References
==========

.. _ref-1:

**[1]** Katsios, K., & Papadopoulos, H. (2024). Multi-label conformal prediction with a Mahalanobis distance nonconformity measure.
*Proceedings of Machine Learning Research*, 230, 1-14. `Proceedings <https://proceedings.mlr.press/v230/katsios24a.html>`_

.. _ref-2:

**[2]** Katsios, K., & Papadopoulos, H. (2025). Incorporating Structural Penalties in Multi-label Conformal Prediction.
*Proceedings of Machine Learning Research*, 266, 1-20. `Proceedings <https://proceedings.mlr.press/v266/katsios25a.html>`_

.. _ref-3:

**[3]** Papadopoulos, H. (2014). A cross-conformal predictor for multi-label classification. In *Artificial Intelligence Applications and Innovations: AIAI 2014 Workshops: CoPA, MHDW, IIVC, and MT4BD, Rhodes, Greece, September 19-21, 2014. Proceedings 10* (pp. 241–250). Springer. `DOI: 10.1007/978-3-662-44722-2_26 <https://doi.org/10.1007/978-3-662-44722-2_26>`_

.. _ref-4:

**[4]** Maltoudoglou, L., Paisios, A., Lenc, L., Martı́nek, J., Král, P., & Papadopoulos, H. (2022). Well-calibrated confidence measures for multi-label text classification with a large number of labels. *Pattern Recognition*, 122, 108271. `DOI: 10.1016/j.patcog.2021.108271 <https://doi.org/10.1016/j.patcog.2021.108271>`_

.. _ref-5:

**[5]** Lambrou, A., & Papadopoulos, H. (2016). Binary relevance multi-label conformal predictor. In *Conformal and Probabilistic Prediction with Applications* (pp. 90–104). Springer. `DOI: 10.1007/978-3-319-33395-3_7 <https://doi.org/10.1007/978-3-319-33395-3_7>`_

.. _ref-6:

**[6]** Papadopoulos, H., Proedrou, K., Vovk, V., & Gammerman, A. (2002a). Inductive confidence machines for regression. In *Machine learning: ECML 2002: 13th European conference on machine learning Helsinki, Finland, August 19–23, 2002 proceedings 13* (pp. 345–356). Springer. `DOI: 10.1007/3-540-36755-1_29 <https://doi.org/10.1007/3-540-36755-1_29>`_

.. _ref-7:

**[7]** Papadopoulos, H., Vovk, V., & Gammerman, A. (2002b). Qualified prediction for large data sets in the case of pattern recognition. In *ICMLA* (pp. 159–163).

.. _ref-8:

**[8]** Vovk, V., Gammerman, A., & Shafer, G. (2005). *Algorithmic Learning in a Random World* (Vol. 29). Springer. `DOI: 10.1007/b106715 <https://doi.org/10.1007/b106715>`_

.. _ref-9:

**[9]** Vovk, V., Fedorova, V., Nouretdinov, I., & Gammerman, A. (2016). Criteria of efficiency for conformal prediction. In *Conformal and Probabilistic Prediction with Applications: 5th International Symposium, COPA 2016, Madrid, Spain, April 20-22, 2016, Proceedings 5* (pp. 23–39). Springer. `DOI: 10.1007/978-3-319-33395-3_2 <https://doi.org/10.1007/978-3-319-33395-3_2>`_