# PAIR-Former: Budgeted Relational Multi-Instance Learning for Functional miRNA Target Prediction

arXiv: https://arxiv.org/abs/2602.00465 · HTML: https://mlsanity.com/paper/2602.00465/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 31 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Spectrally-normalized margin bounds for neural networks](https://mlsanity.com/paper/1706.08498/) (2017)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification](https://mlsanity.com/paper/2203.12081/) (2022)
- [Multiple Instance Learning: A Survey of Problem Characteristics and Applications](https://mlsanity.com/paper/1612.03365/) (2016)
- [Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images](https://mlsanity.com/paper/2204.12454/) (2022)
- [Sm: enhanced localization in Multiple Instance Learning for medical imaging classification](https://mlsanity.com/paper/2410.03276/) (2024)
- [TargetNet: Functional microRNA Target Prediction with Deep Neural Networks](https://mlsanity.com/paper/2107.11381/) (2021)

## Abstract (arXiv)

Functional miRNA--mRNA targeting is a large-bag prediction problem where each transcript yields a heavy-tailed pool of candidate target sites (CTSs), yet only a pair-level label is observed. Prior methods use max-pooling over individual CTS scores, ignoring relational patterns among sites, but modeling these patterns is critical for accuracy. The challenge is that naive relational aggregation incurs O(n2) cost, prohibitive when n reaches thousands, yet a cheap scan alone discards the very interactions that drive functional repression. We formalize this tension as Budgeted Relational Multi-Instance Learning (BR-MIL), a new MIL problem where the compute budget K is a first-class constraint such that at most K instances per bag may receive expensive encoding and relational processing. We establish theoretical foundations for BR-MIL, proving that both approximation quality and generalization are governed by K rather than the raw bag size n. Building on this theory, we propose PAIR-Former, which scans all candidates cheaply, selects K diverse CTSs, and aggregates them via Set Transformer. PAIR-Former achieves state-of-the-art performance, outperforming all reproduced baselines with F1=0.840 on miRAW (10-fold balanced CV) and 0.839 on deepTargetPro in transfer evaluation, while achieving 0.793 on the large-scale MTI benchmark (420K pairs, 38× larger), demonstrating that budgeted relational MIL scales where naive approaches fail. Additional results on CAMELYON16 and Musk2 further show that the proposed BR-MIL formulation extends beyond biological sequence modeling.