| 2026 | ICCBR | Keep Adaptation Simple: Implicit vs. Explicit Adaptation for LLM-Based CBR. | Ravi Regulagedda, Nischal Bangalore Krupashankar, David Leake |
| 2026 | ICCBR | Hallucinations Considered Helpful: Increasing Case Base Competence with LLM-Hallucinated Cases. | Kaitlynne Wilkerson, David Leake |
| 2026 | ICCBR | Combining Deep Learning and Large Language Models for Retrieval-Based Image Classification. | Zachary Wilkerson, David Leake, David Crandall |
| 2025 | ICCBR | Levels of AI Memory - And Case-Based Ways for LLMs to Ascend Them. | Michael W. Floyd, David Leake, David H. Mnager, Ian D. Watson, Kaitlynne Wilkerson |
| 2025 | ICCBR | Learning Case Features with Proxy-Guided Deep Neural Networks. | Vibhas Vats, Zachary Wilkerson, Hiroki Sato, David Leake, David Crandall |
| 2025 | ICCBR | Case Hallucinations and Steps Toward Repair. | Kaitlynne Wilkerson, David Leake |
| 2025 | ICCBR | Extracting Features with Deep Learning for Ensemble-Driven Case-Based Classification. | Zachary Wilkerson, David Leake, David Crandall, Benjamin Wilkerson |
| 2025 | IJCAI | EnergyCompress: A General Case Base Learning Strategy. | Fadi Badra, Esteban Marquer, Marie-Jeanne Lesot, Miguel Couceiro, David Leake |
| 2025 | IJCAI | Run Like a Neural Network, Explain Like k-Nearest Neighbor. | Xiaomeng Ye, David Leake, Yu Wang, David Crandall |
| 2024 | ICCBR | On Implementing Case-Based Reasoning with Large Language Models. | Kaitlynne Wilkerson, David Leake |
| 2024 | ICCBR | Extracting Indexing Features for CBR from Deep Neural Networks: A Transfer Learning Approach. | Zachary Wilkerson, David Leake, Vibhas Vats, David Crandall |
| 2024 | ICCBR | Towards Network Implementation of CBR: Case Study of a Neural Network K-NN Algorithm. | Xiaomeng Ye, David Leake, Yu Wang, Ziwei Zhao, David Crandall |
| 2023 | ICCBR | Cases Are King: A User Study of Case Presentation to Explain CBR Decisions. | Lawrence Gates, David Leake, Kaitlynne Wilkerson |
| 2023 | ICCBR | Towards Addressing Problem-Distribution Drift with Case Discovery. | David Leake, Brian Schack |
| 2023 | ICCBR | Examining the Impact of Network Architecture on Extracted Feature Quality for CBR. | David Leake, Zachary Wilkerson, Vibhas Vats, Karan Acharya, David J. Crandall |
| 2023 | ICCBR | Less is Better: An Energy-Based Approach to Case Base Competence. | Esteban Marquer, Fadi Badra, Marie-Jeanne Lesot, Miguel Couceiro, David Leake |
| 2022 | ICCBR | Case-based Explanation: Making the Implicit Explicit. | David Leake |
| 2022 | ICCBR | Extracting Case Indices from Convolutional Neural Networks: A Comparative Study. | David Leake, Zachary Wilkerson, David Crandall |
| 2022 | ICCBR | Leveraging SHAP and CBR for Dimensionaltiy Reduction on the Psychology Prediction Dataset. | Zachary Wilkerson, David Leake, David Crandall |
| 2022 | ICCBR | Case Adaptation with Neural Networks: Capabilities and Limitations. | Xiaomeng Ye, David Leake, David Crandall |
| 2022 | ICCBR | Generating Counterfactual Images: Towards a C2C-VAE Approach. | Ziwei Zhao, David Leake, Xiaomeng Ye, David J. Crandall |
| 2021 | ICCBR | Evaluating CBR Explanation Capabilities: Survey and Next Steps. | Lawrence Gates, David Leake |
| 2021 | ICCBR | Harmonizing Case Retrieval and Adaptation with Alternating Optimization. | David Leake, Xiaomeng Ye |
| 2021 | ICCBR | On Combining Knowledge-Engineered and Network-Extracted Features for Retrieval. | Zachary Wilkerson, David Leake, David J. Crandall |
| 2021 | ICCBR | Learning Adaptations for Case-Based Classification: A Neural Network Approach. | Xiaomeng Ye, David Leake, Vahid Jalali, David J. Crandall |
| 2020 | ICCBR | On Bringing Case-Based Reasoning Methodology to Deep Learning. | David Leake, David J. Crandall |
| 2020 | ICCBR | Learning to Improve Efficiency for Adaptation Paths. | David Leake, Xiaomeng Ye |
| 2020 | ICCBR | Applying Class-to-Class Siamese Networks to Explain Classifications with Supportive and Contrastive Cases. | Xiaomeng Ye, David Leake, William Huibregtse, Mehmet M. Dalkilic |
| 2019 | ICCBR | CBR Confidence as a Basis for Confidence in Black Box Systems. | Lawrence Gates, Caleb Kisby, David Leake |
| 2019 | ICCBR | On Combining Case Adaptation Rules. | David Leake, Xiaomeng Ye |
| 2019 | IJCAI | Unsupervised Hierarchical Temporal Abstraction by Simultaneously Learning Expectations and Representations. | Katherine Metcalf, David Leake |
| 2018 | ICCBR | Harnessing Hundreds of Millions of Cases: Case-Based Prediction at Industrial Scale. | Vahid Jalali, David Leake |
| 2018 | ICCBR | Exploration vs. Exploitation in Case-Base Maintenance: Leveraging Competence-Based Deletion with Ghost Cases. | David Leake, Brian Schack |
| 2018 | ICCBR | Embedded Word Representations for Rich Indexing: A Case Study for Medical Records. | Katherine Metcalf, David Leake |
| 2017 | AAAI | Knowledge-Based Morphological Classification of Galaxies from Vision Features. | Devendra Singh Dhami, David Leake, Sriraam Natarajan |
| 2017 | CogSci | Modelling Unsupervised Event Segmentation: Learning Event Boundaries from Prediction Errors. | Katherine Metcalf, David Leake |
| 2017 | ICCBR | Scaling Up Ensemble of Adaptations for Classification by Approximate Nearest Neighbor Retrieval. | Vahid Jalali, David Leake |
| 2017 | ICCBR | Maintenance for Case Streams: A Streaming Approach to Competence-Based Deletion. | Yang Zhang, Su Zhang, David Leake |
| 2017 | IJCAI | Learning and Applying Case Adaptation Rules for Classification: An Ensemble Approach. | Vahid Jalali, David Leake, Najmeh Forouzandehmehr |
| 2016 | ICCBR | Adaptation-Guided Feature Deletion: Testing Recoverability to Guide Case Compression. | David Leake, Brian Schack |
| 2016 | ICCBR | Case-Base Maintenance: A Streaming Approach. | Yang Zhang, Su Zhang, David Leake |
| 2015 | ICCBR | Flexible Feature Deletion: Compacting Case Bases by Selectively Compressing Case Contents. | David Leake, Brian Schack |
| 2015 | ICDM | Transfer Learning via Relational Type Matching. | Raksha Kumaraswamy, Phillip Odom, Kristian Kersting, David Leake, Sriraam Natarajan |
| 2014 | AAAI | Adaptation-Guided Case Base Maintenance. | Vahid Jalali, David Leake |
| 2014 | FlAIRS | An Ensemble Approach to Adaptation-Guided Retrieval. | Vahid Jalali, David Leake |
| 2014 | ICCBR | On Retention of Adaptation Rules. | Vahid Jalali, David Leake |
| 2013 | FlAIRS | An Ensemble Approach to Instance-Based Regression Using Stretched Neighborhoods. | Vahid Jalali, David Leake |
| 2013 | ICCBR | On Deriving Adaptation Rule Confidence from the Rule Generation Process. | Vahid Jalali, David Leake |
| 2013 | ICCBR | Extending Case Adaptation with Automatically-Generated Ensembles of Adaptation Rules. | Vahid Jalali, David Leake |
| 2012 | FlAIRS | Customizing Question Selection in Conversational Case-Based Reasoning. | Vahid Jalali, David Leake |
| 2012 | ICCBR | Custom Accessibility-Based CCBR Question Selection by Ongoing User Classification. | Vahid Jalali, David Leake |
| 2011 | ICCBR | How Many Cases Do You Need? Assessing and Predicting Case-Base Coverage. | David Leake, Mark Wilson |