Search Results for author: Barbara E. Engelhardt

Found 23 papers, 8 papers with code

Active multi-fidelity Bayesian online changepoint detection

1 code implementation26 Mar 2021 Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams

We propose a multi-fidelity approach that makes cost-sensitive decisions about which data fidelity to collect based on maximizing information gain with respect to changepoints.

Edge-computing Time Series

Latent variable modeling with random features

1 code implementation19 Jun 2020 Gregory W. Gundersen, Michael Minyi Zhang, Barbara E. Engelhardt

By approximating a nonlinear relationship between the latent space and the observations with a function that is linear with respect to random features, we induce closed-form gradients of the posterior distribution with respect to the latent variable.

Dimensionality Reduction Latent Variable Models

Nonparametric Deconvolution Models

1 code implementation17 Mar 2020 Allison J. B. Chaney, Archit Verma, Young-suk Lee, Barbara E. Engelhardt

This uniquely allows NDMs both to deconvolve each observation into its constituent factors, and also to describe how the factor distributions specific to each observation vary across observations and deviate from the corresponding global factors.

Variational Inference

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.


Defining Admissible Rewards for High Confidence Policy Evaluation

no code implementations30 May 2019 Niranjani Prasad, Barbara E. Engelhardt, Finale Doshi-Velez

A key impediment to reinforcement learning (RL) in real applications with limited, batch data is defining a reward function that reflects what we implicitly know about reasonable behaviour for a task and allows for robust off-policy evaluation.

Sequential Gaussian Processes for Online Learning of Nonstationary Functions

no code implementations24 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 +1

An Optimal Policy for Patient Laboratory Tests in Intensive Care Units

no code implementations14 Aug 2018 Li-Fang Cheng, Niranjani Prasad, Barbara E. Engelhardt

There exists an inherent trade-off in the selection and timing of lab tests between considerations of the expected utility in clinical decision-making of a given test at a specific time, and the associated cost or risk it poses to the patient.

Decision Making

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

How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

no code implementations30 Oct 2017 Allison J. B. Chaney, Brandon M. Stewart, Barbara E. Engelhardt

Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions.

Recommendation Systems

Large Linear Multi-output Gaussian Process Learning

1 code implementation30 May 2017 Vladimir Feinberg, Li-Fang Cheng, Kai Li, Barbara E. Engelhardt

Gaussian processes (GPs), or distributions over arbitrary functions in a continuous domain, can be generalized to the multi-output case: a linear model of coregionalization (LMC) is one approach.

Gaussian Processes Time Series

A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units

no code implementations20 Apr 2017 Niranjani Prasad, Li-Fang Cheng, Corey Chivers, Michael Draugelis, Barbara E. Engelhardt

The management of invasive mechanical ventilation, and the regulation of sedation and analgesia during ventilation, constitutes a major part of the care of patients admitted to intensive care units.

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

Coupled Compound Poisson Factorization

no code implementations9 Jan 2017 Mehmet E. Basbug, Barbara E. Engelhardt

We present a general framework, the coupled compound Poisson factorization (CCPF), to capture the missing-data mechanism in extremely sparse data sets by coupling a hierarchical Poisson factorization with an arbitrary data-generating model.

Variational Inference

Dynamic Collaborative Filtering with Compound Poisson Factorization

no code implementations17 Aug 2016 Ghassen Jerfel, Mehmet E. Basbug, Barbara E. Engelhardt

Model-based collaborative filtering analyzes user-item interactions to infer latent factors that represent user preferences and item characteristics in order to predict future interactions.

Variational Inference

Hierarchical Compound Poisson Factorization

no code implementations13 Apr 2016 Mehmet E. Basbug, Barbara E. Engelhardt

Here, we introduce hierarchical compound Poisson factorization (HCPF) that has the favorable Gamma-Poisson structure and scalability of HPF to high-dimensional extremely sparse matrices.

Recommendation Systems

Fast moment estimation for generalized latent Dirichlet models

no code implementations17 Mar 2016 Shiwen Zhao, Barbara E. Engelhardt, Sayan Mukherjee, David B. Dunson

We illustrate the utility of our approach on simulated data, comparing results from MELD to alternative methods, and we show the promise of our approach through the application of MELD to several data sets.

Latent Variable Models Variational Inference

Unsupervised Domain Adaptation Using Approximate Label Matching

no code implementations16 Feb 2016 Jordan T. Ash, Robert E. Schapire, Barbara E. Engelhardt

Domain adaptation addresses the problem created when training data is generated by a so-called source distribution, but test data is generated by a significantly different target distribution.

Unsupervised Domain Adaptation

Nonparametric Reduced-Rank Regression for Multi-SNP, Multi-Trait Association Mapping

no code implementations8 Dec 2015 Ashlee Valente, Geoffrey Ginsburg, Barbara E. Engelhardt

Genome-wide association studies have proven to be essential for understanding the genetic basis of disease.

Adaptive Randomized Dimension Reduction on Massive Data

no code implementations13 Apr 2015 Gregory Darnell, Stoyan Georgiev, Sayan Mukherjee, Barbara E. Engelhardt

In this paper we develop an approach for dimension reduction that exploits the assumption of low rank structure in high dimensional data to gain both computational and statistical advantages.

Dimensionality Reduction

Bayesian group latent factor analysis with structured sparsity

1 code implementation11 Nov 2014 Shiwen Zhao, Chuan Gao, Sayan Mukherjee, Barbara E. Engelhardt

Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for an observation matrix with p features across n samples.

Differential gene co-expression networks via Bayesian biclustering models

no code implementations7 Nov 2014 Chuan Gao, Shiwen Zhao, Ian C. McDowell, Christopher D. Brown, Barbara E. Engelhardt

Further, we develop a method to recover gene co-expression networks from the estimated sparse biclustering matrices.

A latent factor model with a mixture of sparse and dense factors to model gene expression data with confounding effects

1 code implementation17 Oct 2013 Chuan Gao, Christopher D. Brown, Barbara E. Engelhardt

To address this problem, we developed a Bayesian sparse latent factor model that uses a three parameter beta prior to flexibly model shrinkage in the loading matrix.

Applications Genomics

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