Publications
2024
| Lucas P. Damasceno, Egzona Rexhepi, Allison Shafer, Ian Whitehouse, Nathalie Japkowicz, Charles C. Cavalcante, Roberto Corizzo, Zois Boukouvalas:
Exploiting sparsity and statistical dependence in multivariate data fusion: an application to misinformation detection for high-impact events. Mach. Learn. 113(4): 2183-2205 (2024)
DOI: https://doi.org/10.1007/s10994-023-06424-8 | | Marcin Pietron, Dominik Zurek, Kamil Faber, Roberto Corizzo:
Towards efficient deep autoencoders for multivariate time series anomaly detection. CoRR (2024)
DOI: https://doi.org/10.48550/arXiv.2403.02429 | | Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Nathalie Japkowicz:
Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights. IEEE Access 12: 41364-4138 (2024)
DOI: https://doi.org/10.1109/ACCESS.2024.3377690 | | Roberto Corizzo, Jacob Rosen:
Stock market prediction with time series data and news headlines: a stacking ensemble approach. J. Intell. Inf. Syst. 62(1): 27-56 (2024)
DOI: https://doi.org/10.1007/s10844-023-00804-1 | | Dhanush Kikkisetti, Raza Ul-Mustafa, Wendy Melillo, Roberto Corizzo, Zois Boukouvalas, Jeff Gill, Nathalie Japkowicz:
Using LLMs to discover emerging coded antisemitic hate-speech in extremist social media. CoRR (2024)
DOI: https://doi.org/10.48550/arXiv.2401.10841 |
2023
| Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Nathalie Japkowicz:
VLAD: Task-agnostic VAE-based lifelong anomaly detection. Neural Networks 165: 248-273 (2023)
DOI: https://doi.org/10.1016/j.neunet.2023.05.032 | | Roberto Corizzo, Nora Lewis, Lucas P. Damasceno, Allison Shafer, Charles C. Cavalcante, Zois Boukouvalas:
Multimodal One-class Learning for Malicious Online Content Detection. IEEE Big Data: 2146-2151 (2023)
DOI: https://doi.org/10.1109/BigData59044.2023.10386920 | | Roberto Corizzo, Sebastian Leal-Arenas:
One-GPT: A One-Class Deep Fusion Model for Machine-Generated Text Detection. IEEE Big Data: 5743-5752 (2023)
DOI: https://doi.org/10.1109/BigData59044.2023.10386674 | | Kamil Faber, Bartlomiej Sniezynski, Roberto Corizzo:
Distributed Continual Intrusion Detection: A Collaborative Replay Framework. IEEE Big Data: 3255-3263 (2023)
DOI: https://doi.org/10.1109/BigData59044.2023.10386211 | | Ian Whitehouse, Rodrigo Yepez-Lopez, Roberto Corizzo:
Distributed Concept Drift Detection for Efficient Model Adaptation with Big Data Streams. IEEE Big Data: 5392-5397 (2023)
DOI: https://doi.org/10.1109/BigData59044.2023.10386334 | | Dominik Zurek, Roberto Corizzo, Michal Karwatowski, Marcin Pietron, Kamil Faber:
Transformed-*: A domain-incremental lifelong learning scenario generation framework. IJCNN: 1-10 (2023)
DOI: https://doi.org/10.1109/IJCNN54540.2023.10191200 | | Lucas P. Damasceno, Egzona Rexhepi, Allison Shafer, Ian Whitehouse, Charles C. Cavalcante, Roberto Corizzo, Zois Boukouvalas:
Independent Vector Analysis with Sparse Inverse Covariance Estimation: An Application to Misinformation Detection. MLSP: 1-6 (2023)
DOI: https://doi.org/10.1109/MLSP55844.2023.10285970 | | Roberto Corizzo, Gianvito Pio, Emanuele Pio Barracchia, Antonio Pellicani, Nathalie Japkowicz, Michelangelo Ceci:
HURI: Hybrid user risk identification in social networks. World Wide Web (WWW) 26(5): 3409-3439 (2023)
DOI: https://doi.org/10.1007/s11280-023-01192-w | | Marcin Pietron, Dominik Zurek, Kamil Faber, Roberto Corizzo:
Ada-QPacknet - Multi-Task Forget-Free Continual Learning with Quantization Driven Adaptive Pruning. ECAI: 1882-1889 (2023)
DOI: https://doi.org/10.3233/FAIA230477 | | Massimiliano Altieri, Michelangelo Ceci, Roberto Corizzo:
Explainable Spatio-Temporal Graph Modeling. DS: 174-188 (2023)
DOI: https://doi.org/10.1007/978-3-031-45275-8_12 | | Marcin Pietron, Dominik Zurek, Kamil Faber, Roberto Corizzo:
Ada-QPacknet - adaptive pruning with bit width reduction as an efficient continual learning method without forgetting. CoRR (2023)
DOI: https://doi.org/10.48550/arXiv.2308.07939 | | Roberto Corizzo, Sebastian Leal-Arenas:
A Deep Fusion Model for Human $vs$. Machine-Generated Essay Classification. IJCNN: 1-10 (2023)
DOI: https://doi.org/10.1109/IJCNN54540.2023.10191322 | | Marcin Pietron, Dominik Zurek, Kamil Faber, Roberto Corizzo:
AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection. CoRR (2023)
DOI: https://doi.org/10.48550/arXiv.2305.16497 | | Kamil Faber, Dominik Zurek, Marcin Pietron, Nathalie Japkowicz, Antonio Vergari, Roberto Corizzo:
From MNIST to ImageNet and Back: Benchmarking Continual Curriculum Learning. CoRR (2023)
DOI: https://doi.org/10.48550/arXiv.2303.11076 | | Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Nathalie Japkowicz:
Lifelong Learning for Anomaly Detection: New Challenges, Perspectives, and Insights. CoRR (2023)
DOI: https://doi.org/10.48550/arXiv.2303.07557 |
2022
| Indranil Sur, Zachary Daniels, Abrar Rahman, Kamil Faber, Gianmarco J. Gallardo, Tyler L. Hayes, Cameron E. Taylor, Mustafa Burak Gurbuz, James Seale Smith, Sahana Pramod Joshi, Nathalie Japkowicz, Michael Baron, Zsolt Kira, Christopher Kanan, Roberto Corizzo, Ajay Divakaran, Michael Piacentino, Jesse Hostetler, Aswin Raghavan:
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games. AIMLSystems: 12:1-12:9 (2022)
DOI: https://doi.org/10.1145/3564121.3565236 | | Zachary Alan Daniels, Aswin Raghavan, Jesse Hostetler, Abrar Rahman, Indranil Sur, Michael Piacentino, Ajay Divakaran, Roberto Corizzo, Kamil Faber, Nathalie Japkowicz, Michael Baron, James Seale Smith, Sahana Pramod Joshi, Zsolt Kira, Cameron Ethan Taylor, Mustafa Burak Gurbuz, Constantine Dovrolis, Tyler L. Hayes, Christopher Kanan, Jhair Gallardo:
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2. CoLLAs: 1120-1145 (2022)
DOI: https://proceedings.mlr.press/v199/daniels22a.html | | Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Nathalie Japkowicz:
Active Lifelong Anomaly Detection with Experience Replay. DSAA: 1-10 (2022)
DOI: https://doi.org/10.1109/DSAA54385.2022.10032405 | | Massimiliano Altieri, Roberto Corizzo, Michelangelo Ceci:
Scalable Forecasting in Sensor Networks with Graph Convolutional LSTM Models. Big Data: 4595-4600 (2022)
DOI: https://doi.org/10.1109/BigData55660.2022.10020456 | | Roberto Corizzo, Terry Slenn:
Distributed Node Classification with Graph Attention Networks. Big Data: 3720-3725 (2022)
DOI: https://doi.org/10.1109/BigData55660.2022.10020664 | | Leah Ding, Roberto Corizzo, Colin Bellinger, Nancy Ching, Spencer Login, Rodrigo Yepez-Lopez, Jie Gong, Dong L. Wu:
Imbalanced Multi-layer Cloud Classification with Advanced Baseline Imager (ABI) and CloudSat/CALIPSO Data. Big Data: 5902-5909 (2022)
DOI: https://doi.org/10.1109/BigData55660.2022.10020783 | | Roberto Corizzo, Rodrigo Yepez-Lopez, Sébastien Gilbert, Nathalie Japkowicz:
LSTM-based Pulmonary Air Leak Forecasting for Chest Tube Management. Big Data: 5217-5222 (2022)
DOI: https://doi.org/10.1109/BigData55660.2022.10020874 | | Indranil Sur, Zachary Daniels, Abrar Rahman, Kamil Faber, Gianmarco J. Gallardo, Tyler L. Hayes, Cameron E. Taylor, Mustafa Burak Gurbuz, James Seale Smith, Sahana Joshi, Nathalie Japkowicz, Michael Baron, Zsolt Kira, Christopher Kanan, Roberto Corizzo, Ajay Divakaran, Michael R. Piacentino, Jesse Hostetler, Aswin Raghavan:
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games. CoRR (2022)
DOI: https://doi.org/10.48550/arXiv.2212.04603 | | Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Michael Baron, Nathalie Japkowicz:
LIFEWATCH: Lifelong Wasserstein Change Point Detection. IJCNN: 1-8 (2022)
DOI: https://doi.org/10.1109/IJCNN55064.2022.9892891 | | Myles Russell, Dylan Russell, Roberto Corizzo, Nathalie Japkowicz:
Machine Learning for Surgical Risk Assessment Decision Systems. IJCNN: 1-8 (2022)
DOI: https://doi.org/10.1109/IJCNN55064.2022.9892752 | | Roberto Corizzo, Junfeng Ge, Colin Bellinger, Xiaoqiang Zhu, Paula Branco, Kuang-chih Lee, Nathalie Japkowicz, Ruiming Tang, Tao Zhuang, Han Zhu, Biye Jiang, Jiaxin Mao, Weinan Zhang:
4th Workshop on Deep Learning Practice and Theory for High-Dimensional Sparse and Imbalanced Data with KDD 2022. KDD: 4860-4861 (2022)
DOI: https://doi.org/10.1145/3534678.3542896 | | Roberto Corizzo, Michael Baron, Nathalie Japkowicz:
CPDGA: Change point driven growing auto-encoder for lifelong anomaly detection. Knowl. Based Syst. 247: 108756 (2022)
DOI: https://doi.org/10.1016/j.knosys.2022.108756 | | Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Michael Baron, Nathalie Japkowicz:
WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data. CoRR (2022)
DOI: https://arxiv.org/abs/2201.07125 |
2021
| Kushankur Ghosh, Colin Bellinger, Roberto Corizzo, Bartosz Krawczyk, Nathalie Japkowicz:
On the combined effect of class imbalance and concept complexity in deep learning. IEEE BigData: 4859-4868 (2021)
DOI: https://doi.org/10.1109/BigData52589.2021.9672056 | | Roberto Corizzo, Yohan Dauphin, Colin Bellinger, Eftim Zdravevski, Nathalie Japkowicz:
Explainable image analysis for decision support in medical healthcare. IEEE BigData: 4667-4674 (2021)
DOI: https://doi.org/10.1109/BigData52589.2021.9671335 | | Kamil Faber, Roberto Corizzo, Bartlomiej Sniezynski, Michael Baron, Nathalie Japkowicz:
WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data. IEEE BigData: 4450-4459 (2021)
DOI: https://doi.org/10.1109/BigData52589.2021.9671962 | | Roberto Corizzo, Michelangelo Ceci, Gianvito Pio, Paolo Mignone, Nathalie Japkowicz:
Spatially-Aware Autoencoders for Detecting Contextual Anomalies in Geo-Distributed Data. DS: 461-471 (2021)
DOI: https://doi.org/10.1007/978-3-030-88942-5_36 | | Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz:
Calibrated Resampling for Imbalanced and Long-Tails in Deep Learning. DS: 242-252 (2021)
DOI: https://doi.org/10.1007/978-3-030-88942-5_19 | | Bartosz Krawczyk, Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz:
Undersampling with Support Vectors for Multi-Class Imbalanced Data Classification. IJCNN: 1-7 (2021)
DOI: https://doi.org/10.1109/IJCNN52387.2021.9533379 | | Kushankur Ghosh, Colin Bellinger, Roberto Corizzo, Bartosz Krawczyk, Nathalie Japkowicz:
On the combined effect of class imbalance and concept complexity in deep learning. CoRR (2021)
DOI: https://arxiv.org/abs/2107.14194 | | Mirche Arsov, Eftim Zdravevski, Petre Lameski, Roberto Corizzo, Nikola Koteli, Sasho Gramatikov, Kosta Mitreski, Vladimir Trajkovik:
Multi-Horizon Air Pollution Forecasting with Deep Neural Networks. Sensors 21(4): 1235 (2021)
DOI: https://doi.org/10.3390/s21041235 | | Roberto Corizzo, Michelangelo Ceci, Hadi Fanaee-T, João Gama:
Multi-aspect renewable energy forecasting. Inf. Sci. 546: 701-722 (2021)
DOI: https://doi.org/10.1016/j.ins.2020.08.003 |
2020
| Jovan Kalajdjieski, Eftim Zdravevski, Roberto Corizzo, Petre Lameski, Slobodan Kalajdziski, Ivan Miguel Pires, Nuno M. Garcia, Vladimir Trajkovik:
Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks. Remote. Sens. 12(24): 4142 (2020)
DOI: https://doi.org/10.3390/rs12244142 | | Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz:
ReMix: Calibrated Resampling for Class Imbalance in Deep learning. CoRR (2020)
DOI: https://arxiv.org/abs/2012.02312 | | Jonathan Kaufmann, Kathryn Asalone, Roberto Corizzo, Colin Saldanha, John Bracht, Nathalie Japkowicz:
One-Class Ensembles for Rare Genomic Sequences Identification. DS: 340-354 (2020)
DOI: https://doi.org/10.1007/978-3-030-61527-7_23 | | Mirche Arsov, Eftim Zdravevski, Petre Lameski, Roberto Corizzo, Nikola Koteli, Kosta Mitreski, Vladimir Trajkovik:
Short-term air pollution forecasting based on environmental factors and deep learning models. FedCSIS: 15-22 (2020)
DOI: https://doi.org/10.15439/2020F211 | | Michelangelo Ceci, Roberto Corizzo, Nathalie Japkowicz, Paolo Mignone, Gianvito Pio:
ECHAD: Embedding-Based Change Detection From Multivariate Time Series in Smart Grids. IEEE Access 8: 156053-156 (2020)
DOI: https://doi.org/10.1109/ACCESS.2020.3019095 | | Biserka Petrovska, Eftim Zdravevski, Petre Lameski, Roberto Corizzo, Ivan Stajduhar, Jonatan Lerga:
Deep Learning for Feature Extraction in Remote Sensing: A Case-Study of Aerial Scene Classification. Sensors 20(14): 3906 (2020)
DOI: https://doi.org/10.3390/s20143906 | | Roberto Corizzo, Michelangelo Ceci, Eftim Zdravevski, Nathalie Japkowicz:
Scalable auto-encoders for gravitational waves detection from time series data. Expert Syst. Appl. 151: 113378 (2020)
DOI: https://doi.org/10.1016/j.eswa.2020.113378 |
2019
| Sid Ryan, Roberto Corizzo, Iluju Kiringa, Nathalie Japkowicz:
Deep Learning Versus Conventional Learning in Data Streams with Concept Drifts. ICMLA: 1306-1313 (2019)
DOI: https://doi.org/10.1109/ICMLA.2019.00213 | | Sid Ryan, Roberto Corizzo, Iluju Kiringa, Nathalie Japkowicz:
Pattern and Anomaly Localization in Complex and Dynamic Data. ICMLA: 1756-1763 (2019)
DOI: https://doi.org/10.1109/ICMLA.2019.00285 | | Roberto Corizzo, Michelangelo Ceci, Donato Malerba:
Big Data Analytics and Predictive Modeling Approaches for the Energy Sector. BigData Congress: 55-63 (2019)
DOI: https://doi.org/10.1109/BigDataCongress.2019.00020 | | Roberto Corizzo, Michelangelo Ceci, Nathalie Japkowicz:
Anomaly Detection and Repair for Accurate Predictions in Geo-distributed Big Data. Big Data Research 16: 18-35 (2019)
DOI: https://doi.org/10.1016/j.bdr.2019.04.001 | | Ameya Malondkar, Roberto Corizzo, Iluju Kiringa, Michelangelo Ceci, Nathalie Japkowicz:
Spark-GHSOM: Growing Hierarchical Self-Organizing Map for large scale mixed attribute datasets. Inf. Sci. 496: 572-591 (2019)
DOI: https://doi.org/10.1016/j.ins.2018.12.007 | | Roberto Corizzo, Gianvito Pio, Michelangelo Ceci, Donato Malerba:
DENCAST: distributed density-based clustering for multi-target regression. J. Big Data 6: 43 (2019)
DOI: https://doi.org/10.1186/s40537-019-0207-2 | | Michelangelo Ceci, Roberto Corizzo, Donato Malerba, Aleksandra Rashkovska:
Spatial autocorrelation and entropy for renewable energy forecasting. Data Min. Knowl. Discov. 33(3): 698-729 (2019)
DOI: https://doi.org/10.1007/s10618-018-0605-7 |
2018
2017
| Michelangelo Ceci, Roberto Corizzo, Fabio Fumarola, Donato Malerba, Aleksandra Rashkovska:
Predictive Modeling of PV Energy Production: How to Set Up the Learning Task for a Better Prediction? IEEE Trans. Industrial Informatics 13(3): 956-966 (2017)
DOI: https://doi.org/10.1109/TII.2016.2604758 | | Roberto Corizzo, Gianvito Pio, Michelangelo Ceci, Donato Malerba:
Forecasting via Distributed Density-Based Clustering. SEBD: 57 (2017)
DOI: http://ceur-ws.org/Vol-2037/paper_10.pdf | | Roberto Corizzo, Dino Ienco:
Proceedings of the ECML/PKDD Discovery Challenges co-located with European Conference on Machine Learning - Principle and Practice of Knowledge Discovery in Database (ECML PKDD 2017), Skopje, Macedonia, September 18, 2017. DC@PKDD/ECML CEUR Workshop Proceedings 1972, CEUR-WS.org (2017)
DOI: http://ceur-ws.org/Vol-1972 |
2015
| Michelangelo Ceci, Roberto Corizzo, Fabio Fumarola, Michele Ianni, Donato Malerba, Gaspare Maria, Elio Masciari, Marco Oliverio, Aleksandra Rashkovska:
Big Data Techniques For Supporting Accurate Predictions of Energy Production From Renewable Sources. IDEAS: 62-71 (2015)
DOI: https://doi.org/10.1145/2790755.2790762 | | Michelangelo Ceci, Roberto Corizzo, Fabio Fumarola, Michele Ianni, Donato Malerba, Gaspare Maria, Elio Masciari, Marco Oliverio, Aleksandra Rashkovska:
VIPOC Project Research Summary (Discussion Paper). SEBD: 208-215 (2015) |
2014
| Michelangelo Ceci, Nunziato Cassavia, Roberto Corizzo, Pietro Dicosta, Donato Malerba, Gaspare Maria, Elio Masciari, Camillo Pastura:
Innovative power operating center management exploiting big data techniques. IDEAS: 326-329 (2014)
DOI: https://doi.org/10.1145/2628194.2628231 | | Michelangelo Ceci, Nunziato Cassavia, Roberto Corizzo, Pietro Dicosta, Donato Malerba, Gaspare Maria, Elio Masciari, Camillo Pastura:
Big Data Techniques For Renewable Energy Market. SEBD: 369-377 (2014) |
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