6 accepted papers
Conformal prediction converts point predictions into set-valued predictions with coverage guarantees under exchangeability between calibration and deployment data. We study *conformal calibration transfer*, where this requirement fails because labeled calibration is available only in a source space,…
AI-assisted decision-making is subject to AI model uncertainty. Prior works proposed to make this uncertainty explicit for increasing trust and transparency, but its behavioral role was rarely treated. This position paper argues, from a game-theoretic perspective, that human–AI decision support shou…
Effective communication is vital in emergency response scenarios where clarity and speed can save lives. Traditional systems often struggle under the chaotic conditions of real-world emergencies, leading to breakdowns in communication and task management. This paper introduces CLEAR-Command, a syste…
We introduce Knowledge-Refined Prediction Sets (KRPS), a novel approach that performs semantically-aware uncertainty quantification for multitask-based autonomous perception in urban environments. KRPS extends conformal prediction (CP) to ensure 2 properties not typically addressed by CP frameworks:…
Recent advancements in deep learning have significantly enhanced the development of efficient models for multi-modal path prediction within urban environments, offering approaches to navigate complex environments accurately. Despite their performance, models grounded in deep learning techniques freq…
The annotation of 3D point cloud datasets is an expensive and tedious task. To optimize the annotation process, recent works have proposed the use of environments with higher levels of immersion in combination with different types of visual aids. However, two problems remain unresolved. First, the p…