@inproceedings{
kostic2024neural,
title={Neural Conditional Probability for Uncertainty Quantification},
author={Vladimir R Kostic and gregoire pacreau and Giacomo Turri and Pietro Novelli and Karim Lounici and Massimiliano Pontil},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=zXfhHJnMB2}
}
We introduce Neural Conditional Probability (NCP), an operator-theoretic approach to learning conditional distributions with 
a focus on statistical inference tasks. NCP can be used to build conditional confidence regions and extract key statistics such as 
conditional quantiles, mean, and covariance. It offers streamlined learning via a single unconditional training phase, allowing 
efficient inference without the need for retraining even when conditioning changes. By leveraging the approximation 
capabilities of neural networks, NCP efficiently handles a wide variety of complex probability distributions. 
We provide theoretical guarantees that ensure both optimization consistency and statistical accuracy. 
In experiments, we show that NCP with a 2-hidden-layer network matches or outperforms leading methods. 
This demonstrates that a a minimalistic architecture with a theoretically grounded loss can achieve 
competitive results,  even in the face of more complex architectures.
Neural Conditional Probability for Uncertainty Quantification
Vladimir R Kostic gregoire pacreau Giacomo Turri Pietro Novelli Karim Lounici Massimiliano Pontil