Uncertainty Awareness

Uncertainty awareness in machine learning focuses on developing models that not only make predictions but also quantify their own uncertainty, improving reliability and trustworthiness. Current research emphasizes probabilistic methods, such as Bayesian neural networks and quantile regression, alongside non-probabilistic approaches like interval and fuzzy learning, applied across diverse fields including structural dynamics, natural language processing, and reinforcement learning. This research is crucial for building robust and reliable AI systems, particularly in high-stakes applications where understanding the limitations of predictions is paramount, leading to improved decision-making and enhanced safety.