28 citations · 42 across the 9 of their papers we have counts for
9 papers
Deep State-Space Generative Model For Correlated Time-to-Event Predictions
Yuan Xue, Denny Zhou, Nan Du +4
Capturing the inter-dependencies among multiple types of clinically-critical events is critical not only to accurate future event prediction, but also to better treatment planning.…
Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction
Yuan Xue, Nan Du, Anne Mottram +2
We propose to meta-learn an a self-supervised patient trajectory forecast learning rule by meta-training on a meta-objective that directly optimizes the utility of the patient repr…
Learning to Optimize with Stochastic Dominance Constraints
Hanjun Dai, Yuan Xue, Niao He +4
In real-world decision-making, uncertainty is important yet difficult to handle. Stochastic dominance provides a theoretically sound approach for comparing uncertain quantities, bu…
Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach
Shuzhao Xie, Yuan Xue, Yifei Zhu +1
With the advancement of deep learning techniques, major cloud providers and niche machine learning service providers start to offer their cloud-based machine learning tools, also k…
Exploring experimental conditions to reduce uncertainties in the optical potential
M. Catacora-Rios, G. B. King, A. E. Lovell +1
Background: Uncertainty quantification for nuclear theories has gained a more prominent role in the field, with more and more groups attempting to understand the uncertainties on t…
Learning Unstable Dynamical Systems with Time-Weighted Logarithmic Loss
Kamil Nar, Yuan Xue, Andrew M. Dai
When training the parameters of a linear dynamical model, the gradient descent algorithm is likely to fail to converge if the squared-error loss is used as the training loss functi…
Deep Physiological State Space Model for Clinical Forecasting
Yuan Xue, Denny Zhou, Nan Du +4
Clinical forecasting based on electronic medical records (EMR) can uncover the temporal correlations between patients' conditions and outcomes from sequences of longitudinal clinic…
Modelling EHR timeseries by restricting feature interaction
Kun Zhang, Yuan Xue, Gerardo Flores +3
Time series data are prevalent in electronic health records, mostly in the form of physiological parameters such as vital signs and lab tests. The patterns of these values may be s…
Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer
Edward Choi, Zhen Xu, Yujia Li +4
Effective modeling of electronic health records (EHR) is rapidly becoming an important topic in both academia and industry. A recent study showed that using the graphical structure…