Zobrazeno 1 - 10
of 328
pro vyhledávání: '"Lee, Junghwan"'
Observational data have been actively used to estimate treatment effect, driven by the growing availability of electronic health records (EHRs). However, EHRs typically consist of longitudinal records, often introducing time-dependent confoundings th
Externí odkaz:
http://arxiv.org/abs/2406.08851
We present a conformal prediction method for time series using the Transformer architecture to capture long-memory and long-range dependencies. Specifically, we use the Transformer decoder as a conditional quantile estimator to predict the quantiles
Externí odkaz:
http://arxiv.org/abs/2406.05332
Autor:
Lee, Junghwan
In this work, we explored federated learning in temporal heterogeneity across clients. We observed that global model obtained by \texttt{FedAvg} trained with fixed-length sequences shows faster convergence than varying-length sequences. We proposed m
Externí odkaz:
http://arxiv.org/abs/2309.09381
Tailoring treatment for individual patients is crucial yet challenging in order to achieve optimal healthcare outcomes. Recent advances in reinforcement learning offer promising personalized treatment recommendations; however, they rely solely on cur
Externí odkaz:
http://arxiv.org/abs/2307.01519
Change-point detection, detecting an abrupt change in the data distribution from sequential data, is a fundamental problem in statistics and machine learning. CUSUM is a popular statistical method for online change-point detection due to its efficien
Externí odkaz:
http://arxiv.org/abs/2210.17312
Publikováno v:
In Chemical Engineering Journal 15 August 2024 494
Autor:
Lee, Hyochan, Song, Young-Woong, Kim, Min-Young, Lee, JungHwan, Ryu, JiEun, Noh, YooJung, Kim, Su-Jin, Kim, Jaekook, Lim, Jinsub
Publikováno v:
In Solid State Ionics August 2024 411
Publikováno v:
In Applied Thermal Engineering 1 October 2024 254
Autor:
Lee, Junghwan, Kim, Dongwook
Publikováno v:
In Social Sciences & Humanities Open 2024 9
Publikováno v:
In Energy and AI January 2024 15