# Target inductive methods for zero-shot regression

arXiv: https://arxiv.org/abs/2402.01252 · HTML: https://mlsanity.com/paper/2402.01252/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 55 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 16 of 16)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Zero-Shot Relation Extraction via Reading Comprehension](https://mlsanity.com/paper/1706.04115/) (2017)
- [Zero-Shot Learning Through Cross-Modal Transfer](https://mlsanity.com/paper/1301.3666/) (2013)
- [Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2005.00891/) (2020)
- [Ridge Regression, Hubness, and Zero-Shot Learning](https://mlsanity.com/paper/1507.00825/) (2015)
- [Less is more: zero-shot learning from online textual documents with noise suppression](https://mlsanity.com/paper/1604.01146/) (2016)
- [Zero-Shot Learning via Joint Latent Similarity Embedding](https://mlsanity.com/paper/1511.04512/) (2015)
- [Transductive Zero-Shot Action Recognition by Word-Vector Embedding](https://mlsanity.com/paper/1511.04458/) (2015)
- [Alternative Semantic Representations for Zero-Shot Human Action Recognition](https://mlsanity.com/paper/1706.09317/) (2017)
- [Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of Actions](https://mlsanity.com/paper/1707.09145/) (2017)
- [A Concise Review of Transfer Learning](https://mlsanity.com/paper/2104.02144/) (2021)
- [Hyper-Process Model: A Zero-Shot Learning algorithm for Regression Problems based on Shape Analysis](https://mlsanity.com/paper/1810.10330/) (2018)
- [Zero-Shot Domain Adaptation via Kernel Regression on the Grassmannian](https://mlsanity.com/paper/1507.07830/) (2015)
- [CAZSL: Zero-Shot Regression for Pushing Models by Generalizing Through Context](https://mlsanity.com/paper/2003.11696/) (2020)
- [Target-Side Context for Discriminative Models in Statistical Machine Translation](https://mlsanity.com/paper/1607.01149/) (2016)

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

- [Learning A Universal Crime Predictor with Knowledge-guided Hypernetworks](https://mlsanity.com/paper/2511.02336/) (2025)

## Abstract (arXiv)

This research arises from the need to predict the amount of air pollutants in meteorological stations. Air pollution depends on the location of the stations (weather conditions and activities in the surroundings). Frequently, the surrounding information is not considered in the learning process. This information is known beforehand in the absence of unobserved weather conditions and remains constant for the same station. Considering the surrounding information as side information facilitates the generalization for predicting pollutants in new stations, leading to a zero-shot regression scenario. Available methods in zero-shot typically lean towards classification, and are not easily extensible to regression. This paper proposes two zero-shot methods for regression. The first method is a similarity based approach that learns models from features and aggregates them using side information. However, potential knowledge of the feature models may be lost in the aggregation. The second method overcomes this drawback by replacing the aggregation procedure and learning the correspondence between side information and feature-induced models, instead. Both proposals are compared with a baseline procedure using artificial datasets, UCI repository communities and crime datasets, and the pollutants. Both approaches outperform the baseline method, but the parameter learning approach manifests its superiority over the similarity based method.