activity
20202026
most citedLearned practical guidelines for evaluating Conditional Entropy and Mutual Information in discovering major factors of response-vs-covariate dynamics

7 citations · 10 across the 5 of their papers we have counts for

collaborators

6 papers

stat.ME2026

Structure-Preserving Visualization of Complex Systems through Discrete Approximation: An Application to Argo Data

Shang-Ying Shiu, Fushing Hsieh, Ting-Li Chen

This paper presents a framework for constructing structure-preserving representations of complex systems through discrete approximation, and demonstrates its use in studying the ve…

stat.AP2026

Simultaneous Estimation of Ballpark Effects and Team Defense Using Total Bases Residuals

Jhe-Jia Wu, Tian-Li Yan, Ting-Li Chen

Estimating ballpark effects and team defense in baseball is challenging because batted-ball outcomes are influenced by multiple factors, including contact quality, ballpark environ…

stat.AP2022

Unraveling implicit human behavioral effects on dynamic characteristics of Covid-19 daily infection rates in Taiwan

Ting-Li Chen, Elizabeth P. Chou, Min-Yi Chen +1

We study Covid-19 spreading dynamics underlying 84 curves of daily Covid-19 infection rates pertaining to 84 districts belonging to the largest seven cities in Taiwan during her pr…

stat.ME2022★ 7 cited

Learned practical guidelines for evaluating Conditional Entropy and Mutual Information in discovering major factors of response-vs-covariate dynamics

Ting-Li Chen, Hsieh Fushing, Elizabeth P. Chou

We reformulate and reframe a series of increasingly complex parametric statistical topics into a framework of response-vs-covariate (Re-Co) dynamics that is described without any e…

stat.ME2022★ 1 cited

Multiscale major factor selections for complex system data with structural dependency and heterogeneity

Hsieh Fushing, Elizabeth Chou, Ting-Li Chen

Based on structured data derived from large complex systems, we computationally further develop and refine a major factor selection protocol by accommodating structural dependency…

cs.LG2020★ 2 cited

Learning to Embed Categorical Features without Embedding Tables for Recommendation

Wang-Cheng Kang, Derek Zhiyuan Cheng, Tiansheng Yao +4

Embedding learning of categorical features (e.g. user/item IDs) is at the core of various recommendation models including matrix factorization and neural collaborative filtering. T…