Search Results for author: Joyce Ho

Found 8 papers, 4 papers with code

A Survey on Knowledge Graphs for Healthcare: Resources, Applications, and Promises

no code implementations7 Jun 2023 Hejie Cui, Jiaying Lu, Shiyu Wang, ran Xu, Wenjing Ma, Shaojun Yu, Yue Yu, Xuan Kan, Chen Ling, Liang Zhao, Joyce Ho, Fei Wang, Carl Yang

Healthcare knowledge graphs (HKGs) have emerged as a promising tool for organizing medical knowledge in a structured and interpretable way, which provides a comprehensive view of medical concepts and their relationships.

Knowledge Graphs

Neighborhood-Regularized Self-Training for Learning with Few Labels

1 code implementation10 Jan 2023 ran Xu, Yue Yu, Hejie Cui, Xuan Kan, Yanqiao Zhu, Joyce Ho, Chao Zhang, Carl Yang

Our further analysis demonstrates that our proposed data selection strategy reduces the noise of pseudo labels by 36. 8% and saves 57. 3% of the time when compared with the best baseline.


no code implementations ICLR 2022 Huan He, Shifan Zhao, Yuanzhe Xi, Joyce Ho, Yousef Saad

We also empirically show that GDA-AM solves a variety of minimax problems and improves GAN training on several datasets

You Sound Like Someone Who Watches Drama Movies: Towards Predicting Movie Preferences from Conversational Interactions

1 code implementation NAACL 2021 Sergey Volokhin, Joyce Ho, Oleg Rokhlenko, Eugene Agichtein

We call our proposed method ConvExtr (Conversational Collaborative Filtering using External Data), which 1) infers a user{'}s sentiment towards an entity from the conversation context, and 2) transforms the ratings of {``}similar{''} external reviewers to predict the current user{'}s preferences.

Collaborative Filtering Domain Adaptation +1

COPA: Constrained PARAFAC2 for Sparse & Large Datasets

1 code implementation12 Mar 2018 Ardavan Afshar, Ioakeim Perros, Evangelos E. Papalexakis, Elizabeth Searles, Joyce Ho, Jimeng Sun

To tackle these challenges, we propose a {\it CO}nstrained {\it PA}RAFAC2 (COPA) method, which carefully incorporates optimization constraints such as temporal smoothness, sparsity, and non-negativity in the resulting factors.

ACDC: $α$-Carving Decision Chain for Risk Stratification

no code implementations16 Jun 2016 Yubin Park, Joyce Ho, Joydeep Ghosh

The resulting chain of decision rules yields a pure subset of the minority class examples.

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