22 citations · 78 across the 10 of their papers we have counts for
11 papers
MarkerMap: nonlinear marker selection for single-cell studies
Nabeel Sarwar, Wilson Gregory, George A Kevrekidis +2
Single-cell RNA-seq data allow the quantification of cell type differences across a growing set of biological contexts. However, pinpointing a small subset of genomic features expl…
Dimensionless machine learning: Imposing exact units equivariance
Soledad Villar, Weichi Yao, David W. Hogg +2
Units equivariance (or units covariance) is the exact symmetry that follows from the requirement that relationships among measured quantities of physics relevance must obey self-co…
Approximate Latent Force Model Inference
Jacob D. Moss, Felix L. Opolka, Bianca Dumitrascu +1
Physically-inspired latent force models offer an interpretable alternative to purely data driven tools for inference in dynamical systems. They carry the structure of differential…
Deep learning for bioimage analysis
Adrien Hallou, Hannah Yevick, Bianca Dumitrascu +1
Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage dat…
Nonparametric Bayesian multi-armed bandits for single cell experiment design
Federico Camerlenghi, Bianca Dumitrascu, Federico Ferrari +2
The problem of maximizing cell type discovery under budget constraints is a fundamental challenge for the collection and analysis of single-cell RNA-sequencing (scRNA-seq) data. In…
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes
Li-Fang Cheng, Bianca Dumitrascu, Michael Zhang +4
Multi-output Gaussian processes (GPs) are a flexible Bayesian nonparametric framework that has proven useful in jointly modeling the physiological states of patients in medical tim…
Sequential Gaussian Processes for Online Learning of Nonstationary Functions
Michael Minyi Zhang, Bianca Dumitrascu, Sinead A. Williamson +1
Many machine learning problems can be framed in the context of estimating functions, and often these are time-dependent functions that are estimated in real-time as observations ar…
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits
Bianca Dumitrascu, Karen Feng, Barbara E Engelhardt
We address the problem of regret minimization in logistic contextual bandits, where a learner decides among sequential actions or arms given their respective contexts to maximize b…
BIISQ: Bayesian nonparametric discovery of Isoforms and Individual Specific Quantification
Derek Aguiar, Li-Fang Cheng, Bianca Dumitrascu +3
Most human protein-coding genes can be transcribed into multiple possible distinct mRNA isoforms. These alternative splicing patterns encourage molecular diversity and dysregulatio…
Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction
Li-Fang Cheng, Gregory Darnell, Bianca Dumitrascu +4
In the scenario of real-time monitoring of hospital patients, high-quality inference of patients' health status using all information available from clinical covariates and lab tes…
A Bayesian test to identify variance effects
Bianca Dumitrascu, Gregory Darnell, Julien Ayroles +1
Identifying genetic variants that regulate quantitative traits, or QTLs, is the primary focus of the field of statistical genetics. Most current methods are limited to identifying…