Stochastic Perturbations of Tabular Features for Non-Deterministic Inference with Automunge
This work addresses fairness and adversarial robustness in tabular machine learning, though it appears incremental as it builds on known noise injection techniques.
The paper tackles the problem of deterministic inference in tabular data by injecting Gaussian noise into features during inference, resulting in non-deterministic outcomes that can enhance fairness and adversarial protection. It introduces the Automunge library, which integrates random sampling and quantum circuits for this purpose.
Injecting gaussian noise into training features is well known to have regularization properties. This paper considers noise injections to numeric or categoric tabular features as passed to inference, which translates inference to a non-deterministic outcome and may have relevance to fairness considerations, adversarial example protection, or other use cases benefiting from non-determinism. We offer the Automunge library for tabular preprocessing as a resource for the practice, which includes options to integrate random sampling or entropy seeding with the support of quantum circuits, representing a new way to channel quantum algorithms into classical learning.