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cs.LG2025
Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression
Megh Shukla, Aziz Shameem, Mathieu Salzmann +1
Deep heteroscedastic regression models the mean and covariance of the target distribution through neural networks. The challenge arises from heteroscedasticity, which implies that…
cs.LG2023
TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression
Megh Shukla, Mathieu Salzmann, Alexandre Alahi
Deep heteroscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. However, recent works show that…