Understanding the effects of these system inputs on system outputs is crucial to have any meaningful model of a dynamical system.
Recently proposed encoder-decoder structures for modeling Hawkes processes use transformer-inspired architectures, which encode the history of events via embeddings and self-attention mechanisms.
Balanced representation learning methods have been applied successfully to counterfactual inference from observational data.
As a result, there is an unmet need in survival analysis for identifying subpopulations with distinct risk profiles, while jointly accounting for accurate individualized time-to-event predictions.
We present a survival function estimator for probabilistic predictions in time-to-event models, based on a neural network model for draws from the distribution of event times, without explicit assumptions on the form of the distribution.
Semantic hashing has become a powerful paradigm for fast similarity search in many information retrieval systems.
Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice.