Research using Taija

Taija has been used in the following publications:

  • Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition (Hengst et al. 2024) upcoming in ACL’s NAACL Findings 20241.
  • Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals (Altmeyer et al. 2024) published in Proceedings of the AAAI Conference on Artificial Intelligence 2024.
  • Explaining Black-Box Models through Counterfactuals (Altmeyer, Deursen, et al. 2023) published in JuliaCon Proceedings.
  • Endogenous Macrodynamics in Algorithmic Recourse (Altmeyer et al. 2023) published in Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML).

If you have used Taija in your research, please let us know so we can add your publication to the list.

References

Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia CS Liem. 2023. “Endogenous Macrodynamics in Algorithmic Recourse.” In 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 418–31. IEEE.
Altmeyer, Patrick, Arie van Deursen, et al. 2023. “Explaining Black-Box Models Through Counterfactuals.” In Proceedings of the JuliaCon Conferences, 1:130. 1.
Altmeyer, Patrick, Mojtaba Farmanbar, Arie van Deursen, and Cynthia CS Liem. 2024. “Faithful Model Explanations Through Energy-Constrained Conformal Counterfactuals.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:10829–37. 10.
Hengst, Floris den, Ralf Wolter, Patrick Altmeyer, and Arda Kaygan. 2024. “Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition.” https://arxiv.org/abs/2403.18973.

Footnotes

  1. Experiments were run in parallel using Python’s MAPIE and ConformalPrediction.jl, in order to cross-check results. Reported results were produced using MAPIE.↩︎