Exploring the data landscape via metagradient descent

This project was done as part of the Iliad Fellowship. L.J. did this work while at EleutherAI. From functions to data That we can learn efficiently from our environment at all tells us something about (1) the world and (2) ourselves: (1) Regularities in the world are simple enough to be picked up from limited experience. (2) Our inductive biases favour precisely those regularities. Learning, in this case, is effective because the two are matched. Considering a neural network with a fixed architecture lets us probe the second half of this match directly, by asking which (classes of) functions are easy or difficult for them to learn. Neural networks learn to approximate a target function by minimising a loss over the training set We may ask: which (classes of) target functions are easy or difficult to learn for the model? This will depend on the loss function and the choice of the training set We say that the model generalises if it successfully learns to approximate beyond the training set By choosing the subset or, as we will see, by weighting it, we can control the generalisation behavior and hence what is learned. In this setting, our question becomes: which (classes of) data are