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publications
On non-parametric models for detecting outages in the mobile network
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
Finding suspicious activities in financial transactions and distributed ledgers
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/
Generating Multi-Categorical Samples with Generative Adversarial Networks
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
Improving Missing Data Imputation with Deep Generative Models
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
A Data Science Approach for Detecting Honeypots in Ethereum
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
Working with deep generative models and tabular data imputation
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
Machine Learning Techniques for Suspicious Transaction Detection and Analysis
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
Oversampling Tabular Data with Deep Generative Models: Is it worth the effort?
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
Frontrunner Jones and the Raiders of the Dark Forest: An Empirical Study of Frontrunning on the Ethereum Blockchain
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
Low-Emission Driving Assistants: Experience from MODALES
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.
Assessing the Electromagnetic Field Exposure of 5G Transmitters: Challenges & Solutions
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
talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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