A Reinforcement Learning Framework for Learning Optimal Oxygen Treatments in Patients with Acute Respiratory Distress Syndrome

Radial Neighbors for Provably Accurate Scalable Approximations of Gaussian Processes

BACT: nonparametric Bayesian cell typing for single-cell spatial transcriptomics data

Variational bagging: a robust approach for Bayesian uncertainty quantification

Dependency-aware deep generative models for multitasking analysis of spatial omics data

Error-controlled interaction discovery in generic machine learning models

Sparse Learning for Assessing the Association Between Gut Microbiome and Parkinson’s Disease

A new phylogeny-aware microbiome data generator

Representation Learning for Drug-Target Interaction Prediction

OPERA: An Interpretable Algorithm for Patient Stratification based on Partially Ordered Risk Factors

BELIEF in Dependence: Leveraging Atomic Linearity in Data Bits for Rethinking Generalized Linear Models

Scalable Manifold Learning for Complex Imaging Data Analysis

Deep survival prediction models based on medical images and spatial transcriptomic data

Coarse-grained Hawkes processes

Bayesian inference of spatiotemporal Hawkes processes with Gaussian-process Priors

Likelihood-Free Estimation of Hawkes Processes from Binned Count Data

Latent Network Structure Learning From High-Dimensional Multivariate Point Processes