Hence, the following research questions arise: What is the optimal regret lower bound in contextual MNL bandits?

Bayesian brain theory suggests that the brain employs generative models to understand the external world.

However, some critical aspects of it, such as the relationship between superconductivity and materials' chemical/structural features, still need to be understood. Recent successes of data-driven approaches in material science strongly inspire researchers to study this relationship with them, but a corresponding dataset is still lacking.

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.

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

Unless stated otherwise we use American English for transcriptions.

Deep learning based neural decoding from stereotactic electroencephalography (sEEG) would likely benefit from scaling up both dataset and model size. To achieve this, combining data across multiple subjects is crucial.