9 citations · 19 across the 14 of their papers we have counts for
15 papers
Bandwidth Selection in Kernel Density Estimation for Model Calibration
Han Zhou, Teodora Popordanoska, Matthew Blaschko
As deep learning models are increasingly deployed in high-stakes applications, providing well-calibrated uncertainty estimates has become as critical as achieving high predictive a…
CLASH: A Benchmark for Cross-Modal Contradiction Detection
Teodora Popordanoska, Jiameng Li, Matthew B. Blaschko
Contradictory multimodal inputs are common in real-world settings, yet existing benchmarks typically assume input consistency and fail to evaluate cross-modal contradiction detecti…
Revisiting Reweighted Risk for Calibration: AURC, Focal, and Inverse Focal Loss
Han Zhou, Sebastian G. Gruber, Teodora Popordanoska +1
Several variants of reweighted risk functionals, such as focal loss, inverse focal loss, and the Area Under the Risk Coverage Curve (AURC), have been proposed for improving model c…
CARE: Confidence-aware Ratio Estimation for Medical Biomarkers
Jiameng Li, Teodora Popordanoska, Aleksei Tiulpin +3
Ratio-based biomarkers (RBBs), such as the proportion of necrotic tissue within a tumor, are widely used in clinical practice to support diagnosis, prognosis, and treatment plannin…
DAVE: Diagnostic benchmark for Audio Visual Evaluation
Gorjan Radevski, Teodora Popordanoska, Matthew B. Blaschko +1
Audio-visual understanding is a rapidly evolving field that seeks to integrate and interpret information from both auditory and visual modalities. Despite recent advances in multi-…
A Novel Characterization of the Population Area Under the Risk Coverage Curve (AURC) and Rates of Finite Sample Estimators
Han Zhou, Jordy Van Landeghem, Teodora Popordanoska +1
The selective classifier (SC) has been proposed for rank based uncertainty thresholding, which could have applications in safety critical areas such as medical diagnostics, autonom…
Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors
Teodora Popordanoska, Sebastian G. Gruber, Aleksei Tiulpin +2
Proper scoring rules evaluate the quality of probabilistic predictions, playing an essential role in the pursuit of accurate and well-calibrated models. Every proper score decompos…
Estimating calibration error under label shift without labels
Teodora Popordanoska, Gorjan Radevski, Tinne Tuytelaars +1
In the face of dataset shift, model calibration plays a pivotal role in ensuring the reliability of machine learning systems. Calibration error (CE) is an indicator of the alignmen…
Beyond Classification: Definition and Density-based Estimation of Calibration in Object Detection
Teodora Popordanoska, Aleksei Tiulpin, Matthew B. Blaschko
Despite their impressive predictive performance in various computer vision tasks, deep neural networks (DNNs) tend to make overly confident predictions, which hinders their widespr…
Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels
Zifu Wang, Teodora Popordanoska, Jeroen Bertels +2
The soft Dice loss (SDL) has taken a pivotal role in numerous automated segmentation pipelines in the medical imaging community. Over the last years, some reasons behind its superi…
A Consistent and Differentiable Lp Canonical Calibration Error Estimator
Teodora Popordanoska, Raphael Sayer, Matthew B. Blaschko
Calibrated probabilistic classifiers are models whose predicted probabilities can directly be interpreted as uncertainty estimates. It has been shown recently that deep neural netw…
On confidence intervals for precision matrices and the eigendecomposition of covariance matrices
Teodora Popordanoska, Aleksei Tiulpin, Wacha Bounliphone +1
The eigendecomposition of a matrix is the central procedure in probabilistic models based on matrix factorization, for instance principal component analysis and topic models. Quant…
On the relationship between calibrated predictors and unbiased volume estimation
Teodora Popordanoska, Jeroen Bertels, Dirk Vandermeulen +2
Machine learning driven medical image segmentation has become standard in medical image analysis. However, deep learning models are prone to overconfident predictions. This has led…
Machine Guides, Human Supervises: Interactive Learning with Global Explanations
Teodora Popordanoska, Mohit Kumar, Stefano Teso
We introduce explanatory guided learning (XGL), a novel interactive learning strategy in which a machine guides a human supervisor toward selecting informative examples for a class…
Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning
Teodora Popordanoska, Mohit Kumar, Stefano Teso
Recent work has demonstrated the promise of combining local explanations with active learning for understanding and supervising black-box models. Here we show that, under specific…