TILOS Seminar: Synthetic Tasks as Testbeds for Attributing Model Behavior

11am PDT | Wednesday, March 12, 2025

Surbhi Goel, University of Pennsylvania

 
Abstract: Understanding how different components of the machine learning pipeline—spanning data composition, architectural choices, and optimization dynamics—shape model behavior remains a fundamental challenge. In this talk, I will argue that synthetic tasks, which enable precise control over data distribution and task complexity, serve as powerful testbeds for analyzing and attributing behaviors in deep learning. Focusing on the sparse parity learning problem, a canonical task in learning theory, I will present insights into: (1) the phenomenon of “hidden progress” in gradient-based optimization, where models exhibit consistent advancement despite stagnating loss curves; (2) nuanced trade-offs between data, compute, model width, and initialization that govern learning success; and (3) the role of progressive distillation in implicitly structuring curricula to accelerate feature learning. These findings highlight the utility of synthetic tasks in uncovering empirical insights into the mechanisms driving deep learning, without the cost of training expensive models.
This talk is based on joint work with a lot of amazing collaborators: Boaz Barak, Ben Edelman, Sham Kakade, Bingbin Liu, Eran Malach, Sadhika Malladi, Abhishek Panigrahi, Andrej Risteski, and Cyril Zhang.

Surbhi Goel is the Magerman Term Assistant Professor of Computer and Information Science at the University of Pennsylvania. She is associated with the theory group, the ASSET Center on safe, explainable, and trustworthy AI systems, and the Warren Center for Network and Data Sciences. Surbhi’s research focuses on theoretical foundations of modern machine learning paradigms, particularly deep learning, and is supported by Microsoft Research and OpenAI. Previously, she was a postdoctoral researcher at Microsoft Research NYC and completed her Ph.D. at the University of Texas at Austin under Adam Klivans, receiving the UTCS Bert Kay Dissertation Award. She has also been a visiting researcher at IAS, Princeton, and the Simons Institute at UC Berkeley. Surbhi co-founded the Learning Theory Alliance (LeT‐All) and holds several leadership roles, including Office Hours co-chair for ICLR 2024 and co-treasurer for the Association for Computational Learning Theory.