Abstract
As machine learning practices continue to grow in scale, how can we ensure efficient development of ML methods? My research studies carefully selected "sandboxes" -- minimal, representative abstractions of complex systems, which facilitate theoretical analyses and rapid experimental iterations. We will discuss examples of using sandboxes to understand and diagnose current methods, and the potential of this approach towards scientific and efficient progress.