no code implementations • 4 Jun 2024 • Seongyoon Kim, Minchan Jeong, Sungnyun Kim, Sungwoo Cho, Sumyeong Ahn, Se-Young Yun
Federated Learning (FL) has emerged as a pivotal framework for the development of effective global models (global FL) or personalized models (personalized FL) across clients with heterogeneous, non-iid data distribution.
1 code implementation • 13 Feb 2024 • Haeju Lee, Minchan Jeong, Se-Young Yun, Kee-Eung Kim
We argue that when we extract knowledge from source tasks via training source prompts, we need to consider this correlation among source tasks for better transfer to target tasks.
no code implementations • 24 Aug 2023 • Gihun Lee, Minchan Jeong, Sangmook Kim, Jaehoon Oh, Se-Young Yun
FedSOL is designed to identify gradients of local objectives that are inherently orthogonal to directions affecting the proximal objective.
no code implementations • 3 Mar 2023 • Jihwan Oh, Joonkee Kim, Minchan Jeong, Se-Young Yun
In this paper, we present a risk-based exploration that leads to collaboratively optimistic behavior by shifting the sampling region of distribution.
Distributional Reinforcement Learning Multi-agent Reinforcement Learning +2
1 code implementation • 3 Feb 2023 • Jongwoo Ko, Seungjoon Park, Minchan Jeong, Sukjin Hong, Euijai Ahn, Du-Seong Chang, Se-Young Yun
Knowledge distillation (KD) is a highly promising method for mitigating the computational problems of pre-trained language models (PLMs).
2 code implementations • 6 Jun 2021 • Gihun Lee, Minchan Jeong, Yongjin Shin, Sangmin Bae, Se-Young Yun
In federated learning, a strong global model is collaboratively learned by aggregating clients' locally trained models.
1 code implementation • 8 Feb 2019 • Daniel Bienstock, Minchan Jeong, Apurv Shukla, Se-Young Yun
We consider streaming principal component analysis when the stochastic data-generating model is subject to perturbations.
1 code implementation • 3 Mar 2017 • Jung-hun Kim, Se-Young Yun, Minchan Jeong, Jun Hyun Nam, Jinwoo Shin, Richard Combes
This implies that classical approaches cannot guarantee a non-trivial regret bound.