This webpage provides information for the informal Machine Learning (ML) and Economics group at the Department of Economics, University of Oxford, coordinated by Maximilian Kasy.
The goal of this group is to discuss research and develop a common research agenda at the intersection of ML and economics. The focus is on conceptual and methodological contributions of economics to ML and of ML to economics. These two fields share a common language in the frameworks of optimization, probability, and decision theory. Economics has much to contribute to ML with its considerations of multiple agents, inequality and conflicting interests, and private information. ML has much to contribute to economics with its insights on supervised and active learning, considerations of non-traditional data types, and adaptive decision-making. Special emphasis will be put on the social impact of ML, and on non-commercial applications of ML.
- Bi-weekly reading and discussion group on ML and economics, meeting in even weeks of term, starting in HT 2022.
- On June 20 2022, we will organize a small cross-departmental workshop on ML and economics, with presentations of ongoing work and discussants, as well as a keynote by Michael Jordan from UC Berkeley. The tentative schedule can be found at Machine Learning and Economics Jamboree 2022.
- I started teaching a new MPhil course on the foundations of ML for economists in HT 2022.
All faculty, post-docs, and doctoral students are invited to audit this course and participate in discussions.
Syllabus, slides and readers can be found here: Foundations of Machine Learning
- In April 2021, we organized a conference on Machine learning and economic inequality. Recordings of the talks are available on Youtube.
Reading and discussion group
Hilary term 2023:
- 2:30pm, Tuesdays in even weeks
(That is: 24 Jan, 7 Feb, 21 Feb, 7 Mar)
Manor Road Building, Seminar Room B
This term we will be discussing several chapters of the book Prediction, learning, and games by Nicolò Cesa-Bianchi and Gábor Lugosi. This book builds on ideas in game theory and learning theory, and provides a comprehensive framework for online decision-making and adversarial learning.
We will cover the following chapters:
- W2: Chapter 2 (Prediction with expert advice)
- W4: Chapter 4 (Randomized prediction)
- W6: Chapter 6 (Prediction with limited feedback)
- W8: Chapter 7 (Prediction and playing games)
Michaelmas term 2022:
No meetings (Max on sabbatical at MIT).
Trinity term 2022:
- 2:30pm, Tuesdays in even weeks
(That is: 3 May, 17 May, 31 May, 14 June)
Manor Road Building, Seminar Room C
- W6: (Martin Weidner guest edition)
- W8: In preparation. Suggestions welcome!
Hilary term 2022:
- Slides for the coordinating meeting: Slides
- Abigail Adams-Prassl: Website
- Jeremias Adams-Prassl: Website
- Frank DiTraglia: Website, Code
- Edith Elkind: Website
- Paul Goldberg: Website
- Maximilian Kasy: Website, Code
- Margaryta Klymak: Website
- Anders Kock: Website
- Pantelis Koutroumpis: Website
- Nathaniel Lane: Website, Code
- Jeremy Large: Website, Code
- Michael McMahon: Website, Code
- Barbara Petrongolo: Website
- Charles Rahal: Website, Code
- Kevin Sheppard: Website, Code
- Alexander Teytelboym: Website
- Severine Toussaert: Website
- Martin Weidner: Website
- Frank Windmeijer: Website
- Francesco Zanetti: Website