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A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series

Jan 05, 2021
Satya Narayan Shukla, Benjamin M. Marlin

* Presented at NeurIPS 2020 Workshop: ML Retrospectives, Surveys & Meta-Analyses (ML-RSA) 

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A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series: From Discretization to Attention and Invariance

Nov 30, 2020
Satya Narayan Shukla, Benjamin M. Marlin


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Gaussian MRF Covariance Modeling for Efficient Black-Box Adversarial Attacks

Oct 08, 2020
Anit Kumar Sahu, Satya Narayan Shukla, J. Zico Kolter


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Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes

Jul 13, 2020
Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter

* arXiv admin note: substantial text overlap with arXiv:1909.13857 

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Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction

Mar 24, 2020
Satya Narayan Shukla, Benjamin M. Marlin

* Accepted at ACM Conference on Health, Inference and Learning, 2020 

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Assessing the Adversarial Robustness of Monte Carlo and Distillation Methods for Deep Bayesian Neural Network Classification

Feb 07, 2020
Meet P. Vadera, Satya Narayan Shukla, Brian Jalaian, Benjamin M. Marlin

* Presented at SafeAI Workshop, AAAI 2020 

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Black-box Adversarial Attacks with Bayesian Optimization

Sep 30, 2019
Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter


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Interpolation-Prediction Networks for Irregularly Sampled Time Series

Sep 13, 2019
Satya Narayan Shukla, Benjamin M. Marlin

* International Conference on Learning Representations. arXiv admin note: substantial text overlap with arXiv:1812.00531 

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Modeling Irregularly Sampled Clinical Time Series

Dec 03, 2018
Satya Narayan Shukla, Benjamin M. Marlin

* Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:cs/0101200 

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