John V. Guttag

ORCID

Publication Activity (10 Years)

Years Active: 1976-2024
Publications (10 Years): 98

Top Venues

Publications

Marianne Rakic, Hallee E. Wong, Jose Javier Gonzalez Ortiz, Beth Cimini, John V. Guttag, Adrian V. Dalca
Tyche: Stochastic In-Context Learning for Medical Image Segmentation. CoRR (2024)
Jose Javier Gonzalez Ortiz, John V. Guttag, Adrian V. Dalca
Amortized Learning of Dynamic Feature Scaling for Image Segmentation. CoRR (2023)
Jose Javier Gonzalez Ortiz, John V. Guttag, Adrian V. Dalca
Scale-Space Hypernetworks for Efficient Biomedical Image Analysis. NeurIPS (2023)
Hallee E. Wong, Marianne Rakic, John V. Guttag, Adrian V. Dalca
ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Medical Image. CoRR (2023)
Angie Boggust, Harini Suresh, Hendrik Strobelt, John V. Guttag, Arvind Satyanarayan
Saliency Cards: A Framework to Characterize and Compare Saliency Methods. FAccT (2023)
Rajiv Movva, Divya Shanmugam, Kaihua Hou, Priya Pathak, John V. Guttag, Nikhil Garg, Emma Pierson
Coarse race data conceals disparities in clinical risk score performance. CoRR (2023)
Harini Suresh, Divya Shanmugam, Tiffany Chen, Annie G. Bryan, Alexander D'Amour, John V. Guttag, Arvind Satyanarayan
Kaleidoscope: Semantically-grounded, context-specific ML model evaluation. CHI (2023)
Aniruddh Raghu, Payal Chandak, Ridwan Alam, John V. Guttag, Collin M. Stultz
Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series. ICML (2023)
Emily Mu, John V. Guttag, Maggie Makar
Multi-Similarity Contrastive Learning. CoRR (2023)
Victor Ion Butoi, Jose Javier Gonzalez Ortiz, Tianyu Ma, Mert R. Sabuncu, John V. Guttag, Adrian V. Dalca
UniverSeg: Universal Medical Image Segmentation. ICCV (2023)
Victor Ion Butoi, Jose Javier Gonzalez Ortiz, Tianyu Ma, Mert R. Sabuncu, John V. Guttag, Adrian V. Dalca
UniverSeg: Universal Medical Image Segmentation. CoRR (2023)
Katie Matton, Robert Lewis, John V. Guttag, Rosalind W. Picard
Contrastive Learning of Electrodermal Activity Representations for Stress Detection. CHIL (2023)
Rajiv Movva, Divya Shanmugam, Kaihua Hou, Priya Pathak, John V. Guttag, Nikhil Garg, Emma Pierson
Coarse race data conceals disparities in clinical risk score performance. MLHC (2023)
Emily Mu, Kathleen M. Lewis, Adrian V. Dalca, John V. Guttag
Generating Image-Specific Text Improves Fine-grained Image Classification. CoRR (2023)
Aniruddh Raghu, Payal Chandak, Ridwan Alam, John V. Guttag, Collin M. Stultz
Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series. CoRR (2023)
Aniruddh Raghu, Divya Shanmugam, Eugene Pomerantsev, John V. Guttag, Collin M. Stultz
Data Augmentation for Electrocardiograms. CHIL (2022)
Divya Shanmugam, Katie Lewis, Jose Javier Gonzalez Ortiz, Agnieszka Kurant, John V. Guttag
At the Intersection of Deep Learning and Conceptual Art: The End of Signature. CoRR (2022)
Helen Lu, Divya Shanmugam, Harini Suresh, John V. Guttag
Improved Text Classification via Test-Time Augmentation. CoRR (2022)
Harini Suresh, Kathleen M. Lewis, John V. Guttag, Arvind Satyanarayan
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs. IUI (2022)
Aniruddh Raghu, Divya Shanmugam, Eugene Pomerantsev, John V. Guttag, Collin M. Stultz
Data Augmentation for Electrocardiograms. CoRR (2022)
Angie Boggust, Harini Suresh, Hendrik Strobelt, John V. Guttag, Arvind Satyanarayan
Beyond Faithfulness: A Framework to Characterize and Compare Saliency Methods. CoRR (2022)
Kathleen M. Lewis, John V. Guttag
SizeGAN: Improving Size Representation in Clothing Catalogs. CoRR (2022)
Andrew Hoopes, Malte Hoffmann, Douglas N. Greve, Bruce Fischl, John V. Guttag, Adrian V. Dalca
Learning the Effect of Registration Hyperparameters with HyperMorph. CoRR (2022)
Andrew Hoopes, Malte Hoffmann, Bruce Fischl, John V. Guttag, Adrian V. Dalca
HyperMorph: Amortized Hyperparameter Learning for Image Registration. CoRR (2021)
Davis W. Blalock, John V. Guttag
Multiplying Matrices Without Multiplying. CoRR (2021)
Maggie Makar, Lauren West, David Hooper, Eric Horvitz, Erica Shenoy, John V. Guttag
Exploiting structured data for learning contagious diseases under incomplete testing. ICML (2021)
Davis W. Blalock, John V. Guttag
Multiplying Matrices Without Multiplying. ICML (2021)
Divya Shanmugam, Davis W. Blalock, Guha Balakrishnan, John V. Guttag
Better Aggregation in Test-Time Augmentation. ICCV (2021)
Andrew Hoopes, Malte Hoffmann, Bruce Fischl, John V. Guttag, Adrian V. Dalca
HyperMorph: Amortized Hyperparameter Learning for Image Registration. IPMI (2021)
Aniruddh Raghu, John V. Guttag, Katherine Young, Eugene Pomerantsev, Adrian V. Dalca, Collin M. Stultz
Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction. CoRR (2021)
Harini Suresh, John V. Guttag
A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle. EAAMO (2021)
Harini Suresh, Kathleen M. Lewis, John V. Guttag, Arvind Satyanarayan
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs. CoRR (2021)
Aniruddh Raghu, John V. Guttag, Katherine Young, Eugene Pomerantsev, Adrian V. Dalca, Collin M. Stultz
Learning to predict with supporting evidence: applications to clinical risk prediction. CHIL (2021)
Susanne Gaube, Harini Suresh, Martina Raue, Alexander Merritt, Seth J. Berkowitz, Eva Lermer, Joseph F. Coughlin, John V. Guttag, Errol Colak, Marzyeh Ghassemi
Do as AI say: susceptibility in deployment of clinical decision-aids. npj Digit. Medicine 4 (2021)
Marianne Rakic, John V. Guttag, Adrian V. Dalca
Anatomical Predictions using Subject-Specific Medical Data. CoRR (2020)
Kathleen M. Lewis, Natalia S. Rost, John V. Guttag, Adrian V. Dalca
Fast learning-based registration of sparse 3D clinical images. CHIL (2020)
Maggie Makar, Fredrik D. Johansson, John V. Guttag, David A. Sontag
Estimation of Bounds on Potential Outcomes For Decision Making. ICML (2020)
Roshni Sahoo, Divya Shanmugam, John V. Guttag
Unsupervised Domain Adaptation in the Absence of Source Data. CoRR (2020)
Amy Zhao, Guha Balakrishnan, Kathleen M. Lewis, Frédo Durand, John V. Guttag, Adrian V. Dalca
Painting Many Pasts: Synthesizing Time Lapse Videos of Paintings. CoRR (2020)
Amy Zhao, Guha Balakrishnan, Kathleen M. Lewis, Frédo Durand, John V. Guttag, Adrian V. Dalca
Painting Many Pasts: Synthesizing Time Lapse Videos of Paintings. CVPR (2020)
Davis W. Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, John V. Guttag
What is the State of Neural Network Pruning? CoRR (2020)
Davis W. Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, John V. Guttag
What is the State of Neural Network Pruning? MLSys (2020)
Divya Shanmugam, Davis W. Blalock, Guha Balakrishnan, John V. Guttag
When and Why Test-Time Augmentation Works. CoRR (2020)
Adrian V. Dalca, John V. Guttag, Mert R. Sabuncu
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation. CoRR (2019)
Jose Javier Gonzalez Ortiz, Daryush D. Mehta, Jarrad H. Van Stan, Robert E. Hillman, John V. Guttag, Marzyeh Ghassemi
Learning from Few Subjects with Large Amounts of Voice Monitoring Data. MLHC (2019)
Adrian V. Dalca, Marianne Rakic, John V. Guttag, Mert R. Sabuncu
Learning Conditional Deformable Templates with Convolutional Networks. CoRR (2019)
Harini Suresh, John V. Guttag
A Framework for Understanding Unintended Consequences of Machine Learning. CoRR (2019)
Ava P. Soleimany, Harini Suresh, Jose Javier Gonzalez Ortiz, Divya Shanmugam, Nil Gural, John V. Guttag, Sangeeta N. Bhatia
Image segmentation of liver stage malaria infection with spatial uncertainty sampling. CoRR (2019)
Amy Zhao, Guha Balakrishnan, Frédo Durand, John V. Guttag, Adrian V. Dalca
Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation. CVPR (2019)
Adrian V. Dalca, Marianne Rakic, John V. Guttag, Mert R. Sabuncu
Learning Conditional Deformable Templates with Convolutional Networks. NeurIPS (2019)
Adrian V. Dalca, Guha Balakrishnan, John V. Guttag, Mert R. Sabuncu
Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Medical Image Anal. 57 (2019)
Adrian V. Dalca, Guha Balakrishnan, John V. Guttag, Mert R. Sabuncu
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces. CoRR (2019)
Guha Balakrishnan, Adrian V. Dalca, Amy Zhao, John V. Guttag, Frédo Durand, William T. Freeman
Visual Deprojection: Probabilistic Recovery of Collapsed Dimensions. ICCV (2019)
Amy Zhao, Guha Balakrishnan, Frédo Durand, John V. Guttag, Adrian V. Dalca
Data augmentation using learned transforms for one-shot medical image segmentation. CoRR (2019)
Guha Balakrishnan, Adrian V. Dalca, Amy Zhao, John V. Guttag, Frédo Durand, William T. Freeman
Visual Deprojection: Probabilistic Recovery of Collapsed Dimensions. CoRR (2019)
Maggie Makar, Fredrik D. Johansson, John V. Guttag, David A. Sontag
Estimation of Utility-Maximizing Bounds on Potential Outcomes. CoRR (2019)
Adrian V. Dalca, John V. Guttag, Mert R. Sabuncu
Unsupervised Data Imputation via Variational Inference of Deep Subspaces. CoRR (2019)
Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John V. Guttag, Adrian V. Dalca
VoxelMorph: A Learning Framework for Deformable Medical Image Registration. IEEE Trans. Medical Imaging 38 (8) (2019)
Divya Shanmugam, Davis W. Blalock, John V. Guttag
Multiple Instance Learning for ECG Risk Stratification. MLHC (2019)
Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John V. Guttag, Adrian V. Dalca
VoxelMorph: A Learning Framework for Deformable Medical Image Registration. CoRR (2018)
Davis W. Blalock, Samuel Madden, John V. Guttag
Sprintz: Time Series Compression for the Internet of Things. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2 (3) (2018)
Adrian V. Dalca, John V. Guttag, Mert R. Sabuncu
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation. CVPR (2018)
Adrian V. Dalca, Guha Balakrishnan, John V. Guttag, Mert R. Sabuncu
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration. CoRR (2018)
Kathleen M. Lewis, Guha Balakrishnan, Natalia S. Rost, John V. Guttag, Adrian V. Dalca
Fast Learning-based Registration of Sparse Clinical Images. CoRR (2018)
Jen J. Gong, John V. Guttag
Learning to Summarize Electronic Health Records Using Cross-Modality Correspondences. MLHC (2018)
Guha Balakrishnan, Amy Zhao, Adrian V. Dalca, Frédo Durand, John V. Guttag
Synthesizing Images of Humans in Unseen Poses. CVPR (2018)
Davis W. Blalock, Samuel Madden, John V. Guttag
Sprintz: Time Series Compression for the Internet of Things. CoRR (2018)
Adrian V. Dalca, Guha Balakrishnan, John V. Guttag, Mert R. Sabuncu
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration. MICCAI (1) (2018)
Harini Suresh, Jen J. Gong, John V. Guttag
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU. CoRR (2018)
Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John V. Guttag, Adrian V. Dalca
An Unsupervised Learning Model for Deformable Medical Image Registration. CVPR (2018)
Guha Balakrishnan, Amy Zhao, Adrian V. Dalca, Frédo Durand, John V. Guttag
Synthesizing Images of Humans in Unseen Poses. CoRR (2018)
Dina Levy-Lambert, Jen J. Gong, Tristan Naumann, Tom J. Pollard, John V. Guttag
Visualizing Patient Timelines in the Intensive Care Unit. CoRR (2018)
Maggie Makar, John V. Guttag, Jenna Wiens
Learning the Probability of Activation in the Presence of Latent Spreaders. AAAI (2018)
Divya Shanmugam, Davis W. Blalock, Jen J. Gong, John V. Guttag
Multiple Instance Learning for ECG Risk Stratification. CoRR (2018)
Harini Suresh, Jen J. Gong, John V. Guttag
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU. KDD (2018)
Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John V. Guttag, Adrian V. Dalca
An Unsupervised Learning Model for Deformable Medical Image Registration. CoRR (2018)
Davis W. Blalock, John V. Guttag
Bolt: Accelerated Data Mining with Fast Vector Compression. CoRR (2017)
Davis W. Blalock, John V. Guttag
Bolt: Accelerated Data Mining with Fast Vector Compression. KDD (2017)
Neal Wadhwa, Hao-Yu Wu, Abe Davis, Michael Rubinstein, Eugene Shih, Gautham J. Mysore, Justin G. Chen, Oral Büyüköztürk, John V. Guttag, William T. Freeman, Frédo Durand
Eulerian video magnification and analysis. Commun. ACM 60 (1) (2017)
Ronnachai Jaroensri, Amy Zhao, Guha Balakrishnan, Derek Lo, Jeremy D. Schmahmann, Frédo Durand, John V. Guttag
A Video-Based Method for Automatically Rating Ataxia. MLHC (2017)
Jen J. Gong, Tristan Naumann, Peter Szolovits, John V. Guttag
Predicting Clinical Outcomes Across Changing Electronic Health Record Systems. KDD (2017)
Maggie Makar, John V. Guttag, Jenna Wiens
Learning the Probability of Activation in the Presence of Latent Spreaders. CoRR (2017)
Marzyeh Ghassemi, Zeeshan Syed, Daryush D. Mehta, Jarrad H. Van Stan, Robert E. Hillman, John V. Guttag
Uncovering Voice Misuse Using Symbolic Mismatch. MLHC (2016)
Joel Brooks, Matthew Kerr, John V. Guttag
Using machine learning to draw inferences from pass location data in soccer. Stat. Anal. Data Min. 9 (5) (2016)
Davis W. Blalock, John V. Guttag
EXTRACT: Strong Examples from Weakly-Labeled Sensor Data. CoRR (2016)
Marzyeh Ghassemi, Zeeshan Syed, Daryush D. Mehta, Jarrad H. Van Stan, Robert E. Hillman, John V. Guttag
Uncovering Voice Misuse Using Symbolic Mismatch. CoRR (2016)
Jenna Wiens, John V. Guttag, Eric Horvitz
Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach. J. Mach. Learn. Res. 17 (2016)
Yun Liu, Kun-Ta Chuang, Fu-Wen Liang, Huey-Jen Su, Collin M. Stultz, John V. Guttag
Transferring Knowledge from Text to Predict Disease Onset. CoRR (2016)
Davis W. Blalock, John V. Guttag
EXTRACT: Strong Examples from Weakly-Labeled Sensor Data. ICDM (2016)
Joel Brooks, Matthew Kerr, John V. Guttag
Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights. KDD (2016)
Jen J. Gong, Maryann Gong, Dina Levy-Lambert, Jordan R. Green, Tiffany P. Hogan, John V. Guttag
Towards an Automated Screening Tool for Developmental Speech and Language Impairments. INTERSPEECH (2016)
Yun Liu, Collin M. Stultz, John V. Guttag, Kun-Ta Chuang, Fu-Wen Liang, Huey-Jen Su
Transferring Knowledge from Text to Predict Disease Onset. MLHC (2016)
Ronnachai Jaroensri, Amy Zhao, Guha Balakrishnan, Derek Lo, Jeremy D. Schmahmann, John V. Guttag, Frédo Durand
A Video-Based Method for Objectively Rating Ataxia. CoRR (2016)
Guha Balakrishnan, Frédo Durand, John V. Guttag
Video diff: highlighting differences between similar actions in videos. ACM Trans. Graph. 34 (6) (2015)
Anima Singh, Girish N. Nadkarni, Omri Gottesman, Stephen B. Ellis, Erwin P. Bottinger, John V. Guttag
Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration. J. Biomed. Informatics 53 (2015)
Marzyeh Ghassemi, Jarrad H. Van Stan, Daryush D. Mehta, Matías Zanartu, Harold A. Cheyne II, Robert E. Hillman, John V. Guttag
Corrections to "Learning to Detect Vocal Hyperfunction From Ambulatory Neck-Surface Acceleration Features: Initial Results For Vocal Fold Nodules". IEEE Trans. Biomed. Eng. 62 (10) (2015)
Amy Zhao, Frédo Durand, John V. Guttag
Estimating a Small Signal in the Presence of Large Noise. ICCV Workshops (2015)
Jen J. Gong, Thoralf M. Sundt, James D. Rawn, John V. Guttag
Instance Weighting for Patient-Specific Risk Stratification Models. KDD (2015)
Marzyeh Ghassemi, Jarrad H. Van Stan, Daryush D. Mehta, Matías Zanartu, Harold A. Cheyne II, Robert E. Hillman, John V. Guttag
Learning to Detect Vocal Hyperfunction From Ambulatory Neck-Surface Acceleration Features: Initial Results for Vocal Fold Nodules. IEEE Trans. Biomed. Eng. 61 (6) (2014)
Jenna Wiens, John V. Guttag, Eric Horvitz
A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions. J. Am. Medical Informatics Assoc. 21 (4) (2014)

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Publication Activity (10 Years)

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John V. Guttag