Amina Mollaysa

@inproceedings{https://dblp.org/rec/conf/nips/MollaysaPK20,
title = {Goal-directed Generation of Discrete Structures with Conditional Generative Models},
author = {Amina Mollaysa and Brooks Paige and Alexandros Kalousis },
url = {https://papers.nips.cc/paper/2020/file/f9b9f0fef2274a6b7009b5d52f44a3b6-Paper.pdf
https://arxiv.org/abs/2010.02311
https://github.com/amina11/Goal-directed-Generation-of-Discrete-Structures},
year = {2020},
date = {2020-12-05},
booktitle = {Proceedings of the 44th Conference on Neural Information Processing Systems, NeurIPS },
abstract = {Despite recent advances, goal-directed generation of structured discrete data remains challenging. For problems such as program synthesis (generating source code) and materials design (generating molecules), finding examples which satisfy desired constraints or exhibit desired properties is difficult. In practice, expensive heuristic search or reinforcement learning algorithms are often employed. In this paper we investigate the use of conditional generative models which directly attack this inverse problem, by modeling the distribution of discrete structures given properties of interest. Unfortunately, maximum likelihood training of such models often fails with the samples from the generative model inadequately respecting the input properties. To address this, we introduce a novel approach to directly optimize a reinforcement learning objective, maximizing an expected reward. We avoid high-variance score-function estimators that would otherwise be required by sampling from an approximation to the normalized rewards, allowing simple Monte Carlo estimation of model gradients. We test our methodology on two tasks: generating molecules with user-defined properties and identifying short python expressions which evaluate to a given target value. In both cases, we find improvements over maximum likelihood estimation and other baselines.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@conference{pmlr-v101-mollaysa19a,
title = {Learning to Augment with Feature Side-information},
author = {Amina Mollaysa and Alexandros Kalousis and Eric Bruno and Maurits Diephuis },
editor = { November 17 - 19, 2019},
url = {http://proceedings.mlr.press/v101/mollaysa19a.html},
year = {2019},
date = {2019-11-19},
booktitle = {Proceedings of The Eleventh Asian Conference on Machine Learning},
abstract = {Neural networks typically need huge amounts of data to train in order to get reasonable generalizable results. A common approach is to artificially generate samples by using prior knowledge of the data properties or other relevant domain knowledge. However, if the assumptions on the data properties are not accurate or the domain knowledge is irrelevant to the task at hand, one may end up degenerating learning performance by using such augmented data in comparison to simply training on the limited available dataset. We propose a critical data augmentation method using feature side-information, which is obtained from domain knowledge and provides detailed information about features' intrinsic properties. Most importantly, we introduce an instance wise quality checking procedure on the augmented data. It filters out irrelevant or harmful augmented data prior to entering the model. We validated this approach on both synthetic and real-world datasets, specifically in a scenario where the data augmentation is done based on a task independent, unreliable source of information. The experiments show that the introduced critical data augmentation scheme helps avoid performance degeneration resulting from incorporating wrong augmented data. },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@inproceedings{DBLP:conf/icml/MollaysaSK17,
title = {Regularising Non-linear Models Using Feature Side-information},
author = {Amina Mollaysa and Pablo Strasser and Alexandros Kalousis},
url = {http://proceedings.mlr.press/v70/mollaysa17a/mollaysa17a.pdf
http://proceedings.mlr.press/v70/mollaysa17a/mollaysa17a-supp.pdf
https://bitbucket.org/dmmlgeneva/code_icml2017_regularising-non-linear-models-using-feature},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the 34th International Conference on Machine Learning,
ICML 2017, Sydney, NSW, Australia, 6-11 August 2017},
pages = {2508--2517},
crossref = {DBLP:conf/icml/2017},
abstract = {Very often features come with their own vectorial descriptions which provide detailed information about their properties. We refer to these vectorial descriptions as feature side-information. In the standard learning scenario, input is represented as a vector of features and the feature side-information is most often ignored or used only for feature selection prior to model fitting. We believe that feature side-information which carries information about features intrinsic property will help improve model prediction if used in a proper way during learning process. In this paper, we propose a framework that allows for the incorporation of the feature side-information during the learning of very general model families to improve the prediction performance. We control the structures of the learned models so that they reflect features’ similarities as these are defined on the basis of the side-information. We perform experiments on a number of benchmark datasets which show significant predictive performance gains, over a number of baselines, as a result of the exploitation of the side-information.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}