Andres Potapczynski
- PhD in Data Science, NYU
- MSc in Data Science, Columbia University
- BSc in Applied Mathematics, ITAM
- BA in Economics, ITAM
Research & Projects
A Stable and Scalable Method for Solving Initial
Value PDEs with Neural Networks
We propose Neural-IVP, a method for approximating
solutions to high-dimensional PDEs though neural networks.
Our method is scalable, well-conditioned and runs in time linear
to the number of parameters in the neural network.
Topics: Inductive Biases, Partial Differential Equations, Numerical Linear Algebra.
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Topics: Inductive Biases, Partial Differential Equations, Numerical Linear Algebra.
PAC-Bayes Compression Bounds So Tight That They
Can Explain Generalization
We develop a compression approach based on quantizing neural
network parameters in a random linear subspace profoundly
improving previous state-of-the-art generalization bounds and
showing how these tight bounds can help us understand the role of
model size, equivariance, and implicit biases in optimization.
-- 36th Conference on Neural Information Processing Systems (NeurIPS
2022).
Topics: Random Subspaces, Quantization, Equivariance, PAC-Bayes bounds.
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Topics: Random Subspaces, Quantization, Equivariance, PAC-Bayes bounds.
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).
Topics: Gaussian Processes, Quantization, Numerical Linear Algebra.
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Topics: Gaussian Processes, Quantization, Numerical Linear Algebra.
Bias-Free Scalable Gaussian Processes via
Randomized Truncations
We identify the biases introduced by approximate methods
and eliminate them via randomized truncation estimators
-- 38th International Conference on Machine Learning (ICML
2021).
Topics: Gaussian Processes, Russian-Roulette estimators, Kernel Approximations, Numerical Linear Algebra.
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Topics: Gaussian Processes, Russian-Roulette estimators, Kernel Approximations, Numerical Linear Algebra.
Invertible Gaussian Reparameterization: Revisiting the
Gumbel-Softmax
We introduce 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).
Topics: Generative modeling, VAEs, Normalizing Flows, Continuous Relaxations.
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Topics: Generative modeling, VAEs, Normalizing Flows, Continuous Relaxations.
Nowcasting with Google Trends
I propose 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.
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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.
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