XAI
Papers
On a Combinatorial Problem Arising in Machine Teaching (pdf)
B. Håvardstun, J. Kratochvíl, J. Sunde, J.A. Telle
Accepted in the International Conference on Machine Learning, 2024
Large Language Models for Question Answering over proprietary data (pdf)
D. Garigliotti, B. Johansen, J. Vigerust Kallestad, S. Cho, C. Ferri
Submitted, 2024
XAI for Time Series Classification: Evaluating the Benefits of Model Inspection for End-Users (pdf)
B. Håvardstun, C. Ferri, K. Flikka, J.A. Telle
Accepted in the World Conference on eXplainable Artificial Intelligence, 2024
MAP- and MLE-Based Teaching (pdf)
H. Simon, J.A.Telle
Accepted in the Journal of Machine Learning Research, 2024
When Redundancy Matters: Machine Teaching of Representations (pdf)
C. Ferri, D. Garigliotti, B. Håvardstun, J. Hernández-Orallo, J.A. Telle
arxiv preprint, 2024
A historical perspective of biomedical explainable AI research (pdf)
C. Ferri et al.
Patterns, 2023
XAI with Machine Teaching when Humans Are (Not) Informed about the Irrelevant Features (pdf)
B. Håvardstun, C. Ferri, J. Hernández-Orallo, P. Parviainen, J.A. Telle
European Conference on Machine Learning, ECML 2023
Heuristic search of optimal machine teaching curricula (pdf)
M. Garcia-Piqueras, J. Hernández-Orallo
Machine Learning, 1-32, 2023
Non-Cheating Teaching Revisited: A New Probabilistic Machine Teaching Model (pdf)
C. Ferri, J. Hernández-Orallo, J.A. Telle
International Joint Conference on Artificial Intelligence, IJCAI 2022
On the trade-off between fidelity and teaching complexity (pdf)
B. Håvardstun, C. Ferri, J. Hernández-Orallo, P. Parviainen, J.A. Telle
11th International Workshop on Approaches and Applications of Inductive Programming (AAIP), IJCLR 2022
Conditional teaching size (pdf)
M. Garcia-Piqueras, J. Hernández-Orallo
arXiv preprint, 2021
Teaching and explanations: aligning priors between machines and humans (link)
J. Hernández-Orallo, C. Ferri
Human-Like Machine Intelligence, 171-198, 2021
Optimal Teaching Curricula with Compositional Simplicity Priors (pdf)
M. Garcia-Piqueras, J. Hernández-Orallo
European Conference on Machine Learning, ECML 2021
Finite and Confident Teaching in Expectation: Sampling from Infinite Concept Classes (pdf)
J. Hernández-Orallo, J.A. Telle
European Conference on Artificial Intelligence, ECAI 2020
Teaching explanations by examples (pdf)
C. Ferri, J. Hernández-Orallo, J.A. Telle
MI21-HLC Machine Intelligence workshop on Human-Like Computing, 2019
The Teaching Size: Computable Teachers and Learners for Universal Languages (link)
J.A. Telle, J. Hernández-Orallo, C. Ferri
Machine Learning Journal, 2019
Finite biased teaching with infinite concept classes (pdf)
J. Hernández-Orallo, J.A. Telle
arXiv preprint, 2018
“Machine Teaching for Explainable AI” is a joint project between the Department of Informatics at the University of Bergen, the Valencian Research Institute for AI (VRAIN) and industrial partners Equinor and Eviny. The project is financed by the Norwegian Research Council.