Decision makers often care about the causal effect to select the top customers in a specific treatment, we propose a framework on how to do this even when the data does not lend itself to accurately estimate causal effects.
Recommended citation: Fernández-Loría, C, Loría J. (2024+). " Inferring Effect Ordering Without Causal Effect Estimation ." submitted. https://arxiv.org/pdf/2206.12532.pdf
We propose a method to efficiently predict on neural networks that are infinitely wide, which take the form of deep kernel processes, using priors that have unbounded variance.
Recommended citation: Loría, J, Bhadra, A. (2025). "Deep Kernel Posterior Learning under Infinite Variance Prior Weights ." (The 13th International Conference on Learning Representations (ICLR 2025)). https://openreview.net/forum?id=usFdPd4Ghs
We propose a method to do posterior inference on neural networks that are infinitely wide, using priors that have unbounded variance.
Recommended citation: Loría, J, Bhadra, A. (2024). "Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance ." 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024). https://openreview.net/forum?id=J97bdMR7Lv
Bridge is a regularization technique that can consume a lot of time when the number of covariates is much larger than the number of observations. We propose a non-iterative method to reduce this timing without losing statistical prediction power.
Recommended citation: Loria, J, Bhadra, A. (2024). "SURE-tuned Bridge Regression." Statistics and Computing 34, 30. https://link.springer.com/article/10.1007/s11222-023-10350-z
We provide a meaningful way to incorporate domain knowledge into causal discovery, in the specially hard case of mixtures of graphs. Incorporating the expert knowledge allows inferring correct mixture components, without having observed them.
Recommended citation: Björkman, Z., Loría, J., Wharrie, S., Kaski, S. (2025+). " Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphs." submitted. https://arxiv.org/pdf/2510.06735.pdf
We propose a new model for understanding and prediction of demographic modeling, using Markov Chains. We apply it to an institution in Costa Rica.
Recommended citation: Víquez, J. J., Víquez, J. A., Campos, A., Loría, J., & Mendoza, L. A. (2018). Modelación de poblaciones vía cadenas de Markov tridimensionales. Revista De Matemática: Teoría Y Aplicaciones, 25(2), 185–214. https://doi.org/10.15517/rmta.v25i2.33608. https://revistas.ucr.ac.cr/index.php/matematica/article/view/33608/34172