1 citations · 1 across the 4 of their papers we have counts for
4 papers
Bridging the Gap Between Scientific Laws Derived by AI Systems and Canonical Knowledge via Abductive Inference with AI-Noether
Karan Srivastava, Sanjeeb Dash, Ryan Cory-Wright +3
Advances in AI have shown great potential in contributing to the acceleration of scientific discovery. Symbolic regression can fit interpretable models to data, but these models ar…
SynPAT: A System for Generating Synthetic Physical Theories with Data
Jonathan Lenchner, Karan Srivastava, Joao Goncalves +2
Machine-assisted methods for discovering physical laws from background theory and data have recently emerged, promising to advance our understanding of the physical world. However,…
Generative Modeling for Mathematical Discovery
Jordan S. Ellenberg, Cristofero S. Fraser-Taliente, Thomas R. Harvey +2
We present a new implementation of the LLM-driven genetic algorithm {\it funsearch}, whose aim is to generate examples of interest to mathematicians and which has already had some…
A Perturbation Bound on the Subspace Estimator from Canonical Projections
Karan Srivastava, Daniel L. Pimentel-Alarcón
This paper derives a perturbation bound on the optimal subspace estimator obtained from a subset of its canonical projections contaminated by noise. This fundamental result has imp…