One application area of computational methods in drug discovery is the automated design of
small molecules. Despite the large number of publications describing methods and their …

An in-depth understanding of uncertainty is the first step to making effective decisions under
uncertainty. Machine/deep learning (ML/DL) has been hugely leveraged to solve complex …

Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …

As robots acquire increasingly sophisticated skills and see increasingly complex and varied
environments, the threat of an edge case or anomalous failure is ever present. For example …

We investigate the benefit of treating all the parameters in a Bayesian neural network
stochastically and find compelling theoretical and empirical evidence that this standard …

Trustworthy language models should abstain from answering questions when they do not
know the answer. However, the answer to a question can be unknown for a variety of …

Long samples of text from neural language models can be of poor quality. Truncation
sampling algorithms--like top-$ p $ or top-$ k $--address this by setting some words' …

Ensembling neural networks is an effective way to increase accuracy, and can often match
the performance of individual larger models. This observation poses a natural question …

A set of novel approaches for estimating epistemic uncertainty in deep neural networks with
a single forward pass has recently emerged as a valid alternative to Bayesian Neural …

Preference optimization, particularly through Reinforcement Learning from Human
Feedback (RLHF), has achieved significant success in aligning Large Language Models …