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ORCIDYinglun Zhu
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2020 – today
- 2026
[i22]Junkai Luo, Yinglun Zhu:
Online Finetuning Decision Transformers with Pure RL Gradients. CoRR abs/2601.00167 (2026)- 2025
- [c15]Xue Li, Yiyou Sun, Wei Cheng, Yinglun Zhu, Haifeng Chen:
Chain-of-region: Visual Language Models Need Details for Diagram Analysis. ICLR 2025 - [c14]Alberto Del Pia, Dekun Zhou, Yinglun Zhu:
Efficient Sparse PCA via Block-Diagonalization. ICLR 2025 - [c13]Yufei Li, Zexin Li, Yinglun Zhu, Cong Liu:
Lemix: Unified Scheduling for Llm Training and Inference on Multi-Gpu Systems. RTSS 2025: 566-579 - [i21]Zexin Li, Jiancheng Zhang, Yinglun Zhu, Cong Liu:
Mixtraining: A Better Trade-Off Between Compute and Performance. CoRR abs/2502.19513 (2025) - [i20]Bowen Zuo, Yinglun Zhu:
Strategic Scaling of Test-Time Compute: A Bandit Learning Approach. CoRR abs/2506.12721 (2025) - [i19]Yufei Li, Zexin Li, Yinglun Zhu, Cong Liu:
LeMix: Unified Scheduling for LLM Training and Inference on Multi-GPU Systems. CoRR abs/2507.21276 (2025) - [i18]Jiancheng Zhang, Yinglun Zhu:
Towards Multimodal Active Learning: Efficient Learning with Limited Paired Data. CoRR abs/2510.03247 (2025) - [i17]Yinglun Zhu, Jiancheng Zhang, Fuzhi Tang:
Test-Time Matching: Unlocking Compositional Reasoning in Multimodal Models. CoRR abs/2510.07632 (2025) - [i16]Yinglun Zhu:
Interactive Machine Learning: From Theory to Scale. CoRR abs/2512.23924 (2025) - 2024
- [j1]Jifan Zhang, Yifang Chen, Gregory Canal, Arnav Mohanty Das, Gantavya Bhatt, Stephen Mussmann, Yinglun Zhu, Jeff A. Bilmes, Simon Shaolei Du, Kevin Jamieson, Robert D. Nowak:
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning. J. Data-centric Mach. Learn. Res. 1: (7):1-43 (2024) - [c12]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:
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. ACL (Findings) 2024: 6549-6560 - [c11]Dingyang Chen, Qi Zhang, Yinglun Zhu:
Efficient Sequential Decision Making with Large Language Models. EMNLP 2024: 9157-9170 - [i15]Gantavya Bhatt, Yifang Chen, Arnav Mohanty Das, Jifan Zhang, Sang T. Truong, Stephen Mussmann, Yinglun Zhu, Jeffrey A. Bilmes, Simon S. Du, Kevin Jamieson, Jordan T. Ash, Robert D. Nowak:
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. CoRR abs/2401.06692 (2024) - [i14]Dingyang Chen, Qi Zhang, Yinglun Zhu:
Efficient Sequential Decision Making with Large Language Models. CoRR abs/2406.12125 (2024) - [i13]Alberto Del Pia, Dekun Zhou, Yinglun Zhu:
Efficient Sparse PCA via Block-Diagonalization. CoRR abs/2410.14092 (2024) - 2023
- [c10]Mark Rucker, Yinglun Zhu, Paul Mineiro:
Infinite Action Contextual Bandits with Reusable Data Exhaust. ICML 2023: 29259-29274 - [i12]Mark Rucker, Yinglun Zhu, Paul Mineiro:
Infinite Action Contextual Bandits with Reusable Data Exhaust. CoRR abs/2302.08551 (2023) - [i11]Jifan Zhang, Yifang Chen, Gregory Canal, Stephen Mussmann, Yinglun Zhu, Simon Shaolei Du, Kevin Jamieson, Robert D. Nowak:
LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning. CoRR abs/2306.09910 (2023) - 2022
- [c9]Yinglun Zhu, Julian Katz-Samuels, Robert D. Nowak:
Near Instance Optimal Model Selection for Pure Exploration Linear Bandits. AISTATS 2022: 6735-6769 - [c8]Yinglun Zhu, Robert D. Nowak:
Pareto Optimal Model Selection in Linear Bandits. AISTATS 2022: 6793-6813 - [c7]Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro:
Contextual Bandits with Large Action Spaces: Made Practical. ICML 2022: 27428-27453 - [c6]Yinglun Zhu, Paul Mineiro:
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces. ICML 2022: 27574-27590 - [c5]Yinglun Zhu, Robert Nowak:
Active Learning with Neural Networks: Insights from Nonparametric Statistics. NeurIPS 2022 - [c4]Yinglun Zhu, Robert Nowak:
Efficient Active Learning with Abstention. NeurIPS 2022 - [i10]Yinglun Zhu, Robert D. Nowak:
Efficient Active Learning with Abstention. CoRR abs/2204.00043 (2022) - [i9]Yinglun Zhu, Dylan J. Foster, John Langford, Paul Mineiro:
Contextual Bandits with Large Action Spaces: Made Practical. CoRR abs/2207.05836 (2022) - [i8]Yinglun Zhu, Paul Mineiro:
Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces. CoRR abs/2207.05849 (2022) - [i7]Yinglun Zhu, Robert D. Nowak:
Active Learning with Neural Networks: Insights from Nonparametric Statistics. CoRR abs/2210.08367 (2022) - 2021
- [c3]Yinglun Zhu, Dongruo Zhou
, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert Nowak:
Pure Exploration in Kernel and Neural Bandits. NeurIPS 2021: 11618-11630 - [i6]Yinglun Zhu, Robert D. Nowak:
Pareto Optimal Model Selection in Linear Bandits. CoRR abs/2102.06593 (2021) - [i5]Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert D. Nowak:
Pure Exploration in Kernel and Neural Bandits. CoRR abs/2106.12034 (2021) - [i4]Yinglun Zhu, Julian Katz-Samuels, Robert D. Nowak:
Near Instance Optimal Model Selection for Pure Exploration Linear Bandits. CoRR abs/2109.05131 (2021) - 2020
- [c2]Yinglun Zhu, Sumeet Katariya, Robert D. Nowak:
Robust Outlier Arm Identification. ICML 2020: 11566-11575 - [c1]Yinglun Zhu, Robert Nowak:
On Regret with Multiple Best Arms. NeurIPS 2020 - [i3]Yinglun Zhu, Robert D. Nowak:
On Regret with Multiple Best Arms. CoRR abs/2006.14785 (2020) - [i2]Yinglun Zhu, Sumeet Katariya, Robert Nowak:
Robust Outlier Arm Identification. CoRR abs/2009.09988 (2020)
2010 – 2019
- 2017
- [i1]Jiefeng Chen, Zihang Meng, Changtian Sun, Wei Tang, Yinglun Zhu:
ReabsNet: Detecting and Revising Adversarial Examples. CoRR abs/1712.08250 (2017)
Coauthor Index
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