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ORCIDJiuhai Chen
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2020 – today
- 2024
[j3]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf:
Graph Neural Networks Formed via Layer-wise Ensembles of Heterogeneous Base Models. Trans. Mach. Learn. Res. 2024 (2024)- [c12]Dang Nguyen, Jiuhai Chen, Tianyi Zhou:
Multi-Objective Linguistic Control of Large Language Models. ACL (Findings) 2024: 4336-4347 - [c11]Jiuhai Chen, Jonas Mueller:
Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness. ACL (1) 2024: 5186-5200 - [c10]Ming Li, Jiuhai Chen, Lichang Chen, Tianyi Zhou:
Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements. ACL (Findings) 2024: 16160-16176 - [c9]Ming Li, Lichang Chen, Jiuhai Chen, Shwai He, Jiuxiang Gu, Tianyi Zhou:
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning. ACL (Findings) 2024: 16189-16211 - [c8]Lichang Chen, Chen Zhu, Jiuhai Chen, Davit Soselia, Tianyi Zhou, Tom Goldstein, Heng Huang, Mohammad Shoeybi, Bryan Catanzaro:
ODIN: Disentangled Reward Mitigates Hacking in RLHF. ICML 2024 - [c7]Lichang Chen, Jiuhai Chen, Tom Goldstein, Heng Huang, Tianyi Zhou:
InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models. ICML 2024 - [c6]Ming Li, Yong Zhang, Zhitao Li, Jiuhai Chen, Lichang Chen, Ning Cheng, Jianzong Wang, Tianyi Zhou, Jing Xiao:
From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning. NAACL-HLT 2024: 7602-7635 - [i19]Lichang Chen, Chen Zhu, Davit Soselia, Jiuhai Chen, Tianyi Zhou, Tom Goldstein, Heng Huang, Mohammad Shoeybi, Bryan Catanzaro:
ODIN: Disentangled Reward Mitigates Hacking in RLHF. CoRR abs/2402.07319 (2024) - [i18]Ming Li, Lichang Chen, Jiuhai Chen, Shwai He, Jiuxiang Gu, Tianyi Zhou:
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning. CoRR abs/2402.10110 (2024) - [i17]Ming Li, Jiuhai Chen, Lichang Chen, Tianyi Zhou:
Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements. CoRR abs/2402.10614 (2024) - [i16]Jiuhai Chen, Jonas Mueller:
Automated Data Curation for Robust Language Model Fine-Tuning. CoRR abs/2403.12776 (2024) - [i15]Xiyao Wang, Jiuhai Chen, Zhaoyang Wang, Yuhang Zhou, Yiyang Zhou, Huaxiu Yao, Tianyi Zhou, Tom Goldstein, Parminder Bhatia, Furong Huang, Cao Xiao:
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement. CoRR abs/2405.15973 (2024) - [i14]Lichang Chen, Jiuhai Chen, Chenxi Liu, John Kirchenbauer, Davit Soselia, Chen Zhu, Tom Goldstein, Tianyi Zhou, Heng Huang:
OPTune: Efficient Online Preference Tuning. CoRR abs/2406.07657 (2024) - [i13]Jiuhai Chen, Rifaa Qadri, Yuxin Wen, Neel Jain, John Kirchenbauer, Tianyi Zhou, Tom Goldstein:
GenQA: Generating Millions of Instructions from a Handful of Prompts. CoRR abs/2406.10323 (2024) - [i12]Dang Nguyen, Jiuhai Chen, Tianyi Zhou:
Multi-Objective Linguistic Control of Large Language Models. CoRR abs/2406.16229 (2024) - 2023
- [c5]Jiuhai Chen, Lichang Chen, Chen Zhu, Tianyi Zhou:
How Many Demonstrations Do You Need for In-context Learning? EMNLP (Findings) 2023: 11149-11159 - [c4]Lichang Chen, Jiuhai Chen, Heng Huang, Minhao Cheng
:
PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer. EMNLP 2023: 13512-13525 - [c3]Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein:
GOAT: A Global Transformer on Large-scale Graphs. ICML 2023: 17375-17390 - [i11]Jiuhai Chen, Lichang Chen, Chen Zhu, Tianyi Zhou:
It Takes One to Tango but More Make Trouble? The Number of Demonstrations Needed for In-Context Learning. CoRR abs/2303.08119 (2023) - [i10]Jiuhai Chen, Lichang Chen, Heng Huang, Tianyi Zhou:
When do you need Chain-of-Thought Prompting for ChatGPT? CoRR abs/2304.03262 (2023) - [i9]Lichang Chen, Jiuhai Chen, Tom Goldstein, Heng Huang, Tianyi Zhou:
InstructZero: Efficient Instruction Optimization for Black-Box Large Language Models. CoRR abs/2306.03082 (2023) - [i8]Ming Li, Yong Zhang, Zhitao Li, Jiuhai Chen, Lichang Chen, Ning Cheng, Jianzong Wang, Tianyi Zhou, Jing Xiao:
From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning. CoRR abs/2308.12032 (2023) - [i7]Jiuhai Chen, Jonas Mueller:
Quantifying Uncertainty in Answers from any Language Model via Intrinsic and Extrinsic Confidence Assessment. CoRR abs/2308.16175 (2023) - [i6]Ming Li, Lichang Chen, Jiuhai Chen, Shwai He, Heng Huang, Jiuxiang Gu, Tianyi Zhou:
Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning. CoRR abs/2310.11716 (2023) - 2022
- [c2]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf:
Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features. ICLR 2022 - [c1]Yangkun Wang, Jiarui Jin, Weinan Zhang, Yongyi Yang, Jiuhai Chen, Quan Gan, Yong Yu, Zheng Zhang, Zengfeng Huang, David Wipf:
Why Propagate Alone? Parallel Use of Labels and Features on Graphs. ICLR 2022 - [i5]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf:
A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features. CoRR abs/2206.08473 (2022) - 2021
- [j2]Yiwei Wang, Jiuhai Chen, Chun Liu, Lulu Kang:
Particle-based energetic variational inference. Stat. Comput. 31(3): 34 (2021) - [j1]Jiuhai Chen, Lulu Kang, Guang Lin:
Gaussian Process Assisted Active Learning of Physical Laws. Technometrics 63(3): 329-342 (2021) - [i4]Yangkun Wang, Jiarui Jin, Weinan Zhang, Yongyi Yang, Jiuhai Chen, Quan Gan, Yong Yu, Zheng Zhang, Zengfeng Huang, David Wipf:
Why Propagate Alone? Parallel Use of Labels and Features on Graphs. CoRR abs/2110.07190 (2021) - [i3]Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf:
Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features. CoRR abs/2110.13413 (2021) - [i2]Jiuhai Chen, Chen Zhu, Bin Dai:
Understanding the Role of Self-Supervised Learning in Out-of-Distribution Detection Task. CoRR abs/2110.13435 (2021) - 2020
- [i1]Yiwei Wang, Jiuhai Chen, Lulu Kang, Chun Liu:
Particle-based Energetic Variational Inference. CoRR abs/2004.06443 (2020)
Coauthor Index
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last updated on 2024-09-26 01:54 CEST by the dblp team
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