Online Distributional Prediction via Latent Cluster Geometry Under Drift and Corruption

Online learning in non-stationary streams is often formulated as tracking a point estimate, but many applications require predicting the full data-generating distribution. We study online distributional prediction under drift and adversarial corruption. Our approach represents each candidate law thr...