Quantile Regression (QR) can be used to estimate aleatoric uncertainty i...
Despite recent advances in synthetic data generation, the scientific
com...
We revisit the problem of differentially private squared error linear
re...
We provide a differentially private algorithm for producing synthetic da...
Despite impressive state-of-the-art performance on a wide variety of mac...
We propose, implement, and evaluate a new algorithm for releasing answer...
Robustness of machine learning models is critical for security related
a...
Estimation of uncertainty in deep learning models is of vital importance...
We propose a robust variational autoencoder with β divergence for
tabula...
Dropout as a regularizer in deep neural networks has been less effective...
We consider online forecasting problems for non-convex machine learning
...
The availability and easy access to digital communication increase the r...
Here, we study different update rules in stochastic gradient descent (SG...
We consider an online learning process to forecast a sequence of outcome...
The use of complex models --with many parameters-- is challenging with
h...