1 citations · 2 across the 6 of their papers we have counts for
8 papers
Algebraic Machine Learning for Small-to-Medium Datasets Is Competitive against Strong Standard Baselines
David Mendez, Fernando Martin-Maroto, Gonzalo G. de Polavieja
Symbolic methods are generally not considered competitive with strong modern learners on realistic supervised tasks. We evaluate Algebraic Machine Learning (AML), a framework that…
Beyond ECE: Calibrated Size Ratio, Risk Assessment, and Confidence-Weighted Metrics
Fernando Martin-Maroto, Nabil Abderrahaman, Gonzalo G. de Polavieja
Confidence calibration has been dominated by the Expected Calibration Error (ECE), a linear metric that counts calibration offset equally regardless of the confidence level at whic…
The pairwise distributive law of semilattice congruences
Fernando Martin-Maroto, Antonio Ricciardo, Gonzalo G. de Polavieja
We show that the congruence lattice of a semilattice satsifies a form of distributivity relative to principal congruences of the form . Particularly, we establis…
Algebraic Machine Learning: Learning as computing an algebraic decomposition of a task
Fernando Martin-Maroto, Nabil Abderrahaman, David Mendez +1
Statistics and Optimization are foundational to modern Machine Learning. Here, we propose an alternative foundation based on Abstract Algebra, with mathematics that facilitates the…
Infinite Atomized Semilattices
Fernando Martin-Maroto, Antonio Ricciardo, David Mendez +1
We extend the theory of atomized semilattices to the infinite setting. We show that it is well-defined and that every semilattice is atomizable. We also study atom redundancy, focu…
Semantic Embeddings in Semilattices
Fernando Martin-Maroto, Gonzalo G. de Polavieja
To represent anything from mathematical concepts to real-world objects, we have to resort to an encoding. Encodings, such as written language, usually assume a decoder that underst…