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Found 10 papers, 4 papers with code
Estimating calibration error under label shift without labels
In the face of dataset shift, model calibration plays a pivotal role in ensuring the reliability of machine learning systems.
Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors
Proper scoring rules evaluate the quality of probabilistic predictions, playing an essential role in the pursuit of accurate and well-calibrated models.
Beyond Classification: Definition and Density-based Estimation of Calibration in Object Detection
Despite their impressive predictive performance in various computer vision tasks, deep neural networks (DNNs) tend to make overly confident predictions, which hinders their widespread use in safety-critical applications.
Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels
As a result, we obtain superior Dice scores and model calibration, which supports the wider adoption of DMLs in practice.
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.
On confidence intervals for precision matrices and the eigendecomposition of covariance matrices
Moreover, we derive a method to bound the entries of the inverse covariance matrix, the so-called precision matrix.
On the relationship between calibrated predictors and unbiased volume estimation
This has led to a renewed focus on calibrated predictions in the medical imaging and broader machine learning communities.
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.
Machine Guides, Human Supervises: Interactive Learning with Global Explanations
Compared to other explanatory interactive learning strategies, which are machine-initiated and rely on local explanations, XGL is designed to be robust against cases in which the explanations supplied by the machine oversell the classifier's quality.
Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning
This biases the "narrative" presented by the machine to the user. We address this narrative bias by introducing explanatory guided learning, a novel interactive learning strategy in which: i) the supervisor is in charge of choosing the query instances, while ii) the machine uses global explanations to illustrate its overall behavior and to guide the supervisor toward choosing challenging, informative instances.
