| 2026 | IDA | CARTGen-IR: Synthetic Tabular Data Generation for Imbalanced Regression. | Antnio Pedro Pinheiro, Rita P. Ribeiro |
| 2025 | SAC | Efficient Instance Selection in Tree-Based Models for Data Streams Classification. | Aldo Marcelo Paim, Joo Gama, Bruno Veloso, Fabrcio Enembreck, Rita P. Ribeiro |
| 2024 | ECAI | More (Enough) Is Better: Towards Few-Shot Illegal Landfill Waste Segmentation. | Matas Molina, Bruno Veloso, Carlos Abreu Ferreira, Rita P. Ribeiro, Joo Gama |
| 2024 | IJCCI | Predictive Maintenance for Industry 4.0 & 5.0. | Rita P. Ribeiro |
| 2024 | IDA | Super-Resolution Analysis for Landfill Waste Classification. | Matas Molina, Rita P. Ribeiro, Bruno Veloso, Joo Gama |
| 2023 | EPIA | Topic Model with Contextual Outlier Handling: a Study on Electronic Invoice Product Descriptions. | Csar Andrade, Rita P. Ribeiro, Joo Gama |
| 2023 | EPIA | Pollution Emission Patterns of Transportation in Porto, Portugal Through Network Analysis. | Thiago Andrade, Nirbhaya Shaji, Rita P. Ribeiro, Joo Gama |
| 2023 | KDD | XAI for Predictive Maintenance. | Joo Gama, Slawomir Nowaczyk, Sepideh Pashami, Rita P. Ribeiro, Grzegorz J. Nalepa, Bruno Veloso |
| 2022 | DIS | Model Optimization in Imbalanced Regression. | Anibal Silva, Rita P. Ribeiro, Nuno Moniz |
| 2022 | IDA | A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set. | Narjes Davari, Sepideh Pashami, Bruno Veloso, Slawomir Nowaczyk, Yuantao Fan, Pedro Mota Pereira, Rita P. Ribeiro, Joo Gama |
| 2022 | IDA | Bank Statements to Network Features: Extracting Features Out of Time Series Using Visibility Graph. | Nirbhaya Shaji, Joo Gama, Rita P. Ribeiro, Pedro Gomes |
| 2022 | IDA | Combining Multiple Data Sources to Predict IUCN Conservation Status of Reptiles. | Ndia Soares, Joo F. Gonalves, Raquel Vasconcelos, Rita P. Ribeiro |
| 2021 | DSAA | Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry. | Narjes Davari, Bruno Veloso, Rita P. Ribeiro, Pedro Mota Pereira, Joo Gama |
| 2018 | DIS | MetaUtil: Meta Learning for Utility Maximization in Regression. | Paula Branco, Lus Torgo, Rita P. Ribeiro |
| 2018 | DSAA | SMOTEBoost for Regression: Improving the Prediction of Extreme Values. | Nuno Moniz, Rita P. Ribeiro, Vtor Cerqueira, Nitesh V. Chawla |
| 2017 | DSAA | Learning Through Utility Optimization in Regression Tasks. | Paula Branco, Lus Torgo, Rita P. Ribeiro, Eibe Frank, Bernhard Pfahringer, Markus Michael Rau |
| 2017 | EPIA | Exploring Resampling with Neighborhood Bias on Imbalanced Regression Problems. | Paula Branco, Lus Torgo, Rita P. Ribeiro |
| 2017 | PAKDD | Relevance-Based Evaluation Metrics for Multi-class Imbalanced Domains. | Paula Branco, Lus Torgo, Rita P. Ribeiro |
| 2015 | EPIA | An Experimental Study on Predictive Models Using Hierarchical Time Series. | Ana M. Silva, Rita P. Ribeiro, Joo Gama |
| 2014 | DIS | Failure Prediction - An Application in the Railway Industry. | Pedro Mota Pereira, Rita P. Ribeiro, Joo Gama |
| 2013 | EPIA | SMOTE for Regression. | Lus Torgo, Rita P. Ribeiro, Bernhard Pfahringer, Paula Branco |
| 2012 | ICDM | Towards Utility Maximization in Regression. | Rita P. Ribeiro |
| 2010 | ECAI | Interval Forecast of Water Quality Parameters. | Orlando Ohashi, Lus Torgo, Rita P. Ribeiro |
| 2009 | DIS | Precision and Recall for Regression. | Lus Torgo, Rita P. Ribeiro |
| 2006 | DIS | Rule-Based Prediction of Rare Extreme Values. | Rita P. Ribeiro, Lus Torgo |
| 2006 | PAKDD | Predicting Rare Extreme Values. | Lus Torgo, Rita P. Ribeiro |
| 2003 | EPIA | Predicting Harmful Algae Blooms. | Rita P. Ribeiro, Lus Torgo |