Abstract: Modern machine learning systems excel at pattern recognition but remain limited in their ability to autonomously discover strategies for planning, exploration, and adaptation; core components of sequential decision making. In this talk, I present recent advances that take a learning-to-learn perspective on this challenge, showing how decision-making algorithms themselves can emerge from data.