# Stochastic Perturbations of Tabular Features for Non-Deterministic Inference with Automunge

arXiv: https://arxiv.org/abs/2202.09248 · HTML: https://mlsanity.com/paper/2202.09248/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 81 references that are in the map.
- First posted on arXiv 18 Feb 2022.

## Builds on (most cited first, 27 of 27)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Certified Adversarial Robustness via Randomized Smoothing](https://mlsanity.com/paper/1902.02918/) (2019)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [The effect of data encoding on the expressive power of variational quantum machine learning models](https://mlsanity.com/paper/2008.08605/) (2020)
- [Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning](https://mlsanity.com/paper/1805.08651/) (2018)
- [Adversarial Logit Pairing](https://mlsanity.com/paper/1803.06373/) (2018)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [Well-tuned Simple Nets Excel on Tabular Datasets](https://mlsanity.com/paper/2106.11189/) (2021)
- [SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning](https://mlsanity.com/paper/2110.04361/) (2021)
- [Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck](https://mlsanity.com/paper/1910.12911/) (2019)
- [Robustness of classifiers: from adversarial to random noise](https://mlsanity.com/paper/1608.08967/) (2016)
- [Noisy intermediate-scale quantum (NISQ) algorithms](https://mlsanity.com/paper/2101.08448/) (2021)
- [Stochastic Pooling for Regularization of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1301.3557/) (2013)
- [Noisy Recurrent Neural Networks](https://mlsanity.com/paper/2102.04877/) (2021)
- [Explicit Regularisation in Gaussian Noise Injections](https://mlsanity.com/paper/2007.07368/) (2020)
- [Exact expressions for double descent and implicit regularization via surrogate random design](https://mlsanity.com/paper/1912.04533/) (2019)
- [Edward: A library for probabilistic modeling, inference, and criticism](https://mlsanity.com/paper/1610.09787/) (2016)
- [Analyzing noise in autoencoders and deep networks](https://mlsanity.com/paper/1406.1831/) (2014)
- [On the Inherent Regularization Effects of Noise Injection During Training](https://mlsanity.com/paper/2102.07379/) (2021)
- [Circa: Stochastic ReLUs for Private Deep Learning](https://mlsanity.com/paper/2106.08475/) (2021)
- [Flexible FOND Planning with Explicit Fairness Assumptions](https://mlsanity.com/paper/2103.08391/) (2021)
- [On Data-centric Myths](https://mlsanity.com/paper/2111.11514/) (2021)
- [How Much Data Do You Need? An Operational, Pre-Asymptotic Metric for Fat-tailedness](https://mlsanity.com/paper/1802.05495/) (2018)
- [Adaptive Multi-View ICA: Estimation of noise levels for optimal inference](https://mlsanity.com/paper/2102.10964/) (2021)

## Built on by (most cited first, 1 of 1)

- [Feature Encodings for Gradient Boosting with Automunge](https://mlsanity.com/paper/2209.12309/) (2022)

## Abstract (arXiv)

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.