Spring 2024, Stanford MS&E233: Game Theory, Data Science and AI
Winter 2024, Stanford MS&E228/CS226: Applied Causal Inference Powered by ML and AI
Fall 2023, Stanford MS&E328/CS328: Foundations of Causal Machine Learning
Spring 2023, Stanford MS&E328/CS328: Foundations of Causal Machine Learning
Winter 2023, Stanford MS&E228: Applied Causal Inference Powered by ML and AI
Spring 2019, MIT EECS, 6.853 Algorithmic Game Theory and Data Science
Spring 2017, MIT EECS, 6.853 Algorithmic Game Theory and Data Science
- Online learning, online convex optimization, minimax theorem from no-regret learning. [Lecture 3] [Lecture 4]
- Online learning and general games: correlated equilibrium via no-regret, quality of no-regret outcomes [Lecture 7] [Lecture 8]
- Intro to Statistical Learning Theory: Rademacher complexity, Growth number [Lecture 13] [Lecture 14]
- Statistical Learning Theory and Mechanism Design: learning optimal auctions from samples [Lecture 15] [Lecture 16]
- Estimation in Games: learning value CDFs from bids in an auction [Lecture 20]
- Revenue inference and A/B testing in auctions [Lecture 21]
- Welfare guarantees from data [Lecture 22a]
- Online learning of mechanisms [Lecture 22b]
The remainder of the lectures of the course can be found here: [AGT and Data Science Lectures]
Cornell mini-course: Econometric Theory for Games
- Part I: Intro to Econometrics and Econometrics of Bayesian Games [Part I]
- Part II: Complete Information Games and Set Inference [Part II]
- Part III: Dynamic Games and Auctions [Part II]
Tutorial on Econometrics and Machine Learning
- Econometrics and Machine Learning [Presentation]
Tutorial on Game Theoretic Opportunities and Challenges in Generative Adversarial Networks
- Game Theoretic Opportunities and Challenges in Generative Adversarial Networks [Presentation]
Learning and Mechanism Design Survey
- Learning and Mechanism Design [Presentation]