Anna Goldenberg

Orcid: 0000-0002-2416-833X

Affiliations:
  • University of Toronto, ON, Canada


According to our database1, Anna Goldenberg authored at least 72 papers between 2001 and 2024.

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Bibliography

2024
Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Waveform Data.
CoRR, 2024

Measurement Scheduling for ICU Patients with Offline Reinforcement Learning.
CoRR, 2024

Learning from Time Series under Temporal Label Noise.
CoRR, 2024

Integrate Any Omics: Towards genome-wide data integration for patient stratification.
CoRR, 2024

2023
Maintaining Stability and Plasticity for Predictive Churn Reduction.
CoRR, 2023

RiskFix: Supporting Expert Validation of Predictive Timeseries Models in High-Intensity Settings.
Proceedings of the 25th Eurographics Conference on Visualization, 2023

Moment-alignment domain adaptation in the few-shot and low-resource context.
Proceedings of the 19th International Symposium on Medical Information Processing and Analysis, 2023

Dynamic Interpretable Change Point Detection for Physiological Data Analysis.
Proceedings of the Machine Learning for Health, 2023

What's fair is... fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning: JustEFAB.
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

Childhood Adversity's Impact on Dynamic Mental Health During and Post Pregnancy.
Proceedings of the Workshop on Data Science Techniques for Datasets on Mental and Neurodegenerative Disorders co-located with SDS23 IEEE Swiss Conference on Data Science(IEEESDS'23), 2023

2022
The promise of machine learning applications in solid organ transplantation.
npj Digit. Medicine, 2022

Better Understanding of the Metamorphosis of Pregnancy (BUMP): protocol for a digital feasibility study in women from preconception to postpartum.
npj Digit. Medicine, 2022

The silent trial - the bridge between bench-to-bedside clinical AI applications.
Frontiers Digit. Health, 2022

An integration engineering framework for machine learning in healthcare.
Frontiers Digit. Health, 2022

From Single-Visit to Multi-Visit Image-Based Models: Single-Visit Models are Enough to Predict Obstructive Hydronephrosis.
CoRR, 2022

Time-Varying Correlation Networks for Interpretable Change Point Detection.
CoRR, 2022

Considerations for Visualizing Uncertainty in Clinical Machine Learning Models.
CoRR, 2022

Error Amplification When Updating Deployed Machine Learning Models.
Proceedings of the Machine Learning for Healthcare Conference, 2022

NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Volume-based Performance not Guaranteed by Promising Patch-based Results in Medical Imaging.
Proceedings of the Proceedings on "I Can't Believe It's Not Better!, 2022

Learning Unsupervised Representations for ICU Timeseries.
Proceedings of the Conference on Health, Inference, and Learning, 2022

How to validate Machine Learning Models Prior to Deployment: Silent trial protocol for evaluation of real-time models at ICU.
Proceedings of the Conference on Health, Inference, and Learning, 2022

Decoupling Local and Global Representations of Time Series.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Extracting Clinician's Goals by What-if Interpretable Modeling.
CoRR, 2021

3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI.
CoRR, 2021

How Interpretable and Trustworthy are GAMs?
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding.
Proceedings of the 9th International Conference on Learning Representations, 2021

Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings.
Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

Towards Robust Classification Model by Counterfactual and Invariant Data Generation.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

A comprehensive EHR timeseries pre-training benchmark.
Proceedings of the ACM CHIL '21: ACM Conference on Health, 2021

2020
Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning.
J. Am. Medical Informatics Assoc., 2020

Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds.
CoRR, 2020

A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data.
CoRR, 2020

Using Generative Models for Pediatric wbMRI.
CoRR, 2020

What went wrong and when? Instance-wise Feature Importance for Time-series Models.
CoRR, 2020

What went wrong and when? Instance-wise feature importance for time-series black-box models.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Preparing a Clinical Support Model for Silent Mode in General Internal Medicine.
Proceedings of the Machine Learning for Healthcare Conference, 2020

Hidden Risks of Machine Learning Applied to Healthcare: Unintended Feedback Loops Between Models and Future Data Causing Model Degradation.
Proceedings of the Machine Learning for Healthcare Conference, 2020

Predicting Obstructive Hydronephrosis Based on Ultrasound Alone.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

2019
Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities.
Inf. Fusion, 2019

Reducing Adversarial Example Transferability Using Gradient Regularization.
CoRR, 2019

The False Positive Control Lasso.
CoRR, 2019

Dr.VAE: improving drug response prediction via modeling of drug perturbation effects.
Bioinform., 2019

What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use.
Proceedings of the Machine Learning for Healthcare Conference, 2019

Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks.
Proceedings of the Machine Learning for Healthcare Conference, 2019

Dynamic Measurement Scheduling for Event Forecasting using Deep RL.
Proceedings of the 36th International Conference on Machine Learning, 2019

Explaining Image Classifiers by Counterfactual Generation.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL.
CoRR, 2018

Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation.
CoRR, 2018

Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples.
CoRR, 2018

Explaining Image Classifiers by Adaptive Dropout and Generative In-filling.
CoRR, 2018

Prediction of Cardiac Arrest from Physiological Signals in the Pediatric ICU.
Proceedings of the Machine Learning for Healthcare Conference, 2018

2017
Vicus: Exploiting local structures to improve network-based analysis of biological data.
PLoS Comput. Biol., 2017

Incorporating networks in a probabilistic graphical model to find drivers for complex human diseases.
PLoS Comput. Biol., 2017

Dropout Feature Ranking for Deep Learning Models.
CoRR, 2017

2016
Safikhani <i>et al</i>. reply.
Nat., 2016

Modeling trajectories of mental health: challenges and opportunities.
CoRR, 2016

PharmacoGx: an R package for analysis of large pharmacogenomic datasets.
Bioinform., 2016

JBASE: Joint Bayesian Analysis of Subphenotypes and Epistasis.
Bioinform., 2016

2015
Subtyping: What It is and Its Role in Precision Medicine.
IEEE Intell. Syst., 2015

Combining exome and gene expression datasets in one graphical model of disease to empower the discovery of disease mechanisms.
CoRR, 2015

2014
Gradient-based Laplacian Feature Selection.
CoRR, 2014

EquiNMF: Graph Regularized Multiview Nonnegative Matrix Factorization.
CoRR, 2014

A probabilistic approach to explore human miRNA targetome by integrating miRNA-overexpression data and sequence information.
Bioinform., 2014

2012
Detecting microRNAs of high influence on protein functional interaction networks: a prostate cancer case study.
BMC Syst. Biol., 2012

Mixture Model for Sub-Phenotyping in GWAS.
Proceedings of the Biocomputing 2012: Proceedings of the Pacific Symposium, 2012

2011
Unsupervised detection of genes of influence in lung cancer using biological networks.
Bioinform., 2011

2009
A Survey of Statistical Network Models.
Found. Trends Mach. Learn., 2009

2006
Exploratory Study of a New Model for Evolving Networks.
Proceedings of the Statistical Network Analysis: Models, Issues, and New Directions, 2006

2005
Bayes net graphs to understand co-authorship networks?
Proceedings of the 3rd international workshop on Link discovery, 2005

2004
Tractable learning of large Bayes net structures from sparse data.
Proceedings of the Machine Learning, 2004

2001
Artificial neural network approach to data analysis and parameter estimation in experimental spectroscopy.
Informatica (Slovenia), 2001


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