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Anna Goldenberg
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- affiliation: University of Toronto, ON, Canada
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2020 – today
- 2024
- [i36]Shihao Ma, Andy G. X. Zeng, Benjamin Haibe-Kains, Anna Goldenberg, John E. Dick, Bo Wang:
Integrate Any Omics: Towards genome-wide data integration for patient stratification. CoRR abs/2401.07937 (2024) - [i35]Sujay Nagaraj, Walter Gerych, Sana Tonekaboni, Anna Goldenberg, Berk Ustun, Thomas Hartvigsen:
Learning from Time Series under Temporal Label Noise. CoRR abs/2402.04398 (2024) - [i34]Zongliang Ji, Anna Goldenberg, Rahul G. Krishnan:
Measurement Scheduling for ICU Patients with Offline Reinforcement Learning. CoRR abs/2402.07344 (2024) - [i33]Sujay Nagaraj, Andrew J. Goodwin, Dmytro Lopushanskyy, Danny Eytan, Robert W. Greer, Sebastian D. Goodfellow, Azadeh Assadi, Anand Jayarajan, Anna Goldenberg, Mjaye L. Mazwi:
Needles in Needle Stacks: Meaningful Clinical Information Buried in Noisy Waveform Data. CoRR abs/2409.00041 (2024) - 2023
- [j19]Sujay Nagaraj
, Sarah M. Goodday, Thomas Hartvigsen
Dissecting the heterogeneity of "in the wild" stress from multimodal sensor data. npj Digit. Medicine 6 (2023) - [c29]Jennifer Yu, Mai Ali, Rui Zhu, Parinita Edke, Anna Goldenberg:
Childhood Adversity's Impact on Dynamic Mental Health During and Post Pregnancy. DSTNDS 2023: 1-17 - [c28]Melissa D. McCradden
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. FAccT 2023: 1505-1519 - [c27]Jennifer Yu, Tina Behrouzi, Kopal Garg, Anna Goldenberg, Sana Tonekaboni:
Dynamic Interpretable Change Point Detection for Physiological Data Analysis. ML4H@NeurIPS 2023: 636-649 - [c26]Lauren Erdman, Mandy Rickard, Kyla N. Velear, Daniel Alvarez, Kunj Sheth, Armando J. Lorenzo, Bo Wang, Anna Goldenberg:
Moment-alignment domain adaptation in the few-shot and low-resource context. SIPAIM 2023: 1-6 - [c25]Gabriela Morgenshtern, Arnav Verma, Sana Tonekaboni, Robert Greer, Jürgen Bernard, Mjaye Mazwi, Anna Goldenberg, Fanny Chevalier:
RiskFix: Supporting Expert Validation of Predictive Timeseries Models in High-Intensity Settings. EuroVis (Short Papers) 2023: 13-17 - [i32]George Adam, Benjamin Haibe-Kains, Anna Goldenberg:
Maintaining Stability and Plasticity for Predictive Churn Reduction. CoRR abs/2305.04135 (2023) - 2022
- [j18]Azadeh Assadi
An integration engineering framework for machine learning in healthcare. Frontiers Digit. Health 4 (2022) - [j17]Jethro C. C. Kwong, Lauren Erdman, Adree Khondker, Marta Skreta, Anna Goldenberg, Melissa D. McCradden, Armando J. Lorenzo, Mandy Rickard:
The silent trial - the bridge between bench-to-bedside clinical AI applications. Frontiers Digit. Health 4 (2022) - [j16]Sarah M. Goodday, E. Karlin, A. Brooks, C. Chapman, Daniel R. Karlin
Better Understanding of the Metamorphosis of Pregnancy (BUMP): protocol for a digital feasibility study in women from preconception to postpartum. npj Digit. Medicine 5 (2022) - [j15]Neta Gotlieb
The promise of machine learning applications in solid organ transplantation. npj Digit. Medicine 5 (2022) - [c24]Sana Tonekaboni, Chun-Liang Li, Sercan Ö. Arik, Anna Goldenberg, Tomas Pfister:
Decoupling Local and Global Representations of Time Series. AISTATS 2022: 8700-8714 - [c23]Addison Weatherhead, Robert Greer, Michael-Alice Moga, Mjaye Mazwi, Danny Eytan, Anna Goldenberg, Sana Tonekaboni:
Learning Unsupervised Representations for ICU Timeseries. CHIL 2022: 152-168 - [c22]Sana Tonekaboni, Gabriela Morgenshtern, Azadeh Assadi
How to validate Machine Learning Models Prior to Deployment: Silent trial protocol for evaluation of real-time models at ICU. CHIL 2022: 169-182 - [c21]Abhishek Moturu, Sayali Joshi, Andrea S. Doria, Anna Goldenberg:
Volume-based Performance not Guaranteed by Promising Patch-based Results in Medical Imaging. ICBINB 2022: 85-93 - [c20]Chun-Hao Chang, Rich Caruana, Anna Goldenberg:
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning. ICLR 2022 - [c19]George-Alexandru Adam, Chun-Hao Kingsley Chang, Benjamin Haibe-Kains, Anna Goldenberg:
Error Amplification When Updating Deployed Machine Learning Models. MLHC 2022: 715-740 - [i31]Sana Tonekaboni, Chun-Liang Li, Sercan Ö. Arik, Anna Goldenberg, Tomas Pfister:
Decoupling Local and Global Representations of Time Series. CoRR abs/2202.02262 (2022) - [i30]Caitlin F. Harrigan, Gabriela Morgenshtern, Anna Goldenberg, Fanny Chevalier:
Considerations for Visualizing Uncertainty in Clinical Machine Learning Models. CoRR abs/2210.12220 (2022) - [i29]Kopal Garg, Sana Tonekaboni, Anna Goldenberg:
Time-Varying Correlation Networks for Interpretable Change Point Detection. CoRR abs/2211.03991 (2022) - [i28]Stanley Bryan Z. Hua, Mandy Rickard, John Weaver, Alice X. Xiang, Daniel Alvarez, Kyla N. Velear, Kunj Sheth, Gregory E. Tasian, Armando J. Lorenzo, Anna Goldenberg, Lauren Erdman:
From Single-Visit to Multi-Visit Image-Based Models: Single-Visit Models are Enough to Predict Obstructive Hydronephrosis. CoRR abs/2212.13535 (2022) - 2021
- [c18]Matthew B. A. McDermott, Bret Nestor, Evan Kim, Wancong Zhang, Anna Goldenberg, Peter Szolovits, Marzyeh Ghassemi:
A comprehensive EHR timeseries pre-training benchmark. CHIL 2021: 257-278 - [c17]Chun-Hao Chang, George-Alexandru Adam, Anna Goldenberg:
Towards Robust Classification Model by Counterfactual and Invariant Data Generation. CVPR 2021: 15212-15221 - [c16]Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi:
Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings. FAccT 2021: 723-734 - [c15]Sana Tonekaboni, Danny Eytan, Anna Goldenberg:
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding. ICLR 2021 - [c14]Chun-Hao Chang, Sarah Tan, Benjamin J. Lengerich, Anna Goldenberg, Rich Caruana:
How Interpretable and Trustworthy are GAMs? KDD 2021: 95-105 - [i27]Alex Chang, Vinith M. Suriyakumar, Abhishek Moturu, James Tu, Nipaporn Tewattanarat, Sayali Joshi, Andrea Doria, Anna Goldenberg:
3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI. CoRR abs/2103.13497 (2021) - [i26]Sana Tonekaboni, Danny Eytan, Anna Goldenberg:
Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding. CoRR abs/2106.00750 (2021) - [i25]Chun-Hao Chang, George-Alexandru Adam, Anna Goldenberg:
Towards Robust Classification Model by Counterfactual and Invariant Data Generation. CoRR abs/2106.01127 (2021) - [i24]Chun-Hao Chang, Rich Caruana, Anna Goldenberg:
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning. CoRR abs/2106.01613 (2021) - [i23]Chun-Hao Chang, George-Alexandru Adam, Rich Caruana, Anna Goldenberg:
Extracting Clinician's Goals by What-if Interpretable Modeling. CoRR abs/2110.15165 (2021) - 2020
- [j14]Melissa D. McCradden
Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning. J. Am. Medical Informatics Assoc. 27(12): 2024-2027 (2020) - [c13]Lauren Erdman, Marta Skreta, Mandy Rickard, Carson McLean, Aziz Mezlini, Daniel T. Keefe
Predicting Obstructive Hydronephrosis Based on Ultrasound Alone. MICCAI (3) 2020: 493-503 - [c12]George-Alexandru Adam, Chun-Hao Kingsley Chang, Benjamin Haibe-Kains, Anna Goldenberg:
Hidden Risks of Machine Learning Applied to Healthcare: Unintended Feedback Loops Between Models and Future Data Causing Model Degradation. MLHC 2020: 710-731 - [c11]Bret Nestor, Liam G. McCoy, Amol Verma, Chloé Pou-Prom, Joshua Murray, Sebnem Kuzulugil, David Dai, Muhammad Mamdani, Anna Goldenberg, Marzyeh Ghassemi:
Preparing a Clinical Support Model for Silent Mode in General Internal Medicine. MLHC 2020: 950-972 - [c10]Sana Tonekaboni, Shalmali Joshi, Kieran Campbell, David Duvenaud, Anna Goldenberg:
What went wrong and when? Instance-wise feature importance for time-series black-box models. NeurIPS 2020 - [i22]Sana Tonekaboni, Shalmali Joshi, David Duvenaud, Anna Goldenberg:
What went wrong and when? Instance-wise Feature Importance for Time-series Models. CoRR abs/2003.02821 (2020) - [i21]Alex Chang, Vinith M. Suriyakumar, Abhishek Moturu, Nipaporn Tewattanarat, Andrea Doria, Anna Goldenberg:
Using Generative Models for Pediatric wbMRI. CoRR abs/2006.00727 (2020) - [i20]Chun-Hao Chang, Sarah Tan, Benjamin J. Lengerich, Anna Goldenberg, Rich Caruana:
How Interpretable and Trustworthy are GAMs? CoRR abs/2006.06466 (2020) - [i19]Matthew B. A. McDermott, Bret Nestor, Evan Kim, Wancong Zhang, Anna Goldenberg, Peter Szolovits, Marzyeh Ghassemi:
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data. CoRR abs/2007.10185 (2020) - [i18]Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi:
Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings. CoRR abs/2010.06667 (2020) - [i17]Erik Drysdale, Devin Singh, Anna Goldenberg:
Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds. CoRR abs/2011.06058 (2020)
2010 – 2019
- 2019
- [j13]Ladislav Rampásek
Dr.VAE: improving drug response prediction via modeling of drug perturbation effects. Bioinform. 35(19): 3743-3751 (2019) - [j12]Marinka Zitnik, Francis Nguyen, Bo Wang
Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities. Inf. Fusion 50: 71-91 (2019) - [c9]Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud:
Explaining Image Classifiers by Counterfactual Generation. ICLR (Poster) 2019 - [c8]Chun-Hao Chang, Mingjie Mai, Anna Goldenberg:
Dynamic Measurement Scheduling for Event Forecasting using Deep RL. ICML 2019: 951-960 - [c7]Sana Tonekaboni, Shalmali Joshi, Melissa D. McCradden, Anna Goldenberg:
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. MLHC 2019: 359-380 - [c6]Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. MLHC 2019: 381-405 - [i16]Chun-Hao Chang, Mingjie Mai, Anna Goldenberg:
Dynamic Measurement Scheduling for Event Forecasting using Deep RL. CoRR abs/1901.09699 (2019) - [i15]Erik Drysdale, Yingwei Peng, Timothy P. Hanna, Paul Nguyen, Anna Goldenberg:
The False Positive Control Lasso. CoRR abs/1903.12584 (2019) - [i14]George Adam, Petr Smirnov, Benjamin Haibe-Kains, Anna Goldenberg:
Reducing Adversarial Example Transferability Using Gradient Regularization. CoRR abs/1904.07980 (2019) - [i13]Sana Tonekaboni, Shalmali Joshi, Melissa D. McCradden, Anna Goldenberg:
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. CoRR abs/1905.05134 (2019) - [i12]Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi:
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. CoRR abs/1908.00690 (2019) - 2018
- [c5]Sana Tonekaboni, Mjaye Mazwi, Peter Laussen, Danny Eytan, Robert Greer, Sebastian D. Goodfellow, Andrew J. Goodwin, Michael Brudno, Anna Goldenberg:
Prediction of Cardiac Arrest from Physiological Signals in the Pediatric ICU. MLHC 2018: 534-550 - [i11]Marinka Zitnik, Francis Nguyen, Bo Wang, Jure Leskovec, Anna Goldenberg, Michael M. Hoffman
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities. CoRR abs/1807.00123 (2018) - [i10]Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud:
Explaining Image Classifiers by Adaptive Dropout and Generative In-filling. CoRR abs/1807.08024 (2018) - [i9]George-Alexandru Adam, Petr Smirnov, Anna Goldenberg, David Duvenaud, Benjamin Haibe-Kains:
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples. CoRR abs/1808.06645 (2018) - [i8]Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi:
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation. CoRR abs/1811.12583 (2018) - [i7]Chun-Hao Chang, Mingjie Mai, Anna Goldenberg:
Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL. CoRR abs/1812.00268 (2018) - 2017
- [j11]Aziz M. Mezlini, Anna Goldenberg
Incorporating networks in a probabilistic graphical model to find drivers for complex human diseases. PLoS Comput. Biol. 13(10) (2017) - [j10]Bo Wang
Vicus: Exploiting local structures to improve network-based analysis of biological data. PLoS Comput. Biol. 13(10) (2017) - [i6]Chun-Hao Chang, Ladislav Rampásek, Anna Goldenberg:
Dropout Feature Ranking for Deep Learning Models. CoRR abs/1712.08645 (2017) - 2016
- [j9]Recep Colak, TaeHyung Kim, Hilal Kazan, Yoomi Oh, Miguel Cruz
JBASE: Joint Bayesian Analysis of Subphenotypes and Epistasis. Bioinform. 32(2): 203-210 (2016) - [j8]Petr Smirnov
PharmacoGx: an R package for analysis of large pharmacogenomic datasets. Bioinform. 32(8): 1244-1246 (2016) - [j7]Zhaleh Safikhani, Nehme Hachem
Safikhani et al. reply. Nat. 540(7631): E2-E4 (2016) - [i5]Lauren Erdman, Ekansh Sharma, Eva Unternaehrer, Shantala A. Hari Dass, Kieran J. O'Donnell, Sara Mostafavi, Rachel Edgar, Michael S. Kobor, Hélène Gaudreau, Michael J. Meaney, Anna Goldenberg:
Modeling trajectories of mental health: challenges and opportunities. CoRR abs/1612.01055 (2016) - 2015
- [j6]Suchi Saria, Anna Goldenberg:
Subtyping: What It is and Its Role in Precision Medicine. IEEE Intell. Syst. 30(4): 70-75 (2015) - [i4]Aziz M. Mezlini, Fabio Fuligni, Adam Shlien, Anna Goldenberg:
Combining exome and gene expression datasets in one graphical model of disease to empower the discovery of disease mechanisms. CoRR abs/1508.07527 (2015) - 2014
- [j5]Yue Li, Anna Goldenberg, Ka-Chun Wong
A probabilistic approach to explore human miRNA targetome by integrating miRNA-overexpression data and sequence information. Bioinform. 30(5): 621-628 (2014) - [i3]Bo Wang, Anna Goldenberg:
Gradient-based Laplacian Feature Selection. CoRR abs/1404.2948 (2014) - [i2]Daniel Hidru, Anna Goldenberg:
EquiNMF: Graph Regularized Multiview Nonnegative Matrix Factorization. CoRR abs/1409.4018 (2014) - 2012
- [j4]Mohammed Alshalalfa, Gary D. Bader
Detecting microRNAs of high influence on protein functional interaction networks: a prostate cancer case study. BMC Syst. Biol. 6: 112 (2012) - [c4]David Warde-Farley, Michael Brudno, Quaid Morris, Anna Goldenberg:
Mixture Model for Sub-Phenotyping in GWAS. Pacific Symposium on Biocomputing 2012: 363-374 - 2011
- [j3]Anna Goldenberg, Sara Mostafavi, Gerald T. Quon
Unsupervised detection of genes of influence in lung cancer using biological networks. Bioinform. 27(22): 3166-3172 (2011)
2000 – 2009
- 2009
- [j2]Anna Goldenberg, Alice X. Zheng, Stephen E. Fienberg, Edoardo M. Airoldi:
A Survey of Statistical Network Models. Found. Trends Mach. Learn. 2(2): 129-233 (2009) - [i1]Anna Goldenberg, Alice X. Zheng, Stephen E. Fienberg, Edoardo M. Airoldi:
A survey of statistical network models. CoRR abs/0912.5410 (2009) - 2007
- [e1]Edoardo M. Airoldi
Statistical Network Analysis: Models, Issues, and New Directions - ICML 2006 Workshop on Statistical Network Analysis, Pittsburgh, PA, USA, June 29, 2006, Revised Selected Papers. Lecture Notes in Computer Science 4503, Springer 2007, ISBN 978-3-540-73132-0 [contents] - 2006
- [c3]Anna Goldenberg, Alice X. Zheng:
Exploratory Study of a New Model for Evolving Networks. SNA@ICML 2006: 75-89 - 2005
- [c2]Anna Goldenberg, Andrew W. Moore:
Bayes net graphs to understand co-authorship networks? LinkKDD 2005: 1-8 - 2004
- [c1]Anna Goldenberg, Andrew W. Moore:
Tractable learning of large Bayes net structures from sparse data. ICML 2004 - 2001
- [j1]Mehmed M. Kantardzic, Anna Goldenberg, Troy E. Howe, Peter Faguy:
Artificial neural network approach to data analysis and parameter estimation in experimental spectroscopy. Informatica (Slovenia) 25(1) (2001)
Coauthor Index
Chun-Hao Chang
aka: Chun-Hao Kingsley Chang
aka: Chun-Hao Kingsley Chang
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