The syllabus for this semester's graduate course on machine learning.| arg min
Do machine learning researchers actually care about simple baselines?| arg min
Revisiting Sutton’s Bitter Lesson essay in the light of GPT5| www.argmin.net
A book about how we gave computers the power to decide for us| www.argmin.net
Deriving the laws of probability through superforecasting| www.argmin.net
Imagining a new syllabus for a first course on machine learning.| www.argmin.net
Meehl's Philosophical Psychology, Lecture 5, Part 1.| www.argmin.net
The commitments required for fully open source machine learning| www.argmin.net
The NeurIPS checklist corroborates the bureaucratic theory of statistics.| arg min
Jessica Dai on theory for the world as it could be| www.argmin.net
To figure out the purpose of forecasting, I put on my Dan Davies hat and ask, “What do forecasts do?”| arg min
Remind me what happens when a measure becomes a target.| www.argmin.net
Neil Postman's Moral Theology and The Power of Storytelling| www.argmin.net
Four positive things I learned in our class on Machine Learning Evaluation| www.argmin.net
Why do evaluations tend to find that social programs don't work?| www.argmin.net
In which I feel very seen by David Donoho| www.argmin.net
Test-set reuse: the problem that wasn't.| www.argmin.net
Introduction: Blogging Philosophical Psychology| www.argmin.net
