I study trustworthy machine learning from a learning theory perspective.
I recently published my thesis on the sample complexity of robust learning against evasion (test-time) attacks.
Theses
Journal Publications
- On the hardness of robust classification
P. Gourdeau, V. Kanade, M. Kwiatkowska, and J. Worrell
In Journal of Machine Learning Research, 2021.
Paper.
- Bisimulation metrics and norms for real-weighted automata
B. Balle, P. Gourdeau, and P. Panangaden
In Information and Computation , 2020.
Paper.
Conference Publications
For the most up-to-date list, see Google scholar.
- When are local queries useful for robust learning?
P. Gourdeau, V. Kanade, M. Kwiatkowska, and J. Worrell
In 36th Conference on Neural Information Processing Systems (NeurIPS), 2022.
Paper. Slides.
- Sample complexity bounds for robustly learning decision lists against evasion attacks
P. Gourdeau, V. Kanade, M. Kwiatkowska, and J. Worrell
In International Joint Conference in Artifical Intelligence (IJCAI), 2022.
Paper. Poster. Slides (long presentation).
- On the hardness of robust classification
P. Gourdeau, V. Kanade, M. Kwiatkowska, and J. Worrell
In 33rd Conference on Neural Information Processing Systems (NeurIPS), 2019.
Paper. Poster. Spotlight Slides.
- Bisimulation metrics for weighted automata
B. Balle, P. Gourdeau, and P. Panangaden
In 44th International Colloquium on Automata, Languages, and Programming (ICALP 2017), Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik , 2017.
Paper. Slides.
- Feature selection and oversampling in analysis of clinical data for extubation readiness in extreme preterm infants
P. Gourdeau, L. Kanbar, W. Shalish, G. Sant'Anna, R. Kearney, and D. Precup
In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4427-4430, IEEE, 2015.
Paper. Poster.