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Jiuhai 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
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
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
Particle-based Energetic Variational Inference. CoRR abs/2004.06443 (2020)
Coauthor Index
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last updated on 2024-09-26 00:59 CEST by the dblp team
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