Sample complexity in machine learning concerns how training methods and architectures maximize model performance on a given dataset.

“Yeah. So I think when we're talking about producing a good machine learning model, there's two dimensions. One is given a data set, you know, what are the training algorithms, neural architectures, and all that to maximize the model's performance on that data set.”

“You know, that's called sample complexity in machine learning. That's the holy grail of machine learning research, to get low sample complexity. When people talk about recursive self-improvement, they're talking about, uh, AI inventing new ideas typically to bring down that sample complexity, better architectures.”