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A. Masegosa

21 papers hereh-index 191.2k citations90 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author2
  • first author6
  • middle author11
  • last author2

Across the 21 of 21 papers where every author was matched, so the position is known.

fields
  • cs.LG16
  • stat.ML2
  • cs.AI1
  • cs.SE1
  • q-bio.BM1
same name
  • A. Masegosa — 3 papers, h 17

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20142026
most citedAMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning

17 citations · 78 across the 17 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.LG2026

Contrastive Linearized Laplace: Loss Geometry, Valid Repairs, and Reliable Cores

Francisco J. Sáez-Maldonado, Luis A. Ortega, Andrés R. Masegosa +1

While contrastive learning is widely used to learn deterministic representations, it does not inherently provide estimates of uncertainty. Post-hoc uncertainty estimation methods,…

cs.LG2026

Generalized Matheron Variational Implicit Processes

Luis A. Ortega, Andrés R. Masegosa, Thomas D. Nielsen

Implicit-process priors specify distributions over functions through sample-forward mechanisms such as Bayesian neural networks and stochastic simulators, but their function-space…

q-bio.BM2026

Sequence-Informed Geometric Evaluation of RNA 3D Structures

Andrea Zerio, Yighua Yao, Alessandro Micheli +5

Computational RNA structure pipelines generate many candidate conformations for the same sequence. Reliable evaluation therefore requires more than recognising plausible geometry,…

cs.LG2026

Flow-Transformed Implicit Processes for Function-Space Variational Inference

Luis A. Ortega, Andrés R. Masegosa, Thomas D. Nielsen

Implicit-process priors define distributions over functions through flexible generative mechanisms, making them attractive for Bayesian function-space modelling. However, performin…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.