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

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)