# Simplified and Unified Analysis of Various Learning Problems by Reduction to Multiple-Instance Learning

arXiv: https://arxiv.org/abs/1911.05999 · HTML: https://mlsanity.com/paper/1911.05999/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2019.

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

- [Bounding the Worst-class Error: A Boosting Approach](https://mlsanity.com/paper/2310.14890/) (2023)

## Abstract (arXiv)

In statistical learning, many problem formulations have been proposed so far, such as multi-class learning, complementarily labeled learning, multi-label learning, multi-task learning, which provide theoretical models for various real-world tasks. Although they have been extensively studied, the relationship among them has not been fully investigated. In this work, we focus on a particular problem formulation called Multiple-Instance Learning (MIL), and show that various learning problems including all the problems mentioned above with some of new problems can be reduced to MIL with theoretically guaranteed generalization bounds, where the reductions are established under a new reduction scheme we provide as a by-product. The results imply that the MIL-reduction gives a simplified and unified framework for designing and analyzing algorithms for various learning problems. Moreover, we show that the MIL-reduction framework can be kernelized.