609 citations · 1.2k across the 42 of their papers we have counts for
46 papers
Inductive Conformal Prediction under Data Scarcity: Exploring the Impacts of Nonconformity Measures
Yuko Kato, David M. J. Tax, Marco Loog
Conformal prediction, which makes no distributional assumptions about the data, has emerged as a powerful and reliable approach to uncertainty quantification in practical applicati…
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance
Marco Loog, Tom Viering
Plotting a learner's generalization performance against the training set size results in a so-called learning curve. This tool, providing insight in the behavior of the learner, is…
A view on model misspecification in uncertainty quantification
Yuko Kato, David M. J. Tax, Marco Loog
Estimating uncertainty of machine learning models is essential to assess the quality of the predictions that these models provide. However, there are several factors that influence…
An Analysis of Model-Based Reinforcement Learning From Abstracted Observations
Rolf A. N. Starre, Marco Loog, Elena Congeduti +1
Many methods for Model-based Reinforcement learning (MBRL) in Markov decision processes (MDPs) provide guarantees for both the accuracy of the model they can deliver and the learni…
On the reusability of samples in active learning
Gijs van Tulder, Marco Loog
An interesting but not extensively studied question in active learning is that of sample reusability: to what extent can samples selected for one learner be reused by another? This…
Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts
Chirag Raman, Hayley Hung, Marco Loog
Forecasting tasks surrounding the dynamics of low-level human behavior are of significance to multiple research domains. In such settings, methods for explaining specific forecasts…