The COVID-19 pandemic emphasized the importance of epidemic forecasting for decision
makers in multiple domains, ranging from public health to the economy. Forecasting …

Decision-focused learning (DFL) was recently proposed for stochastic optimization problems
that involve unknown parameters. By integrating predictive modeling with an implicitly …

The COVID-19 pandemic has brought forth the importance of epidemic forecasting for
decision makers in multiple domains, ranging from public health to the economy as a whole …

We introduce EINNs, a framework crafted for epidemic forecasting that builds upon the
theoretical grounds provided by mechanistic models as well as the data-driven expressibility …

Lifelong Learning (LL) refers to the ability to continually learn and solve new problems with
incremental available information over time while retaining previous knowledge. Much …

Large molecular representation models pre-trained on massive unlabeled data have shown
great success in predicting molecular properties. However, these models may tend to overfit …

Deep neural networks (DNNs) have achieved enormous success in a wide range of
domains, such as computer vision, natural language processing and scientific areas …

Machine learning models are widely used to solve real-world problems in science and
industry. To build robust models, we should quantify the uncertainty of the model's …

Probabilistic time-series forecasting enables reliable decision making across many
domains. Most forecasting problems have diverse sources of data containing multiple …

The uncertain infection transmission causes challenges in accurate disease prediction.
Numerous methods have been proposed to capture the temporal pictures from past …