ENCP: Episode-Normalized Conformal Prediction for Vision-and-Language Navigation
[Uczenie maszynowe] Autorzy: Vicky Feliren, A. Taufiq Asyhari, Muhamad Risqi U. Saputra
Uncertainty estimation for Vision-Language-Navigation (VLN) models is a critical task since it can help identify ambiguous and unreliable predictions, enabling agents to make safer navigation decisions. As one of the most advanced uncertainty estimation frameworks, conformal prediction (CP) offers a promising approach for uncertainty estimation in VLN. However, given that VLN agent requires a sequence of steps, standard calibration in conformal prediction fails to provide coverage guarantee it promises over a dependent, variable-length VLN episode. To this end, we propose Episode-Normalized Conformal Prediction (ENCP), which rescales a nonconformity score by the policy's residual confidence and calibrates one maximum score per episode. Under exchangeable calibration and test episodes, this construction covers the ground truth at every step with probability at least $1 - α$, while allowi…
- Kategoria arxiv
- cs.LG
- Kategoria
- Uczenie maszynowe