| 2026 | FlAIRS | Learning Team Synergy from Team Composition with a Siamese Transformer. | Xiaomeng Ye, Elliot Mayo, Joseph Cuthbert, Anthony Head |
| 2025 | FlAIRS | Fully Interpretable and Adjustable Model for Depression Diagnosis: A Qualitative Approach. | Kuo Deng, Xiaomeng Ye, Kun Wang, Angelina Pennino, Abigail Jarvis, Yola Hall |
| 2025 | ICCV | EmbodiedSplat: Personalized Real-To-Sim-To-Real Navigation with Gaussian Splats From a Mobile Device. | Gunjan Chhablani, Xiaomeng Ye, Muhammad Zubair Irshad, Zsolt Kira |
| 2025 | IJCAI | Run Like a Neural Network, Explain Like k-Nearest Neighbor. | Xiaomeng Ye, David Leake, Yu Wang, 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 |
| 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 | Harmonizing Case Retrieval and Adaptation with Alternating Optimization. | David Leake, Xiaomeng Ye |
| 2021 | ICCBR | Learning Adaptations for Case-Based Classification: A Neural Network Approach. | Xiaomeng Ye, David Leake, Vahid Jalali, 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 | FlAIRS | C2C Trace Retrieval: Fast Classification Using Class-to-Class Weighting. | Xiaomeng Ye |
| 2019 | ICCBR | On Combining Case Adaptation Rules. | David Leake, Xiaomeng Ye |
| 2018 | FlAIRS | The Enemy of My Enemy Is My Friend: Class-to-Class Weighting in K-Nearest Neighbors Algorithm. | Xiaomeng Ye |