Publications – Alejandro Kuratomi

2025

Kuratomi, A., Lee, Z., Tsaparas, P., Dinis, J. G., Pitoura E., Lindgren, T., and Papapetrou, P. Counterfair: Group counterfactuals for bias detection, mitigation and subgroup identification. In IEEE International Conference on Data Mining (ICDM). IEEE, 2024.

Kuratomi, A., Miliou, I., Lee, Z., Lindgren, T., & Papapetrou, P. Ijuice: integer JUstIfied counterfactual explanations. Machine Learning, 1-41. Springer, 2024.
Kuratomi, A., Lee, Z., Dinis, J. G., Lindgren, T., and Garcia, D. CRITS: Convolutional rectifier for interpretable time series classification. In Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track: European Conference, ECML PKDD 2024. Cham: Springer International Publishing.
Lesley, U. and Kuratomi, A. Improving Xai Explanations for Clinical Decision-making– Physicians’ Perspective on Local Explanations in Healthcare. In International Conference on Artificial Intelligence in Medicine (AIME), pages 296–312. Springer, 2024.

Kuratomi, A., Lee, Z., Miliou, I., Lindgren, T., and Papapetrou, P. Orange: Opposite-label sorting for tangent explanations in heterogeneous spaces. In IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA). Springer, 2023.

Kuratomi, A., Miliou, I., Lee, Z., Lindgren, T., and Papapetrou, P. JUICE: JUstIfied Counterfactual Explanations. In Discovery Science: 25th International Conference, DS 2022, Montpellier, France, October 10–12, 2022, Proceedings (pp. 493-508). Cham: Springer Nature Switzerland.
Kuratomi, A., Pitoura, E., Papapetrou, P., Lindgren, T., Tsaparas, P., Measuring the burden of (un) fairness using counterfactuals. In Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track: European Conference, ECML PKDD 2022. Cham: Springer International Publishing.

Kuratomi, A., Lindgren, T., and Papapetrou, P. Prediction of Global Navigation Satellite System Positioning Errors with Guarantees. In Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track: European Conference, ECML PKDD 2020. Cham: Springer International Publishing.

Kuratomi, A. GNSS Position Error Estimated by Machine Learning Techniques with Environmental Information Input. In Master Thesis in Engineering Design: Mechatronics, KTH. Retrieved from https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-262692