Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

Consider the problem of imputing missing values in a dataset. One the one hand,
conventional approaches using iterative imputation benefit from the simplicity and …

Generative, pre-trained transformers (GPTs, a type of" Foundation Models") have reshaped
natural language processing (NLP) through their versatility in diverse downstream tasks …

Electronic health records (EHRs) have been successfully used in data science and machine
learning projects. However, most of these data are collected for clinical use rather than for …

How to learn a good predictor on data with missing values? Most efforts focus on first
imputing as well as possible and second learning on the completed data to predict the …

An increasing amount of research is being devoted to applying machine learning methods to
electronic health record (EHR) data for various clinical purposes. This growing area of …

In digital medicine, patient data typically record health events over time (eg, through
electronic health records, wearables, or other sensing technologies) and thus form unique …

Machine learning models often fail to generalize well under distributional shifts.
Understanding and overcoming these failures have led to a research field of Out-of …

Energy forecasting is crucial in scheduling and planning future electric load, so as to
improve the reliability and safeness of the power grid. Despite recent developments of …

Synthesizing time series data is pivotal in modern society, aiding effective decision making
and ensuring privacy preservation in various scenarios. Time series are associated with …