Amina Mollaysa

Amina Mollaysa Assistant HES, PhD student CS UNIGE


Amina Mollaysa joined the DMML team as a research and teaching assistant in March 2015. After finishing her bachelor on Theoretical Math, she is leading her carrier where the theory and application meet. She has studied MathMods - Mathematical Modelling in Engineering: Theory, Numerics, Application Master Program at the University of Nice Sophia Antipolice, University of Hamburg and the University of L'Aquila. She has worked on the free-flight routing problem for her master thesis within the E-Motion project in Hamburg. She is currently pursuing a Ph.D. in the Department of Computer Science at the University of Geneva udner the joint supervision of Profs. Alexandros Kalousis and Stephane Marchand-Maillet.


The main focus of her research is learning with feature-side information. Until now she has explored two directions. The first one is the use of feature side-information to design better regularizers for deep nets to avoid overfitting. The second one is to use feature side-information for data augmentation.

Currently, she is looking into GAN and VAEs and how these can be used to learn data augmentations. She is also interested on deep network optimization and network interpretability by explaining individual predictions of the generic multilayer neural network.



Mollaysa, Amina; Paige, Brooks; Kalousis, Alexandros

Goal-directed Generation of Discrete Structures with Conditional Generative Models Inproceedings

Proceedings of the 44th Conference on Neural Information Processing Systems, NeurIPS , 2020.

Abstract | Links | BibTeX


Mollaysa, Amina; Kalousis, Alexandros; Bruno, Eric; Diephuis, Maurits

Learning to Augment with Feature Side-information Conference

Proceedings of The Eleventh Asian Conference on Machine Learning, 2019.

Abstract | Links | BibTeX


Mollaysa, Amina; Strasser, Pablo; Kalousis, Alexandros

Regularising Non-linear Models Using Feature Side-information Inproceedings

Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, pp. 2508–2517, 2017.

Abstract | Links | BibTeX