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Interpretability

Understanding Stakeholders' Perceptions and Needs Across the LLM Supply Chain
Understanding Stakeholders' Perceptions and Needs Across the LLM Supply Chain
Explainability and transparency of AI systems are undeniably important, leading to several research studies and tools addressing them. …
Conference on Human Factors in Computing Systems (ACM-CHI),  2024.
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Invariant Causal Set Covering Machines
Invariant Causal Set Covering Machines
Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms …
Workshop on Spurious Correlations, Invariance, and Stability (ICML),  2023.
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RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data

Recent metabolomics measurement devices, such as mass spectrometers, produce extremely high-dimensional data. Together with small …

Intelligent Systems for Molecular Biology and European Conference on Computational Biology,  2021.
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Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability
To date, there has been no formal study of the statistical cost of interpretability in machine learning. As such, the discourse around …
ArXiv,  2020.
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Interpretable genotype-to-phenotype classifiers with performance guarantees
Interpretable genotype-to-phenotype classifiers with performance guarantees
Understanding the relationship between the genome of a cell and its phenotype is a central problem in precision medicine. Nonetheless, …
Nature Scientific Reports,  2018.
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