Temporal Domain Generalization (TDG) addresses the challenge of training predictive models under temporally varying data distributions. Traditional TDG approaches typically focus on domain data collected at fixed, discrete time intervals, which limits their capability to capture the inherent dynamics within continuous-evolving and irregularly-observed temporal domains.

Neural Information Processing Systems http://nips.cc/

Our approach builds upon variational autoencoder (V AE) models for sequential data and combines them with recent work on neural image compression.

Model interpretability is an increasingly important component of practical machine learning. Some ofthemost common forms ofinterpretability systems are example-based, local, and global explanations. One of the main challenges in interpretability isdesigning explanation systems thatcancapture aspects ofeach of these explanation types, in order to develop a more thorough understanding of the model. We address this challenge in a novel model called MAPLE that useslocallinearmodeling techniques alongwithadualinterpretation ofrandom forests (both as a supervised neighborhood approach and as a feature selection method).

Wedemonstrate theproposed methodology onahypertension dataset,showingthatourprescribed treatment leadstoalarger reduction in the systolic blood pressure compared to a series of alternatives.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

The meta model seeks to answer "what would the outcome be if we were to deploy a different