Adaptive Approximation and Estimation of Deep Neural Network to Intrinsic Dimensionality. - Ryumei Nakada; Masaaki Imaizumi (CoRR, 2019)

Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality. - Ryumei Nakada; Masaaki Imaizumi (J. Mach. Learn. Res., 2020)

Asymptotic Risk of Overparameterized Likelihood Models: Double Descent Theory for Deep Neural Networks. - Ryumei Nakada; Masaaki Imaizumi (CoRR, 2021)

Shrinkage estimation with singular priors and an application to small area estimation. - Ryumei Nakada; Tatsuya Kubokawa; Malay Ghosh; Sayar Karmakar (J. Multivar. Anal., 2021)

Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data. - Ryumei Nakada; Halil Ibrahim Gulluk; Zhun Deng; Wenlong Ji; James Zou 001; Linjun Zhang (AISTATS, 2023)

Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training. - Alyssa Huang; Peihan Liu; Ryumei Nakada; Linjun Zhang; Wanrong Zhang 001 (CoRR, 2023)

The Power of Contrast for Feature Learning: A Theoretical Analysis. - Wenlong Ji; Zhun Deng; Ryumei Nakada; James Zou 001; Linjun Zhang (J. Mach. Learn. Res., 2023)

S2FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity. - Xinyu Yang; Jixuan Leng; Geyang Guo; Jiawei Zhao; Ryumei Nakada; Linjun Zhang; Huaxiu Yao; Beidi Chen (NeurIPS, 2024)

Contrastive Learning on Multimodal Analysis of Electronic Health Records. - Tianxi Cai; Feiqing Huang; Ryumei Nakada; Linjun Zhang; Doudou Zhou (CoRR, 2024)

Differentially Private Federated Learning: Servers Trustworthiness, Estimation, and Statistical Inference. - Zhe Zhang; Ryumei Nakada; Linjun Zhang (CoRR, 2024)

Synthetic Oversampling: Theory and A Practical Approach Using LLMs to Address Data Imbalance. - Ryumei Nakada; Yichen Xu; Lexin Li; Linjun Zhang (CoRR, 2024)

NEAT: Nonlinear Parameter-efficient Adaptation of Pre-trained Models. - Yibo Zhong; Haoxiang Jiang; Lincan Li; Ryumei Nakada; Tianci Liu 003; Linjun Zhang; Huaxiu Yao; Haoyu Wang 004 (CoRR, 2024)

A Theoretical Framework for Prompt Engineering: Approximating Smooth Functions with Transformer Prompts. - Ryumei Nakada; Wenlong Ji; Tianxi Cai; James Zou; Linjun Zhang (CoRR, 2025)

Residual Feature Integration is Sufficient to Prevent Negative Transfer. - Yichen Xu; Ryumei Nakada; Linjun Zhang; Lexin Li (CoRR, 2025)