Search Results for author:
Found 2 papers, 1 papers with code
A Consistent and Differentiable Lp Canonical Calibration Error Estimator
As a remedy, we propose a low-bias, trainable calibration error estimator based on Dirichlet kernel density estimates, which asymptotically converges to the true $L_p$ calibration error.
Calibration Regularized Training of Deep Neural Networks using Kernel Density Estimation
The computational complexity of our estimator is O(n^2), matching that of the kernel maximum mean discrepancy, used in a previously considered trainable calibration estimator.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
