Posts by Collection

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Inferring Effect Ordering Without Causal Effect Estimation

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

Deep Kernel Posterior Learning under Infinite Variance Prior Weights

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

Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance

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

SURE-tuned Bridge Regression

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

Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphs

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

Demographic Modeling via 3-dimensional Markov Chains

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

Teaching at Universidad de Costa Rica

• CA 202: Herramientas de Cómputo para el Cálculo Actuarial (Fall 2017 & Fall 2018) [slides, code, etc]
  

Teaching at Purdue University

• STAT 350: Introduction to Statistics (Fall 2019^a,Fall 2022^t)
• STAT 301: Elements of Statistics (Fall 2019^a, Spring 2020^a, Fall 2020^a, Spring 2021^a, Fall 2021^t, Spring 2022^t, Summer 2022^t)