Trimming the $ ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning. - Jihun Yun; Peng Zheng 002; Eunho Yang; Aurélie C. Lozano; Aleksandr Y. Aravkin (ICML, 2019)

Stochastic Gradient Methods with Block Diagonal Matrix Adaptation. - Jihun Yun; Aurélie C. Lozano; Eunho Yang (CoRR, 2019)

A General Family of Stochastic Proximal Gradient Methods for Deep Learning. - Jihun Yun; Aurélie C. Lozano; Eunho Yang (CoRR, 2020)

Cluster-Promoting Quantization with Bit-Drop for Minimizing Network Quantization Loss. - Jung Hyun Lee; Jihun Yun; Sung Ju Hwang; Eunho Yang (ICCV, 2021)

Adaptive Proximal Gradient Methods for Structured Neural Networks. - Jihun Yun; Aurélie C. Lozano; Eunho Yang (NeurIPS, 2021)

AdaBlock: SGD with Practical Block Diagonal Matrix Adaptation for Deep Learning. - Jihun Yun; Aurélie C. Lozano; Eunho Yang (AISTATS, 2022)

Riemannian SAM: Sharpness-Aware Minimization on Riemannian Manifolds. - Jihun Yun; Eunho Yang (NeurIPS, 2023)

TEDDY: Trimming Edges with Degree-based Discrimination Strategy. - Hyunjin Seo; Jihun Yun; Eunho Yang (ICLR, 2024)

LANTERN: Accelerating Visual Autoregressive Models with Relaxed Speculative Decoding. - Doohyuk Jang; Sihwan Park 001; June Yong Yang; Yeonsung Jung; Jihun Yun; Souvik Kundu 009; Sungyub Kim; Eunho Yang (ICLR, 2025)

Unraveling Zeroth-Order Optimization through the Lens of Low-Dimensional Structured Perturbations. - Sihwan Park 001; Jihun Yun; Sungyub Kim; Souvik Kundu 009; Eunho Yang (CoRR, 2025)

Alignment as Distribution Learning: Your Preference Model is Explicitly a Language Model. - Jihun Yun; Juno Kim; Jongho Park; Junhyuck Kim; Jongha Jon Ryu; Jaewoong Cho; Kwang-Sung Jun (CoRR, 2025)