Transparency in Bayesian Optimization

On May 5th, Tanmay Chakraborty successfully defended his doctoral thesis on explainability and human–algorithm collaboration in Bayesian Optimization. His research addresses a key challenge in industrial optimization: while Bayesian Optimization is highly data-efficient for complex black-box systems, its lack of transparency often limits practical adoption. The thesis introduces novel explanation methods designed specifically for Bayesian, including TNTRules, and MOLONE, which help experts understand optimization recommendations, parameter interactions, uncertainty, and trade-offs during decision-making.

The work further demonstrates through user studies that explanations significantly improve task performance, trust, and understanding without increasing cognitive load, highlighting the importance of explainability for effective human–AI collaboration in real-world optimization workflows.

Tanmay even managed to hide eggs in his thesis — though not very subtly. Congrats on this eggselent achievement!

Special thanks to Christian Wirth from Aumovio for his supervision and guidance.

Key Publications

(Chakraborty et al., 2025) (Chakraborty et al., 2025) (Chakraborty et al., 2025)

1. Tanmay Chakraborty, Marion Koelle, Jörg Schlötterer, Nadine Schlicker, Christian Wirth, and Christin Seifert. Explanation Format does not Matter; but Explanations do - An Eggsbert study on explaining Bayesian Optimization tasks. Information Systems Frontiers. 2025.
   BibTeX

   @article{Chakraborty2025_isf_eggsbert,
     author = {Chakraborty, Tanmay and Koelle, Marion and Schlötterer, Jörg and Schlicker, Nadine and Wirth, Christian and Seifert, Christin},
     journal = {Information Systems Frontiers},
     title = {{Explanation Format does not Matter; but Explanations do - An Eggsbert study on explaining Bayesian Optimization tasks}},
     year = {2025},
     month = dec,
     doi = {10.1007/s10796-025-10671-6},
     file = {:own-pdf/Chakraborty2025_isf_eggsbert_preprint.pdf:PDF},
     publisher = {Springer Nature},
     url = {https://link.springer.com/10.1007/s10796-025-10671-6}
   }
   

2. Tanmay Chakraborty, Christian Wirth, and Christin Seifert. Comparative Explanations: Explanation Guided Decision Making for Human-in-the-Loop Preference Selection. Explainable Artificial Intelligence. 2025.
   BibTeX

   @inproceedings{Chakraborty2025_xaiw_comparitive-explanation,
     author = {Chakraborty, Tanmay and Wirth, Christian and Seifert, Christin},
     booktitle = {Explainable Artificial Intelligence},
     title = {Comparative Explanations: Explanation Guided Decision Making for Human-in-the-Loop Preference Selection},
     year = {2025},
     address = {Cham},
     editor = {Guidotti, Riccardo and Schmid, Ute and Longo, Luca},
     pages = {139--161},
     publisher = {Springer Nature Switzerland},
     doi = {10.1007/978-3-032-08317-3_7}
   }
   

3. Tanmay Chakraborty, Christian Wirth, and Christin Seifert. Explainable Bayesian Optimization. Explainable Artificial Intelligence. 2025.
   BibTeX

   @inproceedings{Chakraborty2025_xaiw_explainable-bayesian-optimization,
     author = {Chakraborty, Tanmay and Wirth, Christian and Seifert, Christin},
     booktitle = {Explainable Artificial Intelligence},
     title = {Explainable Bayesian Optimization},
     year = {2025},
     address = {Cham},
     editor = {Guidotti, Riccardo and Schmid, Ute and Longo, Luca},
     pages = {53--77},
     publisher = {Springer Nature Switzerland},
     doi = {10.1007/978-3-032-08324-1_3}
   }