Many training data attribution (TDA) methods aim to estimate how a model's behavior would change if one or more data points were removed from the training set.

Since the inception of our planet, the meteorological environment, as reflected through spatio-temporal data, has always been a fundamental factor influencing human life, socio-economic progress, and ecological conservation.

Imitation learning from human feedback studies how to train well-performed imitation agents with an annotator's relative comparison of two demonstrations

LMC can suffer from slow convergence - requiring a large number of steps of small step-size to obtain good quality samples.

This work investigates how these complexities necessarily arise for feature learning in the presence of computational-statistical gaps.

As a result, it tractably draws samples even when the matrices forming the Khatri-Rao product have tens of millions of rows each.