Intelligence relies on an agent's knowledge of what it does not know. This capability can be
assessed based on the quality of joint predictions of labels across multiple inputs. In …

Artificial Intelligence (AI) use in automated Electrocardiogram (ECG) classification has
continuously attracted the research community's interest, motivated by their promising …

Predicting properties of proteins is of interest for basic biological understanding and protein
engineering alike. Increasingly, machine learning (ML) approaches are being used for this …

We investigate the extent to which offline demonstration data can improve online learning. It
is natural to expect some improvement, but the question is how, and by how much? We …

We explore uncertainty quantification in large language models (LLMs), with the goal to
identify when uncertainty in responses given a query is large. We simultaneously consider …

We introduce ensembles of stochastic neural networks to approximate the Bayesian
posterior, combining stochastic methods such as dropout with deep ensembles. The stochas …

A popular perspective in Reinforcement learning (RL) casts the problem as probabilistic
inference on a graphical model of the Markov decision process (MDP). The core object of …

A default assumption in the design of reinforcement-learning algorithms is that a decision-
making agent always explores to learn optimal behavior. In sufficiently complex …

AI Uncertainty Quantification (UQ) has the potential to improve human decision-making
beyond AI predictions alone by providing additional useful probabilistic information to users …

Exploration remains a key challenge in deep reinforcement learning (RL). Optimism in the
face of uncertainty is a well-known heuristic with theoretical guarantees in the tabular …