Molecular representation learning lays the foundation for drug discovery. However, existing methods suffer from poor out-of-distribution (OOD) generalization, particularly when data for training and testing originate from different environments.

It is unclear whether distributed errors alone are sufficient for synapses to make coordinated updates to learn complex, nonlinear reward-based learning tasks.

Multilevel Monte-Carlo (MLMC) technique and establish its sample and computational complexities.

To leverage the complementary merits of both worlds, we propose BetterDepth to achieve geometrically correct affine-invariant MDE while capturing fine details.

Much remains to be understood, however, in statistical learning under distribution shifts. This paper focuses on a distribution shift setting where train and test distributions can be related by classes of (data) transformation maps.

These theories support making causal and moral judgments. When something bad happens, we naturally ask: who did what, and why?

Unless stated otherwise we use American English for transcriptions.

That which we call a rose by any other name would smell as sweet."