| Reinforcement learning under threats V Gallego, R Naveiro, DR Insua Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 9939-9940, 2019 | 21 | 2019 |
| Stochastic Gradient MCMC with Repulsive Forces V Gallego, DR Insua NIPS Workshop on Bayesian Deep Learning, 2018 | 16 | 2018 |
| Waves in isotropic totalistic cellular automata: Application to real-time robot navigation C Calvo, JA Villacorta-Atienza, VI Mironov, V Gallego, VA Makarov Advances in Complex Systems 19 (04n05), 1650012, 2016 | 15 | 2016 |
| Variationally inferred sampling through a refined bound V Gallego, D Ríos Insua Entropy 23 (1), 123, 2021 | 11* | 2021 |
| Opponent Aware Reinforcement Learning V Gallego, R Naveiro, DR Insua, D Gómez-Ullate arXiv preprint arXiv:1908.08773, 2019 | 10 | 2019 |
| Adversarial machine learning: Perspectives from adversarial risk analysis DR Insua, R Naveiro, V Gallego, J Poulos arXiv preprint arXiv:2003.03546, 2020 | 9* | 2020 |
| Assessing the effect of advertising expenditures upon sales: a Bayesian structural time series model V Gallego, P Angulo, P Suárez-García, D Gómez-Ullate Applied Stochastic Models in Business and Industry, 1-13, 2019 | 8 | 2019 |
| Adversarial risk analysis: An overview D Banks, V Gallego, R Naveiro, D Ríos Insua Wiley Interdisciplinary Reviews: Computational Statistics 14 (1), e1530, 2022 | 5 | 2022 |
| Synchronization of heteroclinic circuits through learning in chains of neural motifs VA Makarov, C Calvo, V Gallego, A Selskii IFAC-PapersOnLine 49 (14), 80-83, 2016 | 5 | 2016 |
| Protecting classifiers from attacks. a bayesian approach V Gallego, R Naveiro, A Redondo, DR Insua, F Ruggeri arXiv preprint arXiv:2004.08705, 2020 | 4 | 2020 |
| Perspectives on Adversarial Classification D Rios Insua, R Naveiro, V Gallego Mathematics 8 (11), 1957, 2020 | 3 | 2020 |
| AI in drug development: a multidisciplinary perspective. V Gallego, R Naveiro, C Roca, D Rios Insua, NE Campillo Molecular Diversity, 2021 | 2 | 2021 |
| Data sharing games V Gallego, R Naveiro, DR Insua, W Rozas arXiv preprint arXiv:2101.10721, 2021 | 2 | 2021 |
| Multiagent systems N Osman, D López Castro, V Gallego, D Ríos Insua, M San Miguel, ... Editorial CSIC, 2021 | 2 | 2021 |
| Bayesian Factorization Machines for Risk Management and Robust Decision Making P Angulo, V Gallego, D Gómez-Ullate, P Suárez-García Mathematical and Statistical Methods for Actuarial Sciences and Finance, 51-55, 2018 | 2 | 2018 |
| Learning connectivity structure in a chain of network motifs C Calvo, V Gallego, A Selskii, VA Makarov Advanced Science Letters 22 (10), 2647-2651, 2016 | 2 | 2016 |
| TUN-AI: Tuna biomass estimation with Machine Learning models trained on oceanography and echosounder FAD data D Precioso, M Navarro-García, K Gavira-O'Neill, A Torres-Barrán, ... Fisheries Research 250, 106263, 2022 | 1 | 2022 |
| Current Advances in Neural Networks V Gallego, D Ríos Insua Annual Review of Statistics and Its Application 9, 2022 | 1 | 2022 |
| Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning V Gallego arXiv preprint arXiv:2109.13232, 2021 | | 2021 |
| Aggregation Times of Tuna Schools to FADs Estimated by Echosounder Data M Navarro-García, D Precioso, K Gavira-O’Neill, A Torres-Barrán, ... | | 2021 |
David R InsuaICMAT-CSICVerified email at icmat.es
