We analyze the type of learned optimization that occurs when a learned model (such as a neural network) is itself an optimizer - a situation we refer to as mesa-optimization, a neologism we introduce in this paper. We believe that the possibility of mesa-... (more…)

This approach is contrary to Fisher's method where we formulate all theories before encountering the data. We came up with the model after we saw the cards we drew. (more…)

There are a lot of misconceptions and assumptions about machine learning in general and the mysticism of certain services that the big tech… (more…)

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offe... (more…)

In this interview with Suzana Ilic, founder of Machine Learning Tokyo, we ask about ML trends, challenges facing engineers, and how MLT grew into a community of +4800 members. (more…)