The real-time estimation of time-varying parameters from high-dimensional, heavy-tailed and corrupted data-streams is a common sub-routine in systems ranging from those for network monitoring and anomaly detection to those for traffic scheduling in data-centers.

Object tracking is a fundamental task in computer vision, requiring the localization of objects of interest across video frames.

Considering the complex graph structure, are some nodes consistently well-trained and following this principle even with different graph augmentations?

We consider the problem of extracting a reward function by comparing a decision-making diffusion model that models low-reward behavior and one that models high-reward behavior; a setting related to inverse reinforcement learning. We first define the notion of a relative reward function of two diffusion models and show conditions under which it exists and is unique.

SpeAr, which can further alleviate the bias problem.