Sitemap
A list of all the posts and pages found on the site. For you robots out there, 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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portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
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publications
Ontology Alignment Using Inductive Logic Programming
Published in International Conference on Web Research (ICWR), IEEE, 2018
Mapping ontologies using inverse resolution in inductive logic programming.
Recommended Citation: Karimi, H., & Kamandi, A. (2018). “Ontology Alignment Using Inductive Logic Programming.” In 4th International Conference on Web Research (ICWR), 118–127. IEEE.
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YAD: A Learning-based Inductive Logic Programming Tool
Published in Journal of Open Source Software (JOSS), 2018
An open-source, Inductive Logic Programming tool for multi-tabular and multi-dimensional learning based on inverse resolution.
Recommended Citation: Kamandi, A., & Karimi, H. (2018). “YAD: A Learning-based Inductive Logic Programming Tool.” Journal of Open Source Software (JOSS), 3(30), 892.
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Application-Based Review of Recent Advances of Data Mining in Healthcare
Published in Journal of Biostatistics and Epidemiology (JBE), 2019
Review of data mining advances in healthcare with an application-based perspective.
Recommended Citation: Shirazi, S., Baziyad, H., & Karimi, H. (2019). “An Application-Based Review of Recent Advances of Data Mining in Healthcare.” Journal of Biostatistics and Epidemiology, 5(4).
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A Learning-based Ontology Alignment Approach Using Inductive Logic Programming
Published in Journal of Expert Systems with Applications (ESWA), Elsevier, 2019
Ontology alignment via learning with Inductive Logic Programming (ILP).
Recommended Citation: Karimi, H., & Kamandi, A. (2019). “A Learning-based Ontology Alignment Approach Using Inductive Logic Programming.” Journal of Expert Systems with Applications (ESWA), Elsevier, 125(C), 412–424.
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Quantifying Deep Learning Model Uncertainty in Conformal Prediction
Published in AAAI Symposium Series (Human AI), 2023
Probabilistic quantification of uncertainty derived from conformal prediction sets.
Recommended Citation: Karimi, H., & Samavi, R. (2023). “Quantifying Deep Learning Model Uncertainty in Conformal Prediction.” Proceedings of the AAAI Symposium Series, 1(1), 142–148.
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DENL: Diverse Ensemble and Noisy Logits for Improved Robustness of Neural Networks
Published in Asian Conference on Machine Learning (ACML) - PMLR, 2024
Two-phase training with diversified ensembles and noisy logits for improved adversarial robustness.
Recommended Citation: Yazdani, M., Karimi, H., & Samavi, R. (2024). “DENL: Diverse Ensemble and Noisy Logits for Improved Robustness of Neural Networks.” In Proceedings of the 15th Asian Conference on Machine Learning. Proceedings of Machine Learning Research (PMLR), vol. 222, 1574–1589.
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Evidential Uncertainty Sets in Deep Classifiers Using Conformal Prediction
Published in Symposium on Conformal and Probabilistic Prediction with Applications (COPA), PMLR, 2024
Evidential Conformal Prediction (ECP) for adaptive prediction sets with coverage guarantees.
Recommended Citation: Karimi, H., & Samavi, R. (2024). “Evidential Uncertainty Sets in Deep Classifiers Using Conformal Prediction.” In Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications. Proceedings of Machine Learning Research (PMLR), vol. 230, 466–489.
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SACP: Spatially-Adaptive Conformal Prediction in Uncertainty Quantification of Medical Image Segmentation
Published in Medical Imaging with Deep Learning (MIDL), PMLR, 2025
Spatially-adaptive conformal prediction for anatomically informed uncertainty sets in medical image segmentation.
Recommended Citation: Bereska, J. I., Karimi, H., & Samavi, R. (2025). “SACP: Spatially-Adaptive Conformal Prediction in Uncertainty Quantification of Medical Image Segmentation.” In Medical Imaging with Deep Learning (MIDL 2025). Published in Proceedings of Machine Learning Research (PMLR).
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talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.