# Scalable Pairwise Kernel Learning with Stochastic Vec Trick

arXiv: https://arxiv.org/abs/2606.16979 · HTML: https://mlsanity.com/paper/2606.16979/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 15 Jun 2026.

## Builds on (most cited first, 11 of 11)

- [Kernel methods through the roof: handling billions of points efficiently](https://mlsanity.com/paper/2006.10350/) (2020)
- [FALKON: An Optimal Large Scale Kernel Method](https://mlsanity.com/paper/1705.10958/) (2017)
- [Scalable Kernel Methods via Doubly Stochastic Gradients](https://mlsanity.com/paper/1407.5599/) (2014)
- [Pairwise Learning for Neural Link Prediction](https://mlsanity.com/paper/2112.02936/) (2021)
- [Toward Large Kernel Models](https://mlsanity.com/paper/2302.02605/) (2023)
- [Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives](https://mlsanity.com/paper/2110.12141/) (2021)
- [Fast Kronecker product kernel methods via generalized vec trick](https://mlsanity.com/paper/1601.01507/) (2016)
- [Generalized vec trick for fast learning of pairwise kernel models](https://mlsanity.com/paper/2009.01054/) (2020)
- [Fast training of large kernel models with delayed projections](https://mlsanity.com/paper/2411.16658/) (2024)
- [Interaction Concordance Index: Performance Evaluation for Interaction Prediction Methods](https://mlsanity.com/paper/2510.14419/) (2025)
- [Inexact Limited Memory Bundle Method](https://mlsanity.com/paper/2604.08067/) (2026)

## Abstract (arXiv)

Pairwise learning is a specialized form of supervised learning that focuses on predicting outcomes for pairs of objects. In this work, we introduce SPaiK, a new scalable kernel learning method tailored for pairwise settings. Our approach preserves the expressive power of kernel methods while substantially reducing computational and memory requirements. The key innovation is the stochastic generalized vec trick (sGVT), a stochastic extension of the sparse Kronecker product multiplication algorithm, which enables efficient large-scale training with pairwise kernels. By incorporating sGVT, SPaiK makes it possible to apply kernel-based pairwise learning to datasets of a size previously out of reach. We evaluate the performance of SPaiK on seven real-world drug-target affinity datasets and compare the results with state-of-the-art methods in pairwise learning.