Lenses and Learners
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Abstract (arXiv)
Lenses are a well-established structure for modelling bidirectional transformations, such as the interactions between a database and a view of it. Lenses may be symmetric or asymmetric, and may be composed, forming the morphisms of a monoidal category. More recently, the notion of a learner has been proposed: these provide a compositional way of modelling supervised learning algorithms, and again form the morphisms of a monoidal category. In this paper, we show that the two concepts are tightly linked. We show both that there is a faithful, identity-on-objects symmetric monoidal functor embedding a category of asymmetric lenses into the category of learners, and furthermore there is such a functor embedding the category of learners into a category of symmetric lenses.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Mar 2019.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 1; 2024: 3; 2025: 2; 2026: 1.
11 papers.
- 2021Categorical Foundations of Gradient-Based Learningcited by 44
- 2021Category Theory in Machine Learningcited by 36
- 2024Pooling Image Datasets With Multiple Covariate Shift and Imbalancecited by 3
- 2024Deep Learning with Parametric Lensescited by 3
- 2022Space-time tradeoffs of lenses and optics via higher category theorycited by 2
- 2024Towards a Categorical Foundation of Deep Learning: A Surveycited by 2
- 2022A Probabilistic Generative Model of Free Categoriescited by 0
- 2023Algebraic Dynamical Systems in Machine Learningcited by 0
- 2025Higher Gauge Flow Modelscited by 0
- 2025Disentangling Hyperedges through the Lens of Category Theorycited by 0
- 2026Modeling Topological Impact on Node Attribute Distributions in Attributed Graphscited by 0
- 2026Modeling Topological Impact on Node Attribute Distributions in Attributed Graphs1 Feb 2026
- 2025Disentangling Hyperedges through the Lens of Category Theory18 Oct 2025
- 2025Higher Gauge Flow Models22 Jul 2025
- 2024Towards a Categorical Foundation of Deep Learning: A Survey7 Oct 2024
- 2024Deep Learning with Parametric Lenses30 Mar 2024
- 2024Pooling Image Datasets With Multiple Covariate Shift and Imbalance5 Mar 2024
- 2023Algebraic Dynamical Systems in Machine Learning6 Nov 2023
- 2022Space-time tradeoffs of lenses and optics via higher category theory19 Sep 2022
- 2022A Probabilistic Generative Model of Free Categories9 May 2022
- 2021Category Theory in Machine Learning13 Jun 2021
- 2021Categorical Foundations of Gradient-Based Learning2 Mar 2021