Traditional pain assessment approaches ranging from self-reporting methods, to
observational scales, rely on the ability of an individual to accurately assess and …

We propose a reinforcement learning (RL) algorithm that uses mutual-information
regularization to optimize a prior action distribution for better performance and exploration …

In its most basic form, decision-making can be viewed as a computational process that
progressively eliminates alternatives, thereby reducing uncertainty. Such processes are …

Joining multiple decision-makers together is a powerful way to obtain more sophisticated
decision-making systems, but requires to address the questions of division of labor and …

One notable weakness of current machine learning algorithms is the poor ability of models
to solve new problems without forgetting previously acquired knowledge. The Continual …

Expected utility models are often used as a normative baseline for human performance in
motor tasks. However, this baseline ignores computational costs that are incurred when …

Information-theoretic bounded rationality describes utility-optimizing decision-makers whose
limited information-processing capabilities are formalized by information constraints. One of …

Catastrophic forgetting remains a challenge for artificial learning systems, especially in the
case of Online learning, where task information is unavailable. This work proposes a novel …

One weakness of machine learning algorithms is the poor ability of models to solve new
problems without forgetting previously acquired knowledge. The Continual Learning (CL) …

The goal of meta-learning is to train a model on a variety of learning tasks, such that it can
adapt to new problems within only a few iterations. Here we propose a principled …