The last few years have seen rapid developments in the deployment and adoption of ML systems. And yet, we lack a cohesive understanding of how these systems work, and the principles and laws (if any!) that govern their behavior. To this end, the goal of this reading group will be to explore the intersection of cutting-edge experiments and corresponding explanations, with the goal of answering a few questions:

  • How can tools from statistics, CS theory, and operations inform a better understanding of machine learning algorithms and systems?
  • What are the right questions to ask, and phenomena to explain—at what level of abstraction should we be aiming to explain them?
  • How might we devise theoretical models that not only explain unexpected phenomena, but also predict new phenomena that we can verify experimentally?

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