376 citations · 598 across the 19 of their papers we have counts for
20 papers
Learning When to Think: Utility-Guided Adaptive Reasoning for Efficient Text Retrieval
Yingtao Luo, Kangyi Zhao, Shiyi Du +1
Reasoning-conditioned embeddings can improve retrieval by progressively refining semantic constraints. However, additional reasoning may introduce over-generalized or semantically…
Benchmarking Waitlist Mortality Prediction in Heart Transplantation Through Time-to-Event Modeling using New Longitudinal UNOS Dataset
Yingtao Luo, Reza Skandari, Carlos Martinez +2
Decisions about managing patients on the heart transplant waitlist are currently made by committees of doctors who consider multiple factors, but the process remains largely ad-hoc…
Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting
Yingtao Luo, Shikai Fang, Binqing Wu +2
Weather forecasting is essential but remains computationally intensive and physically incomplete in traditional numerical weather prediction (NWP) methods. Deep learning (DL) model…
Fairness without Demographics through Learning Graph of Gradients
Yingtao Luo, Zhixun Li, Qiang Liu +1
Machine learning systems are notoriously prone to biased predictions about certain demographic groups, leading to algorithmic fairness issues. Due to privacy concerns and data qual…
Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Training
Yunshu Wu, Yingtao Luo, Xianghao Kong +2
Diffusion models learn to denoise data and the trained denoiser is then used to generate new samples from the data distribution. In this paper, we revisit the diffusion sampling pr…
GSLB: The Graph Structure Learning Benchmark
Zhixun Li, Liang Wang, Xin Sun +8
Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation g…
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning
Jannik Deuschel, Caleb N. Ellington, Yingtao Luo +3
Interpretable policy learning seeks to estimate intelligible decision policies from observed actions; however, existing models force a tradeoff between accuracy and interpretabilit…
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition
Shikai Fang, Qingsong Wen, Yingtao Luo +2
In real-world scenarios like traffic and energy, massive time-series data with missing values and noises are widely observed, even sampled irregularly. While many imputation method…
Learning Differential Operators for Interpretable Time Series Modeling
Yingtao Luo, Chang Xu, Yang Liu +3
Modeling sequential patterns from data is at the core of various time series forecasting tasks. Deep learning models have greatly outperformed many traditional models, but these bl…
Deep Stable Representation Learning on Electronic Health Records
Yingtao Luo, Zhaocheng Liu, Qiang Liu
Deep learning models have achieved promising disease prediction performance of the Electronic Health Records (EHR) of patients. However, most models developed under the I.I.D. hypo…
Improving Multi-Interest Network with Stable Learning
Zhaocheng Liu, Yingtao Luo, Di Zeng +4
Modeling users' dynamic preferences from historical behaviors lies at the core of modern recommender systems. Due to the diverse nature of user interests, recent advances propose t…
RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring
Qiang Liu, Yingtao Luo, Shu Wu +4
In financial credit scoring, loan applications may be approved or rejected. We can only observe default/non-default labels for approved samples but have no observations for rejecte…
AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation
Juyong Jiang, Peiyan Zhang, Yingtao Luo +6
Sequential recommendation (SR) aims to model users dynamic preferences from a series of interactions. A pivotal challenge in user modeling for SR lies in the inherent variability o…
Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation with Pre-training
Juyong Jiang, Peiyan Zhang, Yingtao Luo +6
Sequential recommendation systems are integral to discerning temporal user preferences. Yet, the task of learning from abbreviated user interaction sequences poses a notable challe…
Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)
Yuntian Chen, Yingtao Luo, Qiang Liu +2
Partial differential equations (PDEs) are concise and understandable representations of domain knowledge, which are essential for deepening our understanding of physical processes…
Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations
Yingtao Luo, Qiang Liu, Yuntian Chen +3
Partial differential equations (PDEs) that fit scientific data can represent physical laws with explainable mechanisms for various mathematically-oriented subjects, such as physics…
STAN: Spatio-Temporal Attention Network for Next Location Recommendation
Yingtao Luo, Qiang Liu, Zhaocheng Liu
The next location recommendation is at the core of various location-based applications. Current state-of-the-art models have attempted to solve spatial sparsity with hierarchical g…
Deep Spatial Learning with Molecular Vibration
Ziyang Zhang, Yingtao Luo
Machine learning over-fitting caused by data scarcity greatly limits the application of machine learning for molecules. Due to manufacturing processes difference, big data is not a…
Probability-Density-Based Deep Learning Paradigm for the Fuzzy Design of Functional Metastructures
Ying-Tao Luo, Peng-Qi Li, Dong-Ting Li +7
In quantum mechanics, a norm squared wave function can be interpreted as the probability density that describes the likelihood of a particle to be measured in a given position or m…
A Quantum-Inspired Probabilistic Model for the Inverse Design of Meta-Structures
Yingtao Luo, Xuefeng Zhu
In quantum mechanics, a norm squared wave function can be interpreted as the probability density that describes the likelihood of a particle to be measured in a given position or m…