In many reinforcement learning tasks, the agent has to learn to interact...
Identifying the causal variables of an environment and how to intervene ...
Dynamical systems with complex behaviours, e.g. immune system cells
inte...
Latent neural ordinary differential equations have been proven useful fo...
Causal representation learning is the task of identifying the underlying...
Dealing with non-stationarity in environments (i.e., transition dynamics...
Understanding the latent causal factors of a dynamical system from visua...
Most approaches in reinforcement learning (RL) are data-hungry and speci...
A growing body of work has begun to study intervention design for effici...
Deploying deep reinforcement learning in safety-critical settings requir...
Causal discovery algorithms infer causal relations from data based on se...
An important goal in both transfer learning and causal inference is to m...
We introduce Joint Causal Inference (JCI), a powerful formulation of cau...
Constraint-based causal discovery from limited data is a notoriously
dif...