (CoRR, 2025)