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AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
Silviu-Marian Udrescu · Andrew K Tan · Jiahai Feng · Orisvaldo Neto · Tailin Wu · Max Tegmark

Wed Dec 09 09:00 PM -- 11:00 PM (PST) @ Poster Session 4 #1227

We present an improved method for symbolic regression that seeks to fit data to formulas that are Pareto-optimal, in the sense of having the best accuracy for a given complexity. It improves on the previous state-of-the-art by typically being orders of magnitude more robust toward noise and bad data, and also by discovering many formulas that stumped previous methods. We develop a method for discovering generalized symmetries (arbitrary modularity in the computational graph of a formula) from gradient properties of a neural network fit. We use normalizing flows to generalize our symbolic regression method to probability distributions from which we only have samples, and employ statistical hypothesis testing to accelerate robust brute-force search.

Author Information

Silviu-Marian Udrescu (MIT)
Andrew K Tan (Massachusetts Institute of Technology)
Jiahai Feng (MIT)
Orisvaldo Neto (MIT)
Tailin Wu (Stanford)

Tailin Wu is a postdoc researcher in the Department of Computer Science at Stanford University, working with professor Jure Leskovec. His research interests include representation learning, reasoning, and AI for science.

Max Tegmark (MIT)

Max Tegmark is a professor doing physics and AI research at MIT, and advocates for positive use of technology as president of the Future of Life Institute. He is the author of over 250 publications as well as the New York Times bestsellers “Life 3.0: Being Human in the Age of Artificial Intelligence” and "Our Mathematical Universe: My Quest for the Ultimate Nature of Reality". His AI research focuses on intelligible intelligence. His work with the Sloan Digital Sky Survey on galaxy clustering shared the first prize in Science magazine’s “Breakthrough of the Year: 2003.”

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