# Patterns for Learning with Side Information

arXiv: https://arxiv.org/abs/1511.06429 · HTML: https://mlsanity.com/paper/1511.06429/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2015.

## Built on by (most cited first, 11 of 11)

- [Structure in Deep Reinforcement Learning: A Survey and Open Problems](https://mlsanity.com/paper/2306.16021/) (2023)
- [Transfer and Marginalize: Explaining Away Label Noise with Privileged Information](https://mlsanity.com/paper/2202.09244/) (2022)
- [Object Detectors in the Open Environment: Challenges, Solutions, and Outlook](https://mlsanity.com/paper/2403.16271/) (2024)
- [PNeRV: Enhancing Spatial Consistency via Pyramidal Neural Representation for Videos](https://mlsanity.com/paper/2404.08921/) (2024)
- [Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models](https://mlsanity.com/paper/2110.14993/) (2021)
- [An Information-Theoretic Framework for Supervised Learning](https://mlsanity.com/paper/2203.00246/) (2022)
- [Generalized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object Detection](https://mlsanity.com/paper/2504.07060/) (2025)
- [Efficient learning of nonlinear prediction models with time-series privileged information](https://mlsanity.com/paper/2209.07067/) (2022)
- [Rethinking Knowledge Transfer in Learning Using Privileged Information](https://mlsanity.com/paper/2408.14319/) (2024)
- [Learning to Score](https://mlsanity.com/paper/2504.14302/) (2025)
- [Context-Informed Ship Trajectory Prediction via Conditional Attention](https://mlsanity.com/paper/2607.27418/) (2026)

## Abstract (arXiv)

Supervised, semi-supervised, and unsupervised learning estimate a function given input/output samples. Generalization of the learned function to unseen data can be improved by incorporating side information into learning. Side information are data that are neither from the input space nor from the output space of the function, but include useful information for learning it. In this paper we show that learning with side information subsumes a variety of related approaches, e.g. multi-task learning, multi-view learning and learning using privileged information. Our main contributions are (i) a new perspective that connects these previously isolated approaches, (ii) insights about how these methods incorporate different types of prior knowledge, and hence implement different patterns, (iii) facilitating the application of these methods in novel tasks, as well as (iv) a systematic experimental evaluation of these patterns in two supervised learning tasks.