204 citations · 213 across the 25 of their papers we have counts for
6 papers · 1 filter
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…
SynIB: Informational Bottleneck for Maximizing Synergy in Multimodal Learning
Konstantinos Kontras, Teodora Gagaleska, Thomas Strypsteen +4
A central objective in multimodal learning is to capture synergy: task-relevant information that arises only from the joint use of multiple modalities, and is not available from an…
Bayesian Optimization over Bounded Domains with the Beta Product Kernel
Huy Hoang Nguyen, Han Zhou, Matthew B. Blaschko +1
Bayesian optimization with Gaussian processes (GP) is commonly used to optimize black-box functions. The Matérn and the Radial Basis Function (RBF) covariance functions are used fr…
Using Causality for Enhanced Prediction of Web Traffic Time Series
Chang Tian, Mingzhe Xing, Zenglin Shi +3
Predicting web service traffic has significant social value, as it can be applied to various practical scenarios, including but not limited to dynamic resource scaling, load balanc…
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…