Avoidance of Traps for Nonconvex Stochastic Optimization and Equilibrium Learning in Games

Explore a 25-minute conference talk from the Fourth Symposium on Machine Learning and Dynamical Systems, presented by Anas Barakat from ETH Zürich at the Fields Institute. Delve into strategies for avoiding traps in nonconvex stochastic optimization and equilibrium learning in games. Gain insights into advanced techniques that address challenges in machine learning and dynamical systems, with a focus on improving optimization processes and game theory applications.

Avoidance of traps for nonconvex stochastic optimization and equilibrium learning in games