# Transductive Model Selection under Prior Probability Shift

arXiv: https://arxiv.org/abs/2507.22647 · HTML: https://mlsanity.com/paper/2507.22647/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 30 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [Detecting and Correcting for Label Shift with Black Box Predictors](https://mlsanity.com/paper/1802.03916/) (2018)
- [Leveraging Unlabeled Data to Predict Out-of-Distribution Performance](https://mlsanity.com/paper/2201.04234/) (2022)
- [Learning by Transduction](https://mlsanity.com/paper/1301.7375/) (2013)
- [Predicting with Confidence on Unseen Distributions](https://mlsanity.com/paper/2107.03315/) (2021)
- [Kernel Density Estimation for Multiclass Quantification](https://mlsanity.com/paper/2401.00490/) (2023)
- [Bayesian Quantification with Black-Box Estimators](https://mlsanity.com/paper/2302.09159/) (2023)
- [TAR on Social Media: A Framework for Online Content Moderation](https://mlsanity.com/paper/2108.12752/) (2021)

## Abstract (arXiv)

Transductive learning is a supervised machine learning task in which, unlike in traditional inductive learning, the unlabelled data that require labelling are a finite set and are available at training time. Similarly to inductive learning contexts, transductive learning contexts may be affected by dataset shift, i.e., may be such that the IID assumption does not hold. We here propose a method, tailored to transductive classification contexts, for performing model selection (i.e., hyperparameter optimisation) when the data exhibit prior probability shift, an important type of dataset shift typical of anti-causal learning problems. In our proposed method the hyperparameters can be optimised directly on the unlabelled data to which the trained classifier must be applied; this is unlike traditional model selection methods, that are based on performing cross-validation on the labelled training data. We provide experimental results that show the benefits brought about by our method.