# Learning from Label Proportions and Covariate-shifted Instances

arXiv: https://arxiv.org/abs/2411.12334 · HTML: https://mlsanity.com/paper/2411.12334/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 53 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 19 of 19)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [On Learning from Label Proportions](https://mlsanity.com/paper/1402.5902/) (2014)
- [Deep multi-class learning from label proportions](https://mlsanity.com/paper/1905.12909/) (2019)
- [Learning from Label Proportions by Learning with Label Noise](https://mlsanity.com/paper/2203.02496/) (2022)
- [Co-training for Demographic Classification Using Deep Learning from Label Proportions](https://mlsanity.com/paper/1709.04108/) (2017)
- [Learning from Label Proportions with Generative Adversarial Networks](https://mlsanity.com/paper/1909.02180/) (2019)
- [Weakly Supervised Classification in High Energy Physics](https://mlsanity.com/paper/1702.00414/) (2017)
- [Easy Learning from Label Proportions](https://mlsanity.com/paper/2302.03115/) (2023)
- [Two-stage Training for Learning from Label Proportions](https://mlsanity.com/paper/2105.10635/) (2021)
- [Reacting to Variations in Product Demand: An Application for Conversion Rate (CR) Prediction in Sponsored Search](https://mlsanity.com/paper/1806.08211/) (2018)
- [Deep Learning from Label Proportions for Emphysema Quantification](https://mlsanity.com/paper/1807.08601/) (2018)
- [Learning from Aggregated Data: Curated Bags versus Random Bags](https://mlsanity.com/paper/2305.09557/) (2023)
- [Challenges and approaches to privacy preserving post-click conversion prediction](https://mlsanity.com/paper/2201.12666/) (2022)
- [Fast learning from label proportions with small bags](https://mlsanity.com/paper/2110.03426/) (2021)
- [LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions](https://mlsanity.com/paper/2310.10096/) (2023)
- [Age-Dependent Differential Privacy](https://mlsanity.com/paper/2209.01466/) (2022)
- [Age Aware Scheduling for Differentially-Private Federated Learning](https://mlsanity.com/paper/2405.05962/) (2024)

## Abstract (arXiv)

In many applications, especially due to lack of supervision or privacy concerns, the training data is grouped into bags of instances (feature-vectors) and for each bag we have only an aggregate label derived from the instance-labels in the bag. In learning from label proportions (LLP) the aggregate label is the average of the instance-labels in a bag, and a significant body of work has focused on training models in the LLP setting to predict instance-labels. In practice however, the training data may have fully supervised albeit covariate-shifted source data, along with the usual target data with bag-labels, and we wish to train a good instance-level predictor on the target domain. We call this the covariate-shifted hybrid LLP problem. Fully supervised covariate shifted data often has useful training signals and the goal is to leverage them for better predictive performance in the hybrid LLP setting. To achieve this, we develop methods for hybrid LLP which naturally incorporate the target bag-labels along with the source instance-labels, in the domain adaptation framework. Apart from proving theoretical guarantees bounding the target generalization error, we also conduct experiments on several publicly available datasets showing that our methods outperform LLP and domain adaptation baselines as well techniques from previous related work.