Confidence Estimation

Confidence estimation in machine learning aims to quantify a model's certainty in its predictions, improving reliability and trustworthiness, particularly in high-stakes applications. Current research focuses on developing and comparing methods for estimating confidence, often leveraging techniques like Monte Carlo dropout, variational autoencoders, and Bayesian approaches, and exploring their application across diverse model architectures including LLMs and GNNs. This field is crucial for building robust and dependable AI systems, enabling informed decision-making in areas such as healthcare, legal proceedings, and autonomous systems where accurate uncertainty quantification is paramount. Improving confidence estimation is a key challenge for advancing the reliability and safety of AI.