# Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs

arXiv: https://arxiv.org/abs/2309.11726 · HTML: https://mlsanity.com/paper/2309.11726/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 53 references that are in the map.
- First posted on arXiv 21 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks](https://mlsanity.com/paper/1901.08584/) (2019)
- [Active learning of deep surrogates for PDEs: Application to metasurface design](https://mlsanity.com/paper/2008.12649/) (2020)
- [NEUZZ: Efficient Fuzzing with Neural Program Smoothing](https://mlsanity.com/paper/1807.05620/) (2018)
- [DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates](https://mlsanity.com/paper/2010.04017/) (2020)
- [One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks](https://mlsanity.com/paper/2103.15261/) (2021)
- [On Sound Compilation of Reals](https://mlsanity.com/paper/1309.2511/) (2013)
- [Programming with Neural Surrogates of Programs](https://mlsanity.com/paper/2112.06148/) (2021)

## Abstract (arXiv)

Programmers and researchers are increasingly developing surrogates of programs, models of a subset of the observable behavior of a given program, to solve a variety of software development challenges. Programmers train surrogates from measurements of the behavior of a program on a dataset of input examples. A key challenge of surrogate construction is determining what training data to use to train a surrogate of a given program. We present a methodology for sampling datasets to train neural-network-based surrogates of programs. We first characterize the proportion of data to sample from each region of a program's input space (corresponding to different execution paths of the program) based on the complexity of learning a surrogate of the corresponding execution path. We next provide a program analysis to determine the complexity of different paths in a program. We evaluate these results on a range of real-world programs, demonstrating that complexity-guided sampling results in empirical improvements in accuracy.