# Hashing as Tie-Aware Learning to Rank

arXiv: https://arxiv.org/abs/1705.08562 · HTML: https://mlsanity.com/paper/1705.08562/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2017.

## Built on by (most cited first, 18 of 18)

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- [TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval](https://mlsanity.com/paper/2105.01823/) (2021)
- [Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability](https://mlsanity.com/paper/2209.13262/) (2022)
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- [DeepLSH: Deep Locality-Sensitive Hash Learning for Fast and Efficient Near-Duplicate Crash Report Detection](https://mlsanity.com/paper/2310.06703/) (2023)
- [Unlocking the Potential: Multi-task Deep Learning for Spaceborne Quantitative Monitoring of Fugitive Methane Plumes](https://mlsanity.com/paper/2401.12870/) (2024)
- [Optimizing Area Under the Curve Measures via Matrix Factorization for Predicting Drug-Target Interaction with Multiple Similarities](https://mlsanity.com/paper/2105.01545/) (2021)
- [Ranking Regularization for Critical Rare Classes: Minimizing False Positives at a High True Positive Rate](https://mlsanity.com/paper/2304.00049/) (2023)
- [Pointing the Way, Hiding the Destination: Practical Private Dense Retrieval at Scale](https://mlsanity.com/paper/2608.25735/) (2026)
- [Unsupervised Embedding Learning from Uncertainty Momentum Modeling](https://mlsanity.com/paper/2107.08892/) (2021)
- [DSCH-Loss: A Dynamic Semantic Channel Objective for Deep Semantic Hashing](https://mlsanity.com/paper/2607.24567/) (2026)

## Abstract (arXiv)

Hashing, or learning binary embeddings of data, is frequently used in nearest neighbor retrieval. In this paper, we develop learning to rank formulations for hashing, aimed at directly optimizing ranking-based evaluation metrics such as Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We first observe that the integer-valued Hamming distance often leads to tied rankings, and propose to use tie-aware versions of AP and NDCG to evaluate hashing for retrieval. Then, to optimize tie-aware ranking metrics, we derive their continuous relaxations, and perform gradient-based optimization with deep neural networks. Our results establish the new state-of-the-art for image retrieval by Hamming ranking in common benchmarks.