Prof. David B. Leake
Luddy School of Informatics, Computing, and Engineering
Indiana University, USA
Email: [email protected]
Qualifications
1990 Ph.D., Yale University, USA, computer science
1985 M.Sc., Yale University, USA, computer science
1984 M.A., Brown University, USA, mathematics
Publications (Selected)
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Ye, X., Leake, D., Wang, Y., et al. (2025). Run like a neural network, explain like k-nearest neighbor. Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI 2025). Advance online publication.
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Badra, F., Marquer, E., Lesot, M. J., et al. (2025). EnergyCompress: A General Case Base Learning Strategy. 34th International Joint Conference on Artificial Intelligence (IJCAI 2025).
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Vats, V., Wilkerson, Z., Sato, H., et al. (2025). Learning Case Features with Proxy-Guided Deep Neural. Case-Based Reasoning Research and Development: 33rd International Conference, ICCBR 2025.
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Vats, V., Wilkerson, Z., Sato, H., et al. (2025). Learning Case Features with Proxy-Guided Deep Neural Networks. International Conference on Case-Based Reasoning, 313–327.
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Wilkerson, Z., Leake, D., Crandall, D., et al. (2025). Extracting Features with Deep Learning for Ensemble-Driven Case-Based Classification. International Conference on Case-Based Reasoning, 345–359.
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Bach, K., Bergmann, R., Brand, F., et al. (2025). Case-Based Reasoning Meets Large Language Models: A Research Manifesto For Open Challenges and Research Directions. International Conference on Case-Based Reasoning, 7–21.
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Wilkerson, K., & Leake, D. (2025). Case Hallucinations and Steps Toward Repair. ICCBR 2025 Workshops.
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Floyd, M. W., Leake, D., Ménager, D. H., et al. (2025). Levels of AI Memory—And Case-Based Ways for LLMs to Ascend Them. AI Magazine, 46(1), 22–35.
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Ye, X., Leake, D., Wang, Y., et al. (2024). Learning Analogies between Classes to Create Counterfactual Explanations. Workshop on Analogical Reasoning and Machine Learning (IARML 2024).
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Zhao, Z., Leake, D., Ye, X., et al. (2024). Case-Enhanced Vision Transformer: Improving Explanations of Image Similarity with a ViT-based Similarity Metric. arXiv preprint arXiv:2407.16981.
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Wilkerson, K., & Leake, D. (2024). On implementing case-based reasoning with large language models. International Conference on Case-Based Reasoning, 404–417.
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Ye, X., Leake, D., Wang, Y., et al. (2024). Towards network implementation of cbr: Case study of a neural network k-nn algorithm. International Conference on Case-Based Reasoning, 354–370.
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Wilkerson, Z., Leake, D., Vats, V., et al. (2024). Extracting indexing features for CBR from deep neural networks: A transfer learning approach. International Conference on Case-Based Reasoning, 143–158.
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Granger, R., Leake, D., & Riesbeck, C. K. (2023). In Memoriam: Roger C. Schank, 1946–2023. AI Magazine, 44(3), 343–344.
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Leake, D., Wilkerson, Z., Ye, X., et al. (2023). Enhancing Case-Based Reasoning with Neural Networks. Compendium of Neurosymbolic Artificial Intelligence, 369–387.
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Leake, D. (2023). Bridging ai paradigms with cases and networks. Computer Sciences & Mathematics Forum, 8(1), Article 71.
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Leake, D., Wilkerson, Z., Vats, V., et al. (2023). Examining the impact of network architecture on extracted feature quality for CBR. International Conference on Case-Based Reasoning, 3–18.
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Marquer, E., Badra, F., Lesot, M. J., et al. (2023). Less is better: An energy-based approach to case base competence. ICCBR ATA’23: Workshop on Analogies: From Theory to Applications.
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Hammond, K. J., & Leake, D. B. (2023). Large Language Models Need Symbolic AI. International Workshop on Neuro-Symbolic Learning and Reasoning (NeSy), 204–209.
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Gates, L., Leake, D., & Wilkerson, K. (2023). Cases are King: A User Study of Case Presentation to Explain CBR Decisions. Case-Based Reasoning Research and Development: 31st International Conference, 22–37.
Profile Details
https://homes.luddy.indiana.edu/leake/
https://scholar.google.com/citations?user=CidT-JAAAAAJ&hl=en
https://www.researchgate.net/profile/David-Leake-2
WOS ResearcherID: PFZ-1346-2026