ENBIS-24 Leuven Conference

Session

Regression

17 Sept 2024, 10:05

Leuven, Belgium

Conveners

Regression

• Amandine PIERROT (University of Bath)

43. Which query strategy should one choose in active learning of real-valued functions when the model class is possibly misspecified?

Bernhard Spangl (University of Natural Resources and Life Sciences, Vienna)

17/09/2024, 10:05

Machine Learning

We discuss the problem of active learning in regression scenarios. In active learning, the goal is to provide criteria that the learning algorithm can employ to improve its performance by actively selecting data that are most informative.

Active learning is usually thought of as being a sequential process where the training set is augmented one data point at a time. Additionally, it is...

18. Assessing general process stability with local regression

Mr Karel Kupka (TriloByte Statistical Software)

17/09/2024, 10:25

Process modelling and Control

Process stability is usually defined using iid assumption about data. However violating stability requires some concrete model like changepoint, linear trend, outliers, distributional models, positive or negative autocorrelation, etc. These violations are often tested separately and not all of the possible modes of instability can always be taken into account. We suggested a likelihood-based...

45. On the equivalence between null space and orthogonal space in latent variable modeling

Sergio García Carrión (Universitat Politècnica de València (UPV))

17/09/2024, 10:45

Process modelling and Control

The concepts of null space (NS) and orthogonal space (OS) have been developed in independent contexts and with different purposes.
The former arises in the inversion of Partial Least Squares (PLS) regression models, as first proposed by Jaeckle & MacGregor [1], and represents a subspace in the latent space within which variations in the inputs do not affect the prediction of the outputs. The...