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Alaia Solko-Breslin

2 accepted papers

2025

CTSketch: Compositional Tensor Sketching for Scalable Neurosymbolic Learning

NeurIPS 2025poster

Many computational tasks benefit from being formulated as the composition of neural networks followed by a discrete symbolic program. The goal of neurosymbolic learning is to train the neural networks using end-to-end input-output labels of the composite. We introduce CTSketch, a novel, scalable neu…

Cited by 0SourceScholar
2024

Data-Efficient Learning with Neural Programs

NeurIPS 2024poster

Many computational tasks can be naturally expressed as a composition of a DNN followed by a program written in a traditional programming language or an API call to an LLM. We call such composites "neural programs" and focus on the problem of learning the DNN parameters when the training data consis…