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I: Data problems (and solution concepts) in ML

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Suppose you wanted to train an ML model. Even after deciding on the best training recipe to use, and securing enough compute to train the model, you’ll have to answer questions like: Then, after you train your model, you’ll observe its behavior, and have more questions: What all of these questions have in common is that they are “data questions.” They require us to relate the output of machine learning models (whether it’s performance, specific predictions, or general behaviors) to the data that we train them on. The general class of methods for answering these questions is called data attribution. The goal of this chapter—and more broadly, this tutorial—is to survey the large (and growing) field of data attribution. Our goals will be to: In this chapter, we’re going to talk about three different perspectives (or solution concepts), based on three different conceptions of the data attribution problem. In the coming chapters, we are going to focus in on a specific one of these solution

I: Data problems (and solution concepts) in ML July 18, 2024 --> Suppose you wanted to train an ML model. Even after deciding on the best training recipe to use, and securing enough compute to train the model, you’ll have to answer questions like: What types of data should I train my model on? How much should I pay for different providers’ data? Can I get away with using less data, and if so how? Then, after you train your model, you’ll observe its behavior, and have more questions: Which training data points were responsible for this unwanted behavior? How sensitive is my model to maliciously

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