3 papers
eess.SY2026
Conformal Prediction Regions for Continuous-Time Trajectories under Random Sampling
Joaquin Alvarez, Matteo Sesia, Jyotirmoy V. Deshmukh +1
Uncertainty quantification for continuous-time trajectories is a prerequisite in many safety-critical engineering domains. However, a major challenge in data-driven uncertainty qua…
cs.LG2024
Confident magnitude-based neural network pruning
Joaquin Alvarez
Pruning neural networks has proven to be a successful approach to increase the efficiency and reduce the memory storage of deep learning models without compromising performance. Pr…
stat.ML2024
A distribution-free valid p-value for finite samples of bounded random variables
Joaquin Alvarez
We build a valid p-value based on a concentration inequality for bounded random variables introduced by Pelekis, Ramon and Wang. The motivation behind this work is the calibration…