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CAMINO Ramiro Daniel

Main Referenced Co-authors
STATE, Radu  (7)
HAMMERSCHMIDT, Christian  (4)
BADEN, Mathis  (1)
FALK, Eric  (1)
FERREIRA TORRES, Christof  (1)
Main Referenced Keywords
anti-money laundering (1); Categorical Variables (1); deep generative models (1); Deep Learning (1); Discrete Distributions (1);
Main Referenced Unit & Research Centers
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Services and Data management research group (SEDAN) (1)
Nokia Bell Labs (1)
ULHPC - University of Luxembourg: High Performance Computing (1)
Main Referenced Disciplines
Computer science (9)

Publications (total 9)

The most downloaded
788 downloads
CAMINO, R. D., HAMMERSCHMIDT, C., & STATE, R. (July 2018). Generating Multi-Categorical Samples with Generative Adversarial Networks [Paper presentation]. ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models, Stockholm, Sweden. https://hdl.handle.net/10993/36084

The most cited

34 citations (Scopus®)

CAMINO, R. D., STATE, R., MONTERO, L., & VALTCHEV, P. (2017). Finding Suspicious Activities in Financial Transactions and Distributed Ledgers. In Proceedings of the 17th IEEE International Conference on Data Mining Workshops (ICDMW 2017). https://hdl.handle.net/10993/33743

CAMINO, R. D. (2020). Machine Learning Techniques for Suspicious Transaction Detection and Analysis [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/44939

CAMINO, R. D., FERREIRA TORRES, C., BADEN, M., & STATE, R. (2020). A Data Science Approach for Honeypot Detection in Ethereum. In 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC).
Peer reviewed

CAMINO, R. D., HAMMERSCHMIDT, C., & STATE, R. (17 July 2020). Working with Deep Generative Models and Tabular Data Imputation [Paper presentation]. First Workshop on the Art of Learning with Missing Values (Artemiss), Vienna, Austria.

CAMINO, R. D., HAMMERSCHMIDT, C., & STATE, R. (2020). Minority Class Oversampling for Tabular Data with Deep Generative Models. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/43194.

CAMINO, R. D., HAMMERSCHMIDT, C., & STATE, R. (2019). Improving Missing Data Imputation with Deep Generative Models. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/43196.

CAMINO, R. D., HAMMERSCHMIDT, C., & STATE, R. (July 2018). Generating Multi-Categorical Samples with Generative Adversarial Networks [Paper presentation]. ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models, Stockholm, Sweden.

CAMINO, R. D. (27 June 2018). GAN Applications with Discrete Data [Poster presentation]. 2nd Data Science Summer School (DS3), Paris, France.

FALK, E., CAMINO, R. D., STATE, R., & Gurbani, V. K. (2017). On non-parametric models for detecting outages in the mobile network. In Integrated Network and Service Management 2017 (pp. 1139-1142). doi:10.23919/INM.2017.7987448
Peer reviewed

CAMINO, R. D., STATE, R., MONTERO, L., & VALTCHEV, P. (2017). Finding Suspicious Activities in Financial Transactions and Distributed Ledgers. In Proceedings of the 17th IEEE International Conference on Data Mining Workshops (ICDMW 2017).
Peer reviewed

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