Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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publications
AlphaD3M: Machine Learning Pipeline Synthesis
Published in ICML Workshop on Automated Machine Learning, 2018
Machine learning pipeline synthesis for automated machine learning.
Recommended citation: Drori, I., Krishnamurthy, Y., Rampin, R., Lourenço, R. de P., Ono, J. P., Cho, K., Silva, C., & Freire, J. (2018). "AlphaD3M: Machine Learning Pipeline Synthesis." ICML Workshop on Automated Machine Learning.
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
Published in ICML Workshop on Automated Machine Learning, 2019
Using reinforcement learning and grammars for automatic machine learning pipeline synthesis.
Recommended citation: Drori, I., Krishnamurthy, Y., Lourenço, R. de P., Rampin, R., Cho, K., Silva, C., & Freire, J. (2019). "Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar." ICML Workshop on Automated Machine Learning.
Debugging Machine Learning Pipelines
Published in ACM SIGMOD Workshop on Data Management for End-to-End Machine Learning, 2019
Approaches for debugging machine learning pipelines in practice.
Recommended citation: Lourenço, R., Freire, J., & Shasha, D. (2019). "Debugging Machine Learning Pipelines." ACM SIGMOD Workshop on Data Management for End-to-End Machine Learning.
BugDoc: Algorithms to Debug Computational Processes
Published in ACM SIGMOD 2020, 2020
Algorithms for debugging computational processes in machine learning pipelines.
Recommended citation: Lourenço, R., Freire, J., & Shasha, D. (2020). "BugDoc: Algorithms to Debug Computational Processes." ACM SIGMOD.
BugDoc: A System for Debugging Computational Pipelines
Published in ACM SIGMOD (demo), 2020
A demonstration of BugDoc, a system for debugging computational pipelines.
Recommended citation: Lourenço, R., Freire, J., & Shasha, D. (2020). "BugDoc: A System for Debugging Computational Pipelines." ACM SIGMOD (demo).
DataPrism: Exposing Disconnect between Data and Systems
Published in ACM SIGMOD 2022, 2022
A system for exposing inconsistencies and disconnects between data specifications and system implementations.
Recommended citation: Galhotra, S., Fariha, A., Lourenço, R., Freire, J., Meliou, A., & Srivastava, D. (2022). "DataPrism: Exposing Disconnect between Data and Systems." ACM SIGMOD.
BugDoc: Iterative Debug and Explanation of Pipeline Executions
Published in VLDB Journal, 2022
A system for debugging computational pipelines through iterative debugging and explanation of execution traces.
Recommended citation: Lourenço, R., Freire, J., Simon, E., Weber, G., & Shasha, D. (2022). "BugDoc: Iterative Debug and Explanation of Pipeline Executions." VLDB Journal.
AlphaD3M: An Open-Source AutoML Library for Multiple ML Tasks
Published in AutoML Conference (ABCD Track), 2023
An open-source AutoML library supporting multiple machine learning tasks.
Recommended citation: Lopez, R., Lourenço, R., Rampin, R., Castelo, S., Santos, A. S. R., Ono, J. H. P., Silva, C., & Freire, J. (2023). "AlphaD3M: An Open-Source AutoML Library for Multiple ML Tasks." AutoML Conference (ABCD Track).
Decision support model for time series data augmentation method selection
Published in IEEE Access, 2024
A decision support model for selecting appropriate time series data augmentation methods.
Recommended citation: Joubaud, D., Kubler, S., Lourenço, R., Cordy, M., & Le Traon, Y. (2024). "Decision support model for time series data augmentation method selection." IEEE Access.
Through the Lens of Explainability: Enhancing Trust in Remaining Useful Life Prognosis Models
Published in International Conference on Flexible Automation and Intelligent Manufacturing, 2024
Enhancing trust in remaining useful life prognosis models through explainability techniques.
Recommended citation: Benguessoum, K., Lourenço, R., Bourel, V., & Kubler, S. (2024). "Through the Lens of Explainability: Enhancing Trust in Remaining Useful Life Prognosis Models." International Conference on Flexible Automation and Intelligent Manufacturing, Springer, Cham.
Counterfactual Explainable AI (XAI) Method for Deep Learning-Based Multivariate Time Series Classification
Published in The 40th Annual AAAI Conference on Artificial Intelligence (Main track), 2026
A counterfactual XAI method for explaining deep learning models in multivariate time series classification tasks.
Recommended citation: Cetina, A. P., Benguessoum, K., Lourenço, R., & Kubler, S. (2026). "Counterfactual Explainable AI (XAI) Method for Deep Learning-Based Multivariate Time Series Classification." The 40th Annual AAAI Conference on Artificial Intelligence (Main track).
Towards Trustworthy Human-Centered Manufacturing: A Decision Support Tool for {Model x Explainer} Selection
Published in AAAI International Workshop on Addressing Challenges and Opportunities in Human-Centric Manufacturing, 2026
A decision support tool for selecting model and explainer combinations in human-centered manufacturing systems.
Recommended citation: Benguessoum, K., Lourenço, R., Kubler, S., & Le Traon, Y. (2026). "Towards Trustworthy Human-Centered Manufacturing: A Decision Support Tool for {Model x Explainer} Selection." AAAI International Workshop on Addressing Challenges and Opportunities in Human-Centric Manufacturing.