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Aniruddh Raghu

Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction


Mar 04, 2021
Aniruddh Raghu, John Guttag, Katherine Young, Eugene Pomerantsev, Adrian V. Dalca, Collin M. Stultz

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* ACM Conference on Health, Learning, and Inference 2021 
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Teaching with Commentaries


Nov 05, 2020
Aniruddh Raghu, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey Hinton

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Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML


Sep 19, 2019
Aniruddh Raghu, Maithra Raghu, Samy Bengio, Oriol Vinyals

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Model-Based Reinforcement Learning for Sepsis Treatment


Nov 23, 2018
Aniruddh Raghu, Matthieu Komorowski, Sumeetpal Singh

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* Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216 
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Representation Balancing MDPs for Off-Policy Policy Evaluation


Oct 31, 2018
Yao Liu, Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo Faisal, Finale Doshi-Velez, Emma Brunskill

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Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters


Jul 10, 2018
Aniruddh Raghu, Omer Gottesman, Yao Liu, Matthieu Komorowski, Aldo Faisal, Finale Doshi-Velez, Emma Brunskill

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* Accepted to workshop on Machine Learning for Causal Inference, Counterfactual Prediction, and Autonomous Action at ICML 2018 
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Deep Reinforcement Learning for Sepsis Treatment


Nov 27, 2017
Aniruddh Raghu, Matthieu Komorowski, Imran Ahmed, Leo Celi, Peter Szolovits, Marzyeh Ghassemi

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* Extensions on earlier work (arXiv:1705.08422). Accepted at workshop on Machine Learning For Health at the conference on Neural Information Processing Systems, 2017 
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Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach


May 23, 2017
Aniruddh Raghu, Matthieu Komorowski, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi

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