3 papers
cs.CV2026
Transferring Visual Explainability of Self-Explaining Models to Prediction-Only Models without Additional Training
Yuya Yoshikawa, Ryotaro Shimizu, Takahiro Kawashima +1
In image classification scenarios where both prediction and explanation efficiency are required, self-explaining models that perform both tasks in a single inference are effective.…
cs.LG2025
Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation
Ryotaro Shimizu, Takashi Wada, Yu Wang +9
Recent research on explainable recommendation generally frames the task as a standard text generation problem, and evaluates models simply based on the textual similarity between t…
cs.LG2025
Explaining Black-box Model Predictions via Two-level Nested Feature Attributions with Consistency Property
Yuya Yoshikawa, Masanari Kimura, Ryotaro Shimizu +1
Techniques that explain the predictions of black-box machine learning models are crucial to make the models transparent, thereby increasing trust in AI systems. The input features…