Simón Rodríguez Santana

13 papers A* 3Journal 10
YearRankTypeTitle / Venue / Authors
2026 J jnl
Scalable Linearized Laplace Approximation via Surrogate Neural Kernel.
CoRR
Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato
2025 J jnl
Personalized pricing decisions through adversarial risk analysis.
Int. Trans. Oper. Res.
Daniel Garcia Rasines, Roi Naveiro, David Ríos Insua, Simón Rodríguez Santana
2024 J jnl
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep Learning.
CoRR
Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato
2024 J jnl
Personalized Pricing Decisions Through Adversarial Risk Analysis.
CoRR
Daniel Garcia Rasines, Roi Naveiro, David Ríos Insua, Simón Rodríguez Santana
2024 A* conf
Variational Linearized Laplace Approximation for Bayesian Deep Learning.
ICML
Luis A. Ortega Andrés, Simón Rodríguez Santana, Daniel Hernández-Lobato
2023 A* conf
Deep Variational Implicit Processes.
ICLR
Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato
2023 J jnl
Variational Linearized Laplace Approximation for Bayesian Deep Learning.
CoRR
Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato
2022 J jnl
Adversarial α-divergence minimization for Bayesian approximate inference.
Neurocomputing
Simón Rodríguez Santana, Daniel Hernández-Lobato
2022 J jnl
Correcting Model Bias with Sparse Implicit Processes.
CoRR
Simón Rodríguez Santana, Luis A. Ortega Andrés, Daniel Hernández-Lobato, Bryan Zaldivar
2022 J jnl
Deep Variational Implicit Processes.
CoRR
Luis A. Ortega, Simón Rodríguez Santana, Daniel Hernández-Lobato
2022 A* conf
Function-space Inference with Sparse Implicit Processes.
ICML
Simón Rodríguez Santana, Bryan Zaldivar, Daniel Hernández-Lobato
2021 J jnl
Sparse Implicit Processes for Approximate Inference.
CoRR
Simón Rodríguez Santana, Bryan Zaldivar, Daniel Hernández-Lobato
2019 J jnl
Adversarial α-divergence Minimization for Bayesian Approximate Inference.
CoRR
Simón Rodríguez Santana, Daniel Hernández-Lobato