Search Results for author: Bianca Dumitrascu

Found 9 papers, 5 papers with code

MarkerMap: nonlinear marker selection for single-cell studies

no code implementations28 Jul 2022 Nabeel Sarwar, Wilson Gregory, George A Kevrekidis, Soledad Villar, Bianca Dumitrascu

Single-cell RNA-seq data allow the quantification of cell type differences across a growing set of biological contexts.

Imputation Vocal Bursts Type Prediction

Dimensionless machine learning: Imposing exact units equivariance

1 code implementation2 Apr 2022 Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu

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-consistent dimensional scalings.

BIG-bench Machine Learning Symbolic Regression

Approximate Latent Force Model Inference

1 code implementation24 Sep 2021 Jacob D. Moss, Felix L. Opolka, Bianca Dumitrascu, Pietro Lió

Physically-inspired latent force models offer an interpretable alternative to purely data driven tools for inference in dynamical systems.

Gaussian Processes

Deep learning for bioimage analysis

no code implementations6 Jul 2021 Adrien Hallou, Hannah Yevick, Bianca Dumitrascu, Virginie Uhlmann

Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis.

Nonparametric Bayesian multi-armed bandits for single cell experiment design

1 code implementation11 Oct 2019 Federico Camerlenghi, Bianca Dumitrascu, Federico Ferrari, Barbara E. Engelhardt, Stefano Favaro

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.


Sequential Gaussian Processes for Online Learning of Nonstationary Functions

1 code implementation24 May 2019 Michael Minyi Zhang, Bianca Dumitrascu, Sinead A. Williamson, Barbara E. Engelhardt

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 arrive.

Gaussian Processes Hyperparameter Optimization +3

PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits

no code implementations NeurIPS 2018 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 binary rewards.

Multi-Armed Bandits Thompson Sampling

Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction

1 code implementation27 Mar 2017 Li-Fang Cheng, Gregory Darnell, Bianca Dumitrascu, Corey Chivers, Michael E Draugelis, Kai Li, Barbara E. Engelhardt

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 tests is essential to enable successful medical interventions and improve patient outcomes.

Gaussian Processes Time Series +1

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