Ijuice: integer JUstIfied counterfactual explanations.
Mach. Learn., July, 2024
Z-Time: efficient and effective interpretable multivariate time series classification.
Data Min. Knowl. Discov., January, 2024
Castor: Competing shapelets for fast and accurate time series classification.
CoRR, 2024
CounterFair: Group Counterfactuals for Bias Detection, Mitigation and Subgroup Identification.
Proceedings of the IEEE International Conference on Data Mining, 2024
Enhancing Interpretability in Multivariate Time Series Classification through Dimension and Feature Selection.
Proceedings of the KDD Workshop on Human-Interpretable AI 2024 co-located with 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024), 2024
Interpretable and Explainable Time Series Mining.
Proceedings of the 11th IEEE International Conference on Data Science and Advanced Analytics, 2024
Interpretable Caries Development Prediction with Event Intervals.
Proceedings of the 37th IEEE International Symposium on Computer-Based Medical Systems, 2024
Z-Series: Mining and learning from complex sequential data.
PhD thesis, 2023
Distributional Data Augmentation Methods for Low Resource Language.
CoRR, 2023
ORANGE: Opposite-label soRting for tANGent Explanations in heterogeneous spaces.
Proceedings of the 10th IEEE International Conference on Data Science and Advanced Analytics, 2023
Finding Local Groupings of Time Series.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022
JUICE: JUstIfied Counterfactual Explanations.
Proceedings of the Discovery Science - 25th International Conference, 2022
Z-Hist: A Temporal Abstraction of Multivariate Histogram Snapshots.
Proceedings of the Advances in Intelligent Data Analysis XIX, 2021
Automated Grading of Exam Responses: An Extensive Classification Benchmark.
Proceedings of the Discovery Science - 24th International Conference, 2021
Z-Embedding: A Spectral Representation of Event Intervals for Efficient Clustering and Classification.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020
Z-Miner: An Efficient Method for Mining Frequent Arrangements of Event Intervals.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020
Mining Disproportional Frequent Arrangements of Event Intervals for Investigating Adverse Drug Events.
Proceedings of the 33rd IEEE International Symposium on Computer-Based Medical Systems, 2020