Inference Over Programs That Make Predictions

2018/10/02 by Yura Perov, Perov, Yura
Computer Science · #Adversarial Robustness in Machine Learning #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Programming Languages (cs.PL)

paper · pdf · doi:10.48550/arxiv.1810.01190

openalex publication_date 2018/10/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This abstract extends on the previous work (arXiv:1407.2646, arXiv:1606.00075) on program induction using probabilistic programming. It describes possible further steps to extend that work, such that, ultimately, automatic probabilistic program synthesis can generalise over any reasonable set of inputs and outputs, in particular in regard to text, image and video data.

Related

• Learning Probabilistic Programs
• TerpreT: A Probabilistic Programming Language for Program Induction
• Composing inference algorithms as program transformations
• Automated learning with a probabilistic programming language: Birch
• Program Analysis of Probabilistic Programs