| Trimming the Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning J Yun, P Zheng, E Yang, A Lozano, A Aravkin International Conference on Machine Learning, 7242-7251, 2019 | 23 | 2019 |
| Adaptive proximal gradient methods for structured neural networks J Yun, AC Lozano, E Yang Advances in Neural Information Processing Systems 34, 24365-24378, 2021 | 16 | 2021 |
| A general family of stochastic proximal gradient methods for deep learning J Yun, AC Lozano, E Yang arXiv preprint arXiv:2007.07484, 2020 | 14 | 2020 |
| Cluster-promoting quantization with bit-drop for minimizing network quantization loss JH Lee, J Yun, SJ Hwang, E Yang Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 11 | 2021 |
| Adablock: SGD with practical block diagonal matrix adaptation for deep learning J Yun, A Lozano, E Yang International Conference on Artificial Intelligence and Statistics, 2574-2606, 2022 | 3 | 2022 |
| Stochastic gradient methods with block diagonal matrix adaptation J Yun, AC Lozano, E Yang arXiv preprint arXiv:1905.10757, 2019 | 3 | 2019 |
| M-estimation with the trimmed l1 penalty J Yun, P Zheng, E Yang, A Lozano, A Aravkin arXiv preprint arXiv:1805.07495, 2018 | 3 | 2018 |
| Riemannian SAM: Sharpness-Aware Minimization on Riemannian Manifolds J Yun, E Yang Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| TEDDY: Trimming Edges with Degree-based Discrimination strategY H Seo, J Yun, E Yang arXiv preprint arXiv:2402.01261, 2024 | | 2024 |
| TEDDY: Trimming Edges with Degree-based Graph Diffusion Strategy H Seo, J Yun, E Yang The Twelfth International Conference on Learning Representations, 2023 | | 2023 |
| GradientMix: A Simple yet Effective Regularization for Large Batch Training J Yun, JH Lee, E Yang | | 2022 |
Eunho YangKAISTVerified email at kaist.ac.kr
