Unseen Test Distribution

Unseen test distribution research focuses on improving machine learning model performance when encountering data significantly different from the training data. Current efforts concentrate on developing robust algorithms and models that generalize well to these unseen distributions, employing techniques like mixture-of-experts, quantile risk minimization, and test-time adaptation methods that leverage self-supervision or prototype alignment. This research is crucial for building reliable and trustworthy AI systems applicable to real-world scenarios where data variability is inevitable, impacting fields ranging from anomaly detection to image synthesis and federated learning.