Published in ACM MobiWac 2022, 2022
Recommended citation: Estrada, J., Pardo, J., Camino, R., & Faye, S. (2022). Assessing the Electromagnetic Field Exposure of 5G Transmitters: Challenges & Solutions. In Proceedings of the 20th ACM International Symposium on Mobility Management and Wireless Access (MobiWac ’22), pp. 29–36. https://doi.org/10.1145/3551660.3560906
Published in 14th ITS European Congress, Toulouse, 2022, 2022
Recommended citation: Camino, R., Faye, S., Dimokas, N., & Margaritis, D. (2022). Low-Emission Driving Assistants: Experience from MODALES. In 14th ITS European Congress, Toulouse, France.
Published in USENIX Security Symposium 2021, 2021
Recommended citation: Ferreira Torres, C., & Camino, R. (2021). Frontrunner Jones and the Raiders of the Dark Forest: An Empirical Study of Frontrunning on the Ethereum Blockchain. In USENIX Security Symposium, Virtual 11-13 August 2021. https://www.usenix.org/conference/usenixsecurity21/presentation/torres
Published in "I Can't Believe It's Not Better!" NeurIPS 2020 workshop, 2020
Recommended citation: Camino, R. D., & Hammerschmidt, C. A. (2020). Oversampling Tabular Data with Deep Generative Models: Is it worth the effort?. http://proceedings.mlr.press/v137/camino20a
Published in University of Luxembourg, Luxembourg, 2020
Recommended citation: Camino, R. D. (2020). Machine Learning Techniques for Suspicious Transaction Detection and Analysis (Doctoral dissertation, University of Luxembourg, Luxembourg, Luxembourg). https://orbilu.uni.lu/bitstream/10993/44939/1/PhD_Thesis.pdf
Published in Workshop on the Art of Learning with Missing Values (Artemiss) hosted by the 37 th International Conference on Machine Learning (ICML), 2020
Recommended citation: Camino, R. D., & Hammerschmidt, C. (2020). Working with deep generative models and tabular data imputation. https://openreview.net/forum?id=R4w3PTkCD4
Published in 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 2020
Recommended citation: Camino, R., Torres, C. F., Baden, M., & State, R. (2020, May). A data science approach for detecting honeypots in ethereum. In 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (pp. 1-9). IEEE. https://ieeexplore.ieee.org/abstract/document/9169396
Published in arXiv, 2019
This was never published but was cited a couple of times anyways.
Recommended citation: Camino, R. D., Hammerschmidt, C. A. & State, R. (2019). Improving missing data imputation with deep generative models. arXiv preprint arXiv:1902.10666. https://arxiv.org/abs/1902.10666
Published in ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models, Stockholm, Sweden., 2018
Recommended citation: Camino, R., Hammerschmidt, C., & State, R. (2018). Generating multi-categorical samples with generative adversarial networks. arXiv preprint arXiv:1807.01202. https://arxiv.org/abs/1807.01202
Published in 2017 IEEE International Conference on Data Mining Workshops (ICDMW), 2017
Recommended citation: Camino, R. D., State, R., Montero, L., & Valtchev, P. (2017, November). Finding suspicious activities in financial transactions and distributed ledgers. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 787-796). IEEE. https://ieeexplore.ieee.org/abstract/document/8215741/
Published in 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), 2017
Recommended citation: Falk, E., Camino, R., State, R., & Gurbani, V. K. (2017, May). On non-parametric models for detecting outages in the mobile network. In 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) (pp. 1139-1142). IEEE. https://ieeexplore.ieee.org/abstract/document/7987448