Consider the domain of multiclass classification within the adversarial online setting. What is the price of relying on bandit feedback as opposed to full information? To what extent can an adaptive adversary amplify the loss compared to an oblivious one? To what extent can a randomized learner reduce the loss compared to a deterministic one?

Meshes are surface discretisations intentionally designed to represent the geometry of 3D objects.

Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning.

We show that it provides significant benefits to RL agents on a suite of gridworld, CARL and MetaWorld tasks.

A common practice to address this issue is using the model itself to annotate unlabeled data samples.

However, such architectures often fail to deliver solid predictive performance on real-world tasks, limiting their practical impact. In contrast, global attention-based models such as graph transformers demonstrate strong performance in practice.