— Lecture 7 —

Equilibrium Computation and Machine Learning

Lecturer: Constantinos Daskalakis (MIT)
Time: (Zurich time)
Slides: Click here to download!
Recording: Click here to view! (only for ETH members)


Machine learning has recently made significant advances in single-agent learning challenges, much of that progress being fueled by the empirical success of gradient descent-based methods in computing local optima of non-convex optimization problems. In multi-agent learning challenges, the role of single-objective optimization is played by equilibrium computation. On this front, however, optimization methods have remained less successful in settings, such as adversarial training and multi-agent reinforcement learning, motivated by deep learning applications. Gradient-descent based methods commonly fail to identify equilibria, and even computing local approximate equilibria has remained daunting. We discuss equilibrium computation challenges motivated by machine learning applications through a combination of learning-theoretic, complexity-theoretic, game-theoretic and topological techniques, presenting obstacles and opportunities for machine learning and game theory going forward. No deep learning / complexity theory knowledge will be assumed for this talk.

Recommended reading:

  • Daskalakis, C. et al. (2018). Training GANs with Optimism. arXiv:1711.00141. [Pages 1–10].
  • Daskalakis, C. et al. (2020). The Complexity of Constrained Min-Max Optimization. arXiv:2009.09623. [Pages 1–8; optional!].
  • Daskalakis, C. et al. (2021). Near-Optimal No-Regret Learning in General Games. arXiv:2108.06924. [Optional!]