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2020 – today
- 2024
[c2]Xinyu Yang, Jixuan Leng, Geyang Guo, Jiawei Zhao, Ryumei Nakada, Linjun Zhang, Huaxiu Yao, Beidi Chen:
S2FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity. NeurIPS 2024- [i10]Tianxi Cai, Feiqing Huang, Ryumei Nakada, Linjun Zhang, Doudou Zhou:
Contrastive Learning on Multimodal Analysis of Electronic Health Records. CoRR abs/2403.14926 (2024) - [i9]Zhe Zhang, Ryumei Nakada, Linjun Zhang:
Differentially Private Federated Learning: Servers Trustworthiness, Estimation, and Statistical Inference. CoRR abs/2404.16287 (2024) - [i8]Ryumei Nakada, Yichen Xu, Lexin Li, Linjun Zhang:
Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance. CoRR abs/2406.03628 (2024) - [i7]Yibo Zhong, Haoxiang Jiang, Lincan Li, Ryumei Nakada, Tianci Liu, Linjun Zhang, Huaxiu Yao, Haoyu Wang:
NEAT: Nonlinear Parameter-efficient Adaptation of Pre-trained Models. CoRR abs/2410.01870 (2024) - [i6]Xinyu Yang, Jixuan Leng, Geyang Guo, Jiawei Zhao, Ryumei Nakada, Linjun Zhang, Huaxiu Yao, Beidi Chen:
S2FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity. CoRR abs/2412.06289 (2024) - 2023
- [j3]Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, Linjun Zhang:
The Power of Contrast for Feature Learning: A Theoretical Analysis. J. Mach. Learn. Res. 24: 330:1-330:78 (2023) - [c1]Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng, Wenlong Ji, James Zou, Linjun Zhang:
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data. AISTATS 2023: 4348-4380 - [i5]Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng, Wenlong Ji, James Zou, Linjun Zhang:
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data. CoRR abs/2302.06232 (2023) - [i4]Alyssa Huang, Peihan Liu, Ryumei Nakada, Linjun Zhang, Wanrong Zhang:
Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training. CoRR abs/2306.08173 (2023) - 2021
- [j2]Ryumei Nakada, Tatsuya Kubokawa, Malay Ghosh, Sayar Karmakar
:
Shrinkage estimation with singular priors and an application to small area estimation. J. Multivar. Anal. 183: 104726 (2021) - [i3]Ryumei Nakada, Masaaki Imaizumi:
Asymptotic Risk of Overparameterized Likelihood Models: Double Descent Theory for Deep Neural Networks. CoRR abs/2103.00500 (2021) - [i2]Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, Linjun Zhang:
The Power of Contrast for Feature Learning: A Theoretical Analysis. CoRR abs/2110.02473 (2021) - 2020
- [j1]Ryumei Nakada, Masaaki Imaizumi:
Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality. J. Mach. Learn. Res. 21: 174:1-174:38 (2020)
2010 – 2019
- 2019
- [i1]Ryumei Nakada, Masaaki Imaizumi:
Adaptive Approximation and Estimation of Deep Neural Network to Intrinsic Dimensionality. CoRR abs/1907.02177 (2019)
Coauthor Index
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