Following the convention of RS, existing methods exploit unique user representation in their model design.

Offline reinforcement learning (RL) aims to learn policies from pre-existing datasets without further interactions, making it a challenging task. Q-learning algorithms struggle withextrapolation errors inofflinesettings, while supervised learning methods are constrained by model expressiveness.

We also nuance the idea that learning happens at the edge of chaos by giving evidence that avery desirable feature forneural networks isthehyperbolicity of their Jacobian at initialization.

Deceptive agents are a challenge for the safety, trustworthiness, and cooperation of AI systems.

SGMs have been primarily applied to data living on Euclidean spaces, i.e. manifolds with flat geometry.

Gaussian noise to data, and a generative model, which entails a "denoising" process defined by approximating the time-reversal of the diffusion.