Search Results for author:
Found 8 papers, 7 papers with code
Adversarial Time-to-Event Modeling
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.
Survival Function Matching for Calibrated 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.
Survival Cluster Analysis
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.
Enabling Counterfactual Survival Analysis with Balanced Representations
Balanced representation learning methods have been applied successfully to counterfactual inference from observational data.
NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing
Semantic hashing has become a powerful paradigm for fast similarity search in many information retrieval systems.
Flexible Triggering Kernels for Hawkes Process Modeling
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.
Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations
Understanding the effects of these system inputs on system outputs is crucial to have any meaningful model of a dynamical system.
AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires
Recent advances in immunomics have shown that T-cell receptor (TCR) signatures can accurately predict active or recent infection by leveraging the high specificity of TCR binding to disease antigens.
