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Omar Salemohamed

Doctorat - UdeM
Superviseur⋅e principal⋅e
Laurent Charlin
Sujets de recherche
Apprentissage profond

Publications

Discovering Data Structures: Nearest Neighbor Search and Beyond
Omar Salemohamed
Laurent Charlin
Shivam Garg
Vatsal Sharan
Gregory Valiant
We propose a general framework for end-to-end learning of data structures. Our framework adapts to the underlying data distribution and prov… (voir plus)ides fine-grained control over query and space complexity. Crucially, the data structure is learned from scratch, and does not require careful initialization or seeding with candidate data structures/algorithms. We first apply this framework to the problem of nearest neighbor search. In several settings, we are able to reverse-engineer the learned data structures and query algorithms. For 1D nearest neighbor search, the model discovers optimal distribution (in)dependent algorithms such as binary search and variants of interpolation search. In higher dimensions, the model learns solutions that resemble k-d trees in some regimes, while in others, they have elements of locality-sensitive hashing. The model can also learn useful representations of high-dimensional data and exploit them to design effective data structures. We also adapt our framework to the problem of estimating frequencies over a data stream, and believe it could also be a powerful discovery tool for new problems.
2025-09-17
NeurIPS.cc/2025/Conference (poster)
doi.org
openreview.net
Learning to Design Data-structures: A Case Study of Nearest Neighbor Search
Omar Salemohamed
Vatsal Sharan
Shivam Garg
Laurent Charlin
Gregory Valiant
We propose a general framework for automating data-structure design and apply it to the problem of nearest neighbor search. Our model adapts… (voir plus) to the underlying data distribution and provides fine-grained control over query and space complexity, enabling the discovery of solutions tailored to problem-specific constraints. We are able to reverse-engineer learned algorithms in several settings. In 1D, the model discovers optimal distribution (in)dependent algorithms such as binary search and variants of interpolation search. In higher dimensions, the model learns solutions that resemble K-d trees in some regimes, while in others, have elements of locality-sensitive hashing.
2024-06-26
ICML.cc/2024/Workshop/Differentiable_Almost_Everything (publié)
openreview.net
openreview.net