Uncertainty Estimation
Uncertainty estimation in machine learning aims to quantify the reliability of model predictions, addressing the critical need for trustworthy AI systems. Current research focuses on improving uncertainty quantification across diverse model architectures, including Bayesian neural networks, ensembles, and novel methods like evidential deep learning and conformal prediction, often tailored to specific application domains (e.g., medical imaging, natural language processing). Accurate uncertainty estimation is crucial for responsible AI deployment, enabling better decision-making in high-stakes applications and fostering greater trust in AI-driven outcomes across various scientific and practical fields. This includes identifying unreliable predictions, improving model calibration, and mitigating issues like hallucinations in large language models.
Papers
Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation
Nikolas Koutsoubis, Yasin Yilmaz, Ravi P. Ramachandran, Matthew Schabath, Ghulam Rasool
Enhancing Diagnostic Reliability of Foundation Model with Uncertainty Estimation in OCT Images
Yuanyuan Peng, Aidi Lin, Meng Wang, Tian Lin, Ke Zou, Yinglin Cheng, Tingkun Shi, Xulong Liao, Lixia Feng, Zhen Liang, Xinjian Chen, Huazhu Fu, Haoyu Chen
Just rephrase it! Uncertainty estimation in closed-source language models via multiple rephrased queries
Adam Yang, Chen Chen, Konstantinos Pitas
Just rotate it! Uncertainty estimation in closed-source models via multiple queries
Konstantinos Pitas, Julyan Arbel
Enhancing Active Learning for Sentinel 2 Imagery through Contrastive Learning and Uncertainty Estimation
David Pogorzelski, Peter Arlinghaus, Wenyan Zhang