# Learning to Link

arXiv: https://arxiv.org/abs/1907.00533 · HTML: https://mlsanity.com/paper/1907.00533/

## Key facts

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

## Built on by (most cited first, 14 of 14)

- [A Survey of Methods for Automated Algorithm Configuration](https://mlsanity.com/paper/2202.01651/) (2022)
- [Entity Linking and Discovery via Arborescence-based Supervised Clustering](https://mlsanity.com/paper/2109.01242/) (2021)
- [Triangle and Four Cycle Counting with Predictions in Graph Streams](https://mlsanity.com/paper/2203.09572/) (2022)
- [Learning-to-learn non-convex piecewise-Lipschitz functions](https://mlsanity.com/paper/2108.08770/) (2021)
- [Data driven semi-supervised learning](https://mlsanity.com/paper/2103.10547/) (2021)
- [Provably tuning the ElasticNet across instances](https://mlsanity.com/paper/2207.10199/) (2022)
- [Algorithm Configuration for Structured Pfaffian Settings](https://mlsanity.com/paper/2409.04367/) (2024)
- [Accelerating ERM for data-driven algorithm design using output-sensitive techniques](https://mlsanity.com/paper/2204.03569/) (2022)
- [Improved Generalization Bound and Learning of Sparsity Patterns for Data-Driven Low-Rank Approximation](https://mlsanity.com/paper/2209.08281/) (2022)
- [Learning accurate and interpretable tree-based models](https://mlsanity.com/paper/2405.15911/) (2024)
- [Accelerating data-driven algorithm selection for combinatorial partitioning problems](https://mlsanity.com/paper/2402.14332/) (2024)
- [Distribution-dependent Generalization Bounds for Tuning Linear Regression Across Tasks](https://mlsanity.com/paper/2507.05084/) (2025)
- [Tuning Algorithmic and Architectural Hyperparameters in Graph-Based Semi-Supervised Learning with Provable Guarantees](https://mlsanity.com/paper/2502.12937/) (2025)
- [Algorithmically Aligned Neural Agglomerative Tree Construction](https://mlsanity.com/paper/2610.07271/) (2026)

## Abstract (arXiv)

Clustering is an important part of many modern data analysis pipelines, including network analysis and data retrieval. There are many different clustering algorithms developed by various communities, and it is often not clear which algorithm will give the best performance on a specific clustering task. Similarly, we often have multiple ways to measure distances between data points, and the best clustering performance might require a non-trivial combination of those metrics. In this work, we study data-driven algorithm selection and metric learning for clustering problems, where the goal is to simultaneously learn the best algorithm and metric for a specific application. The family of clustering algorithms we consider is parameterized linkage based procedures that includes single and complete linkage. The family of distance functions we learn over are convex combinations of base distance functions. We design efficient learning algorithms which receive samples from an application-specific distribution over clustering instances and simultaneously learn both a near-optimal distance and clustering algorithm from these classes. We also carry out a comprehensive empirical evaluation of our techniques showing that they can lead to significantly improved clustering performance.