While reinforcement learning (RL) achieves tremendous success in sequential decision-
making problems of many domains, it still faces key challenges of data inefficiency and the …

Multi-agent learning (MAL) studies how agents learn to behave optimally and adaptively
from their experience when interacting with other agents in dynamic environments. The …

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 …

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

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 …

Model-free reinforcement learning (RL) can be used to learn effective policies for complex
tasks, such as Atari games, even from image observations. However, this typically requires …

Independent component analysis provides a principled framework for unsupervised
representation learning, with solid theory on the identifiability of the latent code that …

Despite recent successes of reinforcement learning (RL), it remains a challenge for agents
to transfer learned skills to related environments. To facilitate research addressing this …

The ongoing success of visual question answering methods has been somwehat surprising
given that, at its most general, the problem requires understanding the entire variety of both …

Many reinforcement learning (RL) environments consist of independent entities that interact
sparsely. In such environments, RL agents have only limited influence over other entities in …