Interactive Learning and Interventional Representations

We rethink the principles of interactive models of learning, exploring the role of causal modelling in bridging the gap between observational and interventional learning. The ultimate goal is to understand the organizing principles underlying robust intelligent behaviour, and to enable reliable learning-based decision systems for high-stakes real-world applications.

• Principles of learning-in-the-loop systems
• Online and reinforcement learning
• Causal inference
• Interacting learning systems (multi-agent learning, games, networks)