sum-of-squares

This paper establishes a characterization of the sum-of-squares degree barriers for the reweighted-hinge method in robust halfspace learning using the Christoffel function, revealing a margin-degree tradeoff and explicit outlier barriers.

This paper presents NSPI, a neuro-symbolic framework that combines LLMs and symbolic computation to prove polynomial inequalities. It uses LLM-generated sum-of-squares conjectures, refines them symbolically, and formally verifies the proofs in Lean, demonstrating scalability on polynomials with up to 10 variables.