On the Conditioning Consistency Gap in Conditional Neural Processes

TL;DR

This paper introduces the conditioning consistency gap to quantify how much conditional neural processes violate stochastic process conditions, showing the gap diminishes with larger context sizes but can be significant in few-shot settings.

Contribution

It formally defines the conditioning consistency gap and derives tight bounds showing its decay rate for CNPs with bounded encoders and Lipschitz decoders.

Findings

The consistency gap is $O(1/n^2)$ for CNPs with bounded encoders and Lipschitz decoders.

The decay rate of the gap is proven to be tight.

Inconsistency is negligible for moderate context sizes but relevant in few-shot scenarios.

Abstract

Neural processes are meta-learning models that map context sets to predictive distributions. While inspired by stochastic processes, NPs do not generally satisfy the Kolmogorov consistency conditions required to define a valid stochastic process. This inconsistency is widely acknowledged but poorly understood. Practitioners note that NPs work well despite the violation, without quantifying what this means. We address this gap by defining the conditioning consistency gap, a KL divergence measuring how much a conditional neural process's (CNP) predictions change when a point is added to the context versus conditioned upon. Our main results show that for CNPs with bounded encoders and Lipschitz decoders, the consistency gap is $O(1/n^2)$ in context size $n$, and that this rate is tight. These bounds establish the precise sense in which CNPs approximate valid stochastic processes. The inconsistency is negligible for moderate context sizes but can be significant in the few-shot regime.