Andres Potapczynski
- PhD in Data Science, NYU
- MSc in Data Science, Columbia
- BSc in Applied Mathematics, ITAM
- BA in Economics, ITAM
Research & Projects
Low-Precision Arithmetic for Fast Gaussian Processes
We study the different failure modes that can occur
when training GPs in half precision. To circumvent these failure
modes, we propose a multi-faceted approach involving conjugate
gradients with re-orthogonalization, mixed precision, and
preconditioning -- 38th Conference on Uncertainty in Artificial
Intelligence (UAI 2022).
[PDF]
[CODE]
Bias-Free Scalable Gaussian Processes via
Randomized Truncations
Studies the biases introduced by approximate methods
and eliminates them via randomized truncation estimators
-- 38th International Conference on Machine Learning (ICML 2021).
[PDF]
[CODE]
Invertible Gaussian Reparameterization: Revisiting the
Gumbel-Softmax
Introduces a family of continuous relaxations
that is more flexible, extensible and better performing than the
Gumbel-Softmax -- 34th Conference on Neural Information
Processing Systems (NeurIPS 2020).
[PDF]
[CODE]
Nowcasting with Google Trends
Proposes an alternative kernel bandwidth selection
algorithm and exhibits what Google searches are relevant for
predicting unemployment, influenza outbreaks and violence spikes in Mexico.
The content is in English (past the acknowledgments) and relevant pages are:
4, 26, 36, 43, 48 -- Undergraduate Thesis.
[PDF]
[CODE]
Classifying webpages based on their menu
By modifying Word2Vec we recover an embedding that
helps to cluster clients based on their webpage's menu content -- Capstone Project.
[PDF]