# General Supervision via Probabilistic Transformations

arXiv: https://arxiv.org/abs/1901.08552 · HTML: https://mlsanity.com/paper/1901.08552/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

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

- [Minimax risk classifiers with 0-1 loss](https://mlsanity.com/paper/2201.06487/) (2022)
- [Deep GEM-Based Network for Weakly Supervised UWB Ranging Error Mitigation](https://mlsanity.com/paper/2305.13904/) (2023)
- [IIns-VAE+: A Robust Transfer Learning Framework for Environmental Identification in Wireless Sensing](https://mlsanity.com/paper/2609.06131/) (2026)

## Abstract (arXiv)

Different types of training data have led to numerous schemes for supervised classification. Current learning techniques are tailored to one specific scheme and cannot handle general ensembles of training data. This paper presents a unifying framework for supervised classification with general ensembles of training data, and proposes the learning methodology of generalized robust risk minimization (GRRM). The paper shows how current and novel supervision schemes can be addressed under the proposed framework by representing the relationship between examples at test and training via probabilistic transformations. The results show that GRRM can handle different types of training data in a unified manner, and enable new supervision schemes that aggregate general ensembles of training data.