1 citations · 1 across the 4 of their papers we have counts for
5 papers
On the Tightness and Computational Tractability of Higher-Dimensional Confidence Sequences
Fabian Denoodt, Sibylle Hess, Joaquin Vanschoren +1
Modern sequential monitoring problems often involve multiple metrics, where we monitor several data streams simultaneously and may act once the evidence is strong enough. Confidenc…
How NOT to benchmark your SITE metric: Beyond Static Leaderboards and Towards Realistic Evaluation
Prabhant Singh, Sibylle Hess, Joaquin Vanschoren
Transferability estimation metrics are used to find a high-performing pre-trained model for a given target task without fine-tuning models and without access to the source dataset.…
A Spatially-Aware Multiple Instance Learning Framework for Digital Pathology
Hassan Keshvarikhojasteh, Mihail Tifrea, Sibylle Hess +2
Multiple instance learning (MIL) is a promising approach for weakly supervised classification in pathology using whole slide images (WSIs). However, conventional MIL methods such a…
Occam's model: Selecting simpler representations for better transferability estimation
Prabhant Singh, Sibylle Hess, Joaquin Vanschoren
Fine-tuning models that have been pre-trained on large datasets has become a cornerstone of modern machine learning workflows. With the widespread availability of online model repo…
Scoring rule nets: beyond mean target prediction in multivariate regression
Daan Roordink, Sibylle Hess
Probabilistic regression models trained with maximum likelihood estimation (MLE), can sometimes overestimate variance to an unacceptable degree. This is mostly problematic in the m…