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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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portfolio
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publications
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. (2024+). "Deep Kernel Posterior Learning under Infinite Variance Prior Weights ." (submitted). https://arxiv.org/abs/2410.01284
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 ." (to appear) 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
talks
teaching
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 2019a,Fall 2022t)
- STAT 301: Elements of Statistics (Fall 2019a, Spring 2020a, Fall 2020a, Spring 2021a, Fall 2021t, Spring 2022t, Summer 2022t)