Research map / Learning theory and algorithms / Prediction and decision losses
Locality-sensitive hashing
Hash data to binary codes or buckets for sublinear similarity search.
30 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2025) · an approach in Prediction and decision losses, keyword cluster “ranking · nearest · neighbor”
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Where it stands
Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.
4 papers in 2023–24 and 1 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 0.7% to 0.2%.
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2016 · Towards Optimal Binary Code Learning via Ordinal Embedding (AAAI 2016 · 45 citations)
- 2017 · Optimal Densification for Fast and Accurate Minwise Hashing (ICML 2017 · 35 citations)
- 2018 · Asymmetric Deep Supervised Hashing (AAAI 2018 · 275 citations)
- 2019 · Towards Optimal Discrete Online Hashing with Balanced Similarity (AAAI 2019 · 60 citations)
- 2020 · Bio-Inspired Hashing for Unsupervised Similarity Search (ICML 2020 · 7 citations)
- 2021 · Practical Near Neighbor Search via Group Testing (NeurIPS 2021 · 1 citation)
- 2022 · One Loss for Quantization: Deep Hashing With Discrete Wasserstein Distributional Matching (CVPR 2022 · 50 citations)
- 2023 · Deep Hashing With Minimal-Distance-Separated Hash Centers (CVPR 2023 · 68 citations)
- 2025 · Pacmann: Efficient Private Approximate Nearest Neighbor Search (ICLR 2025 · 0 citations)
Earliest papers here
- Towards Optimal Binary Code Learning via Ordinal Embedding (AAAI 2016 · 45 citations)
- Multilinear Hyperplane Hashing (CVPR 2016 · 41 citations)
- An ensemble diversity approach to supervised binary hashing (NeurIPS 2016 · 9 citations)
What has been tried
28 papers here report 248 measured changes (a component added, removed or swapped, compared with a baseline): helped 76%, no clear effect 7%, hurt 12%, mixed 4%. Papers mostly publish changes that helped, so "helped" is over-represented.
Recent preprints
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Use it
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Related approaches
Same keyword cluster
- Ranking models from comparisons (127 papers)
- Approximate nearest neighbor indexing (58 papers)
- Metric and ordinal embedding (47 papers)
Elsewhere in the topic
- Adaptive testing and experiments (201 papers)
- Active learning and subset selection (199 papers)
- Distributionally robust optimization (184 papers)
- Data attribution and recourse (160 papers)
- Theory of neural network sensitivity (143 papers)
- Determinantal point process inference (122 papers)
- Conformal prediction sets (116 papers)
- Provable post-hoc explanations (92 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).