A fascinating hypothesis is that human and animal intelligence could be explained by a few
principles (rather than an encyclopaedic list of heuristics). If that hypothesis was correct, we …

The ongoing exponential rise in recording capacity calls for new approaches for analysing
and interpreting neural data. Effective dimensionality has emerged as an important property …

The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

The machine learning community's interest in causality has significantly increased in recent
years. My understanding of causality has been shaped by Judea Pearl and a number of …

Reinforcement learning (RL) allows an agent to solve sequential decision-making problems
by interacting with an environment in a trial-and-error fashion. When these environments are …

Representation learning is a central challenge across a range of machine learning areas. In
reinforcement learning, effective and functional representations have the potential to …

One of the main concerns of deep reinforcement learning (DRL) is the data inefficiency
problem, which stems both from an inability to fully utilize data acquired and from naive …

This paper investigates whether learning contingency-awareness and controllable aspects
of an environment can lead to better exploration in reinforcement learning. To investigate …

Advances in unsupervised learning enable reconstruction and generation of samples from
complex distributions, but this success is marred by the inscrutability of the representations …

The framework of causal models provides a principled approach to causal reasoning,
applied today across many scientific domains. Here we present this framework in the …