# Transductive Optimization of Top k Precision

arXiv: https://arxiv.org/abs/1510.05976 · HTML: https://mlsanity.com/paper/1510.05976/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2015.

## Built on by (most cited first, 3 of 3)

- [An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings](https://mlsanity.com/paper/2105.06442/) (2021)
- [A Recommender System for Scientific Datasets and Analysis Pipelines](https://mlsanity.com/paper/2108.09275/) (2021)
- [When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-k Multi-Label Learning](https://mlsanity.com/paper/2309.00007/) (2023)

## Abstract (arXiv)

Consider a binary classification problem in which the learner is given a labeled training set, an unlabeled test set, and is restricted to choosing exactly k test points to output as positive predictions. Problems of this kind---it transductive precision@k---arise in information retrieval, digital advertising, and reserve design for endangered species. Previous methods separate the training of the model from its use in scoring the test points. This paper introduces a new approach, Transductive Top K (TTK), that seeks to minimize the hinge loss over all training instances under the constraint that exactly k test instances are predicted as positive. The paper presents two optimization methods for this challenging problem. Experiments and analysis confirm the importance of incorporating the knowledge of k into the learning process. Experimental evaluations of the TTK approach show that the performance of TTK matches or exceeds existing state-of-the-art methods on 7 UCI datasets and 3 reserve design problem instances.