# Self-Calibrating Neural-Probabilistic Model for Authorship Verification Under Covariate Shift

arXiv: https://arxiv.org/abs/2106.11196 · HTML: https://mlsanity.com/paper/2106.11196/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 9 of 9)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Regularizing Neural Networks by Penalizing Confident Output Distributions](https://mlsanity.com/paper/1701.06548/) (2017)
- [Explainable Authorship Verification in Social Media via Attention-based Similarity Learning](https://mlsanity.com/paper/1910.08144/) (2019)
- [Unsupervised Calibration under Covariate Shift](https://mlsanity.com/paper/2006.16405/) (2020)
- [Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix](https://mlsanity.com/paper/1705.03995/) (2017)
- [Deep Bayes Factor Scoring for Authorship Verification](https://mlsanity.com/paper/2008.10105/) (2020)

## Built on by (most cited first, 3 of 3)

- [O2D2: Out-Of-Distribution Detector to Capture Undecidable Trials in Authorship Verification](https://mlsanity.com/paper/2106.15825/) (2021)
- [Data Science Kitchen at GermEval 2021: A Fine Selection of Hand-Picked Features, Delivered Fresh from the Oven](https://mlsanity.com/paper/2109.02383/) (2021)
- [Fusing Stylometric and Embedding Systems to Estimate Authorship Likelihood Ratios in Japanese](https://mlsanity.com/paper/2606.13991/) (2026)

## Abstract (arXiv)

We are addressing two fundamental problems in authorship verification (AV): Topic variability and miscalibration. Variations in the topic of two disputed texts are a major cause of error for most AV systems. In addition, it is observed that the underlying probability estimates produced by deep learning AV mechanisms oftentimes do not match the actual case counts in the respective training data. As such, probability estimates are poorly calibrated. We are expanding our framework from PAN 2020 to include Bayes factor scoring (BFS) and an uncertainty adaptation layer (UAL) to address both problems. Experiments with the 2020/21 PAN AV shared task data show that the proposed method significantly reduces sensitivities to topical variations and significantly improves the system's calibration.