1 code implementation • 18 Jan 2024 • Seong Jin Cho, Gwangsu Kim, Junghyun Lee, Jinwoo Shin, Chang D. Yoo
Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data.
no code implementations • 25 Nov 2023 • Prin Phunyaphibarn, Junghyun Lee, Bohan Wang, Huishuai Zhang, Chulhee Yun
Although gradient descent with Polyak's momentum is widely used in modern machine and deep learning, a concrete understanding of its effects on the training trajectory remains elusive.
2 code implementations • 28 Oct 2023 • Junghyun Lee, Se-Young Yun, Kwang-Sung Jun
Logistic bandit is a ubiquitous framework of modeling users' choices, e. g., click vs. no click for advertisement recommender system.
1 code implementation • NeurIPS 2023 • Junghyun Lee, Hanseul Cho, Se-Young Yun, Chulhee Yun
Fair Principal Component Analysis (PCA) is a problem setting where we aim to perform PCA while making the resulting representation fair in that the projected distributions, conditional on the sensitive attributes, match one another.
no code implementations • 16 Oct 2023 • Junghyun Lee, Eunsang Lee, Young-Sik Kim, Yongwoo Lee, Joon-Woo Lee, Yongjune Kim, Jong-Seon No
This study proposes an optimized layerwise approximation (OLA), a systematic framework that optimizes both accuracy loss and time consumption by using different approximation polynomials for each layer in the PTA scenario.
1 code implementation • 9 Mar 2023 • Junghyun Lee, Laura Schmid, Se-Young Yun
Then, to mitigate the issue of high communication costs incurred by flooding in complex networks, we propose a new protocol called Flooding with Absorption (FwA).
1 code implementation • 17 Aug 2022 • Yassir Jedra, Junghyun Lee, Alexandre Proutière, Se-Young Yun
We investigate the problems of model estimation and reward-free learning in episodic Block MDPs.
2 code implementations • 23 Sep 2021 • Junghyun Lee, Gwangsu Kim, Matt Olfat, Mark Hasegawa-Johnson, Chang D. Yoo
This paper defines fair principal component analysis (PCA) as minimizing the maximum mean discrepancy (MMD) between dimensionality-reduced conditional distributions of different protected classes.
no code implementations • 14 Jun 2021 • Joon-Woo Lee, HyungChul Kang, Yongwoo Lee, Woosuk Choi, Jieun Eom, Maxim Deryabin, Eunsang Lee, Junghyun Lee, Donghoon Yoo, Young-Sik Kim, Jong-Seon No
Previous PPML schemes replace non-arithmetic activation functions with simple arithmetic functions instead of adopting approximation methods and do not use bootstrapping, which enables continuous homomorphic evaluations.
no code implementations • 7 Jul 2020 • Junghyun Lee, Jawook Gu, Jong Chul Ye
Metal artifact reduction (MAR) is one of the most important research topics in computed tomography (CT).