2 citations · 2 across the 3 of their papers we have counts for
5 papers
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…
Relative Drawing Identification Complexity is Invariant to Modality in Vision-Language Models
Diogo Freitas, Brigt Håvardstun, Cèsar Ferri +3
Large language models have become multimodal, and many of them are said to integrate their modalities using common representations. If this were true, a drawing of a car as an imag…
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…
On a Combinatorial Problem Arising in Machine Teaching
Brigt Håvardstun, Jan Kratochvíl, Joakim Sunde +1
We study a model of machine teaching where the teacher mapping is constructed from a size function on both concepts and examples. The main question in machine teaching is the minim…
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…