no code implementations • 11 Oct 2023 • Jannik Deuschel, Caleb N. Ellington, Benjamin J. Lengerich, Yingtao Luo, Pascal Friederich, Eric P. Xing
Interpretable policy learning seeks to estimate intelligible decision policies from observed actions; however, existing models fall short by forcing a tradeoff between accuracy and interpretability.
no code implementations • 28 Aug 2023 • Shikai Fang, Qingsong Wen, Yingtao Luo, Shandian Zhe, Liang Sun
More importantly, almost all methods assume the observations are sampled at regular time stamps, and fail to handle complex irregular sampled time series arising from different applications.
no code implementations • 3 Sep 2022 • Yingtao Luo, Chang Xu, Yang Liu, Weiqing Liu, Shun Zheng, Jiang Bian
In this work, we propose an learning framework that can automatically obtain interpretable PDE models from sequential data.
1 code implementation • 3 Sep 2022 • Yingtao Luo, Zhaocheng Liu, Qiang Liu
The unstable correlation between procedures and diagnoses existed in the training distribution can cause spurious correlation between historical EHR and future diagnosis.
no code implementations • 14 Jul 2022 • Zhaocheng Liu, Yingtao Luo, Di Zeng, Qiang Liu, Daqing Chang, Dongying Kong, Zhi Chen
Modeling users' dynamic preferences from historical behaviors lies at the core of modern recommender systems.
no code implementations • 1 Jun 2022 • Qiang Liu, Yingtao Luo, Shu Wu, Zhen Zhang, Xiangnan Yue, Hong Jin, Liang Wang
Accordingly, we for the first time propose to model the biased credit scoring data with Multi-Task Learning (MTL).
1 code implementation • 18 May 2022 • Juyong Jiang, Peiyan Zhang, Yingtao Luo, Chaozhuo Li, Jae Boum Kim, Kai Zhang, Senzhang Wang, Xing Xie, Sunghun Kim
Sequential recommendation (SR) aims to model users dynamic preferences from a series of interactions.
1 code implementation • 13 Dec 2021 • Juyong Jiang, Peiyan Zhang, Yingtao Luo, Chaozhuo Li, Jaeboum Kim, Kai Zhang, Senzhang Wang, Sunghun Kim
Our approach leverages bidirectional temporal augmentation and knowledge-enhanced fine-tuning to synthesize authentic pseudo-prior items that \emph{retain user preferences and capture deeper item semantic correlations}, thus boosting the model's expressive power.
2 code implementations • 9 Jun 2021 • Yuntian Chen, Yingtao Luo, Qiang Liu, Hao Xu, Dongxiao Zhang
Partial differential equations (PDEs) are concise and understandable representations of domain knowledge, which are essential for deepening our understanding of physical processes and predicting future responses.
no code implementations • 2 Jun 2021 • Yingtao Luo, Qiang Liu, Yuntian Chen, WenBo Hu, Tian Tian, Jun Zhu
Especially, the discovery of PDEs with highly nonlinear coefficients from low-quality data remains largely under-addressed.
2 code implementations • 8 Feb 2021 • Yingtao Luo, Qiang Liu, Zhaocheng Liu
The next location recommendation is at the core of various location-based applications.
Ranked #1 on point of interests on Gowalla
1 code implementation • 14 Nov 2020 • Ziyang Zhang, Yingtao Luo
Machine learning over-fitting caused by data scarcity greatly limits the application of machine learning for molecules.
1 code implementation • 11 Nov 2020 • Yingtao Luo, XueFeng Zhu
Here, inspired by quantum theory, we propose a probabilistic deep learning paradigm for the inverse design of functional meta-structures.