Overview
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.
Activities
Upcoming
- Bi-weekly reading and discussion group on ML and economics, meeting in even weeks of term.
- Youtube channel of workshops and discussions.
Previous
- May 2024: Workshop on Economic Analyses of Science , featuring tutorials and frontier talks. All talks are available on Youtube.
- May 2023: Workshop on Social foundations for statistics and machine learning, similarly featuring tutorials and frontier talks. All talks are available on Youtube.
- June 2022: 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. Information on this workshop can be found at Machine Learning and Economics Jamboree 2022.
- April 2021: Conference on Machine learning and economic inequality. Recordings of the talks are available on Youtube.
- I teach an MPhil course on the foundations of ML for economists.
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
Reading and discussion group
Michaelmas term 2024
- Time: 2:30pm, Tuesdays in even weeks. (That is: 22 Oct, 5 Nov, 19 Nov, and 3 Dec)
- Location: SR A, Manor Road Building (There will be coffee and pastries!).
- For those who cannot make in person, please join via Zoom.
Topic: Explainability and explanations
- W2: Model interpretability in machine learning
The Mythos of Model Interpretability
Presenter: Gregory Levy - W4: Explanations of automated decisions and the law
Counterfactual Explanations without Opening the Black Box
Presenter: Rania Belahsen - W6: Explanation and the metaphysics of causality
Two concepts of causation (Chapter 9 in Causation and counterfactuals)
Presenter: Jeremy Large. Slides - W8: Adversarial perturbations in computer vision
Universal adversarial perturbations
Presenter: Maximilian Reith
Hilary term 2024
Topic: Applications of AI
Curation this term thanks to Friedrich Geiecke!
Tentative outline:
- W2: Reinforcement learning from human feedback / AI feedback
Training language models to follow instructions with human feedback,
Scaling Reinforcement Learning from Human Feedback with AI Feedback
Presenter: Friedrich Geiecke. - W4: Labor market impact of LLMs
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
Generative AI at Work
Presenter: Jacob Greenspon. - W6: Diffusion models (image generation)
Understanding Diffusion Models: A Unified Perspective,
Diffusion Models: A Comprehensive Survey of Methods and Applications
Presenter: Maximilian Kasy. Slides - W8: AI in science
Learning skillful medium-range global weather forecasting
Highly accurate protein structure prediction with AlphaFold
Michaelmas term 2023
Topic: Large Language Models.
- W2: Foundations (Neural networks and natural language processing)
Chapters 6,7, and 9 of Speech and Language Processing
Presenter: James Dufy. Slides - W4: Foundations (Transformers)
Chapters 10 and 11 of Speech and Language Processing
Presenter: Maximilian Kasy. Slides - W6: Practical implementation
Huggingface NLP course
Presenter: Jeremy Large. Slides, Python notebook - W8: The impact of large language models
ChatGPT Is a Blurry JPEG of the Web
The Debate Over Understanding in AI’s Large Language Models
Presenter: Giulia Caprini
Hilary term 2023
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
- W2
- W4
- W6: (Martin Weidner guest edition)
Hilary term 2022
-
Slides for the coordinating meeting: Slides
- W2:
- W4:
- W6
- W8