Neural networks have achieved remarkable performance across various problem domains,
but their widespread applicability is hindered by inherent limitations such as overconfidence …

In the current landscape of deep learning research, there is a predominant emphasis on
achieving high predictive accuracy in supervised tasks involving large image and language …

Bayesian deep learning (BDL) is a promising approach to achieve well-calibrated
predictions on distribution-shifted data. Nevertheless, there exists no large-scale survey that …

Bayesian neural networks (BNNs) have recently gained popularity due to their ability to
quantify model uncertainty. However, specifying a prior for BNNs that captures relevant …

Fine-tuned Large Language Models (LLMs) often suffer from overconfidence and poor
calibration, particularly when fine-tuned on small datasets. To address these challenges, we …

The linearized-Laplace approximation (LLA) has been shown to be effective and efficient in
constructing Bayesian neural networks. It is theoretically compelling since it can be seen as …

Deep neural networks (DNNs) have found successful applications in many fields, but their
black-box nature hinders interpretability. This is addressed by the neural additive model …

Constrained optimization problems arise in various engineering system operations such as
inventory management and electric power grids. However, the requirement to repeatedly …

Knowing which features of a multivariate time series to measure and when is a key task in
medicine, wearables, and robotics. Better acquisition policies can reduce costs while …

Deep neural networks are highly effective but suffer from a lack of interpretability due to their
black-box nature. Neural additive models (NAMs) solve this by separating into additive sub …