Namjoon Suh

According to our database, Namjoon Suh authored at least 17 papers between 2019 and 2026.

Collaborative distances:

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

2026

On Excess Risk Convergence Rates of Neural Network Classifiers.

Hyunouk Ko, Namjoon Suh, Xiaoming Huo

IEEE Trans. Inf. Theory, March, 2026

Users Mispredict Their Own Preferences for AI Writing Assistance.

Vivian Lai, Zana Buçinca, Nil-Jana Akpinar, Mo Houtti, Hyeonsu B. Kang, Kevin Chian, Namjoon Suh, Alex C. Williams

CoRR, January, 2026

2025

TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data.

Namjoon Suh, Yuning Yang, Din-Yin Hsieh, Qitong Luan, Shirong Xu, Shixiang Zhu, Guang Cheng

Trans. Mach. Learn. Res., 2025

2024

High-Dimensional Multivariate Linear Regression with Weighted Nuclear Norm Regularization.

Namjoon Suh, Li-Hsiang Lin, Xiaoming Huo

J. Comput. Graph. Stat., 2024

TimeAutoDiff: Combining Autoencoder and Diffusion model for time series tabular data synthesizing.

Namjoon Suh, Yuning Yang, Din-Yin Hsieh, Qitong Luan, Shirong Xu, Shixiang Zhu, Guang Cheng

CoRR, 2024

Discriminative Estimation of Total Variation Distance: A Fidelity Auditor for Generative Data.

Lan Tao, Shirong Xu, Chi-Hua Wang, Namjoon Suh, Guang Cheng

CoRR, 2024

Approximation of RKHS Functionals by Neural Networks.

Tian-Yi Zhou, Namjoon Suh, Guang Cheng, Xiaoming Huo

CoRR, 2024

Benefits of Transformer: In-Context Learning in Linear Regression Tasks with Unstructured Data.

Yue Xing, Xiaofeng Lin, Namjoon Suh, Qifan Song, Guang Cheng

CoRR, 2024

A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models.

Namjoon Suh, Guang Cheng

CoRR, 2024

2023

AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing.

Namjoon Suh, Xiaofeng Lin, Din-Yin Hsieh, Merhdad Honarkhah, Guang Cheng

CoRR, 2023

Approximation and non-parametric estimation of functions over high-dimensional spheres via deep ReLU networks.

Namjoon Suh, Tian-Yi Zhou, Xiaoming Huo

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Asymptotic Theory of \(\boldsymbol \ell _1\) -Regularized PDE Identification from a Single Noisy Trajectory.

Yuchen He, Namjoon Suh, Xiaoming Huo, Sung Ha Kang, Yajun Mei

SIAM/ASA J. Uncertain. Quantification, March, 2022

A Non-Parametric Regression Viewpoint : Generalization of Overparametrized Deep RELU Network Under Noisy Observations.

Namjoon Suh, Hyunouk Ko, Xiaoming Huo

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

A network model that combines latent factors and sparse graphs.

Namjoon Suh, Xiaoming Huo, Eric Heim, Lee M. Seversky

Stat. Anal. Data Min., 2021

Asymptotic Theory of 𝓁₁-Regularized PDE Identification from a Single Noisy Trajectory.

Yuchen He, Namjoon Suh, Xiaoming Huo, Sung Ha Kang, Yajun Mei

CoRR, 2021

2019

Factor Analysis on Citation, Using a Combined Latent and Logistic Regression Model.

Namjoon Suh, Xiaoming Huo, Eric Heim, Lee M. Seversky

CoRR, 2019

Adaptive Online Monitoring of the Ising model.

Namjoon Suh, Ruizhi Zhang, Yajun Mei

Proceedings of the 57th Annual Allerton Conference on Communication, 2019