Search Results for author: Yingtao Luo

Found 13 papers, 7 papers with code

Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning

no code implementations11 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.

Imitation Learning Multi-Task Learning

BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition

no code implementations28 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.

Computational Efficiency Gaussian Processes +3

Learning Differential Operators for Interpretable Time Series Modeling

no code implementations3 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.

Decision Making Meta-Learning +2

Deep Stable Representation Learning on Electronic Health Records

1 code implementation3 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.

Disease Prediction Representation Learning

Improving Multi-Interest Network with Stable Learning

no code implementations14 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.

Recommendation Systems

Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation with Pre-training

1 code implementation13 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.

Data Augmentation Self-Knowledge Distillation +1

Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)

2 code implementations9 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.

Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations

no code implementations2 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.

Density Estimation Model Optimization

Deep Spatial Learning with Molecular Vibration

1 code implementation14 Nov 2020 Ziyang Zhang, Yingtao Luo

Machine learning over-fitting caused by data scarcity greatly limits the application of machine learning for molecules.

BIG-bench Machine Learning

A Quantum-Inspired Probabilistic Model for the Inverse Design of Meta-Structures

1 code implementation11 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.

Position Probabilistic Deep Learning

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