Post-hoc Bayesian inference
Bayesian uncertainty estimation for pre-trained neural networks through linearized Laplace, neural kernels, and scalable approximations.
Academic Profile
Luis A. Ortega
Postdoctoral Researcher · Aalborg University
Probabilistic Machine Learning for uncertainty, inference, and generalization.
Function-space variational inference, linearized Laplace approximations, deep ensembles, and Chernoff-style generalization bounds.
Bayesian deep learning
Variational inference
Generalization bounds
Deep ensembles
10
Publications
5
Ongoing projects
4
Research lines
Current Position
Postdoctoral research at Aalborg University.
I am a Postdoctoral Researcher in the Department of Computer Science at Aalborg University, working in the Section for Distributed, Embedded and Intelligent Systems. My work focuses on making modern neural networks more reliable by combining probabilistic inference, uncertainty quantification, and generalization theory.
Research Scope
Function-space inference
Gaussian and implicit-process models that place uncertainty directly over functions, including variational and flow-transformed constructions.
Generalization theory
PAC-Chernoff and large-deviation analyses of interpolation, regularization, and the implicit bias of stochastic optimization.
Ensembles and calibration
Theoretical and empirical study of diversity, model combination, and calibrated prediction in neural network ensembles.
Selected Work
AISTATS 2022
Diversity and Generalization in Neural Network Ensembles
Connects ensemble diversity, generalization error, and common model-combination strategies.ICML 2024
Variational Linearized Laplace Approximation for Bayesian Deep Learning
Approximates LLA via sparse variational Gaussian processes with sub-linear training costs.NeurIPS 2024