Jiuhai Chen

According to our database, Jiuhai Chen authored at least 20 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Automated Data Curation for Robust Language Model Fine-Tuning.

Jiuhai Chen, Jonas Mueller

CoRR, 2024

Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements.

Ming Li, Jiuhai Chen, Lichang Chen, Tianyi Zhou

CoRR, 2024

Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning.

Ming Li, Lichang Chen, Jiuhai Chen, Shwai He, Jiuxiang Gu, Tianyi Zhou

CoRR, 2024

ODIN: Disentangled Reward Mitigates Hacking in RLHF.

Lichang Chen, Chen Zhu, Davit Soselia, Jiuhai Chen, Tianyi Zhou, Tom Goldstein, Heng Huang, Mohammad Shoeybi, Bryan Catanzaro

CoRR, 2024

2023

Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning.

Ming Li, Lichang Chen, Jiuhai Chen, Shwai He, Heng Huang, Jiuxiang Gu, Tianyi Zhou

CoRR, 2023

Quantifying Uncertainty in Answers from any Language Model via Intrinsic and Extrinsic Confidence Assessment.

Jiuhai Chen, Jonas Mueller

CoRR, 2023

From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning.

Ming Li, Yong Zhang, Zhitao Li, Jiuhai Chen, Lichang Chen, Ning Cheng, Jianzong Wang, Tianyi Zhou, Jing Xiao

CoRR, 2023

InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models.

Lichang Chen, Jiuhai Chen, Tom Goldstein, Heng Huang, Tianyi Zhou

CoRR, 2023

When do you need Chain-of-Thought Prompting for ChatGPT?

Jiuhai Chen, Lichang Chen, Heng Huang, Tianyi Zhou

CoRR, 2023

It Takes One to Tango but More Make Trouble? The Number of Demonstrations Needed for In-Context Learning.

Jiuhai Chen, Lichang Chen, Chen Zhu, Tianyi Zhou

CoRR, 2023

GOAT: A Global Transformer on Large-scale Graphs.

Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein

Proceedings of the International Conference on Machine Learning, 2023

How Many Demonstrations Do You Need for In-context Learning?

Jiuhai Chen, Lichang Chen, Chen Zhu, Tianyi Zhou

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer.

Lichang Chen, Jiuhai Chen, Heng Huang, Minhao Cheng

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

2022

A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features.

Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf

CoRR, 2022

Why Propagate Alone? Parallel Use of Labels and Features on Graphs.

Yangkun Wang, Jiarui Jin, Weinan Zhang, Yongyi Yang, Jiuhai Chen, Quan Gan, Yong Yu, Zheng Zhang, Zengfeng Huang, David Wipf

Proceedings of the Tenth International Conference on Learning Representations, 2022

Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features.

Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Gaussian Process Assisted Active Learning of Physical Laws.

Jiuhai Chen, Lulu Kang, Guang Lin

Technometrics, 2021

Particle-based energetic variational inference.

Yiwei Wang, Jiuhai Chen, Chun Liu, Lulu Kang

Stat. Comput., 2021

Understanding the Role of Self-Supervised Learning in Out-of-Distribution Detection Task.

Jiuhai Chen, Chen Zhu, Bin Dai

CoRR, 2021

Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features.

Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf

CoRR, 2021