6 citations · 27 across the 25 of their papers we have counts for
31 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…
The Hierarchy of Saturating Matching Numbers
Hans U. Simon, Jan Arne Telle
In this paper, we study three matching problems all of which came up quite recently in the field of machine teaching. The cost of a matching is defined in such a way that, for some…
Can adversarial attacks by large language models be attributed?
Manuel Cebrian, Andres Abeliuk, Jan Arne Telle
Attributing outputs from Large Language Models (LLMs) in adversarial settings-such as cyberattacks and disinformation campaigns-presents significant challenges that are likely to g…
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…