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
Customer Lifetime Value Prediction with Uncertainty Estimation Using Monte Carlo Dropout
Xinzhe Cao, Yadong Xu, Xiaofeng Yang
Improving Medical Diagnostics with Vision-Language Models: Convex Hull-Based Uncertainty Analysis
Ferhat Ozgur Catak, Murat Kuzlu, Taylor Patrick
Uncertainty-Aware Regularization for Image-to-Image Translation
Anuja Vats, Ivar Farup, Marius Pedersen, Kiran Raja