6 citations · 27 across the 25 of their papers we have counts for
4 papers · 1 filter
Teaching and Learning under Deductive Errors
Jan Arne Telle, Brigt Håvardstun, Jose Hernandez-Orallo
Most models of machine teaching and learning assume the learner makes no errors in its internal deductive inference. However, humans and large language models in few-shot learning…
Evaluating Simplification Algorithms for Interpretability of Time Series Classification
Brigt Håvardstun, Felix Marti-Perez, Cèsar Ferri +1
In this work, we introduce metrics to evaluate the use of simplified time series in the context of interpretability of a TSC -- a Time Series Classifier. Such simplifications are i…
When Redundancy Matters: Machine Teaching of Representations
Cèsar Ferri, Dario Garigliotti, Brigt Arve Toppe Håvardstun +2
In traditional machine teaching, a teacher wants to teach a concept to a learner, by means of a finite set of examples, the witness set. But concepts can have many equivalent repre…
MAP- and MLE-Based Teaching
Hans Ulrich Simon, Jan Arne Telle
Imagine a learner L who tries to infer a hidden concept from a collection of observations. Building on the work [4] of Ferri et al., we assume the learner to be parameterized by pr…