3 papers
cs.CY2026
Fine-tuning LLMs for Tourist Trajectory Prediction using Field Experiment Data
Tatsuya Amano, Hirozumi Yamaguchi
Evaluating mobility interventions at tourist destinations requires predicting visitor behavior under varying conditions. Traditional methods struggle because tourist decisions depe…
cs.LG2026
UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
Shih-Yu Lai, Hirozumi Yamaguchi, Shang-Tse Chen +2
Device-free localization trains models from heterogeneous wireless and visual sensors (e.g., Wi-Fi, LiDAR) distributed across edge devices. Federated learning offers a privacy-resp…
cs.LG2025
MobText-SISA: Efficient Machine Unlearning for Mobility Logs with Spatio-Temporal and Natural-Language Data
Haruki Yonekura, Ren Ozeki, Tatsuya Amano +2
Modern mobility platforms have stored vast streams of GPS trajectories, temporal metadata, free-form textual notes, and other unstructured data. Privacy statutes such as the GDPR r…