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Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty

Sep 13, 2021
Shalmali Joshi, Sonali Parbhoo, Finale Doshi-Velez

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Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing

Aug 27, 2021
Sindhu C. M. Gowda, Shalmali Joshi, Haoran Zhang, Marzyeh Ghassemi

* Published in CIKM 2021 

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On the Connections between Counterfactual Explanations and Adversarial Examples

Jun 18, 2021
Martin Pawelczyk, Shalmali Joshi, Chirag Agarwal, Sohini Upadhyay, Himabindu Lakkaraju

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An Empirical Framework for Domain Generalization in Clinical Settings

Apr 15, 2021
Haoran Zhang, Natalie Dullerud, Laleh Seyyed-Kalantari, Quaid Morris, Shalmali Joshi, Marzyeh Ghassemi

* Published at ACM CHIL 2021 

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Learning Under Adversarial and Interventional Shifts

Mar 29, 2021
Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju

* 19 pages including 5 pages appendix, 6 figures, 2 tables. Preliminary version presented at Causal Discovery & Causality-Inspired Machine Learning Workshop 2020 

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Towards Robust and Reliable Algorithmic Recourse

Feb 26, 2021
Sohini Upadhyay, Shalmali Joshi, Himabindu Lakkaraju

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Confounding Feature Acquisition for Causal Effect Estimation

Nov 17, 2020
Shirly Wang, Seung Eun Yi, Shalmali Joshi, Marzyeh Ghassemi

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Ethical Machine Learning in Health Care

Oct 08, 2020
Irene Y. Chen, Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, Marzyeh Ghassemi

* Annual Reviews in Biomedical Data Science 2021 

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Probabilistic Machine Learning for Healthcare

Sep 23, 2020
Irene Y. Chen, Shalmali Joshi, Marzyeh Ghassemi, Rajesh Ranganath

* Annual Reviews of Biomedical Data Science 2021 

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Sequential Explanations with Mental Model-Based Policies

Jul 17, 2020
Arnold YS Yeung, Shalmali Joshi, Joseph Jay Williams, Frank Rudzicz

* Accepted into ICML 2020 Workshop on Human Interpretability in Machine Learning (Spotlight) 

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Counterfactually Guided Policy Transfer in Clinical Settings

Jun 20, 2020
Taylor W. Killian, Marzyeh Ghassemi, Shalmali Joshi

* 25 pages (including appendix), 14 figures 

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What went wrong and when? Instance-wise Feature Importance for Time-series Models

Mar 05, 2020
Sana Tonekaboni, Shalmali Joshi, David Duvenaud, Anna Goldenberg

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Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems

Jul 22, 2019
Shalmali Joshi, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, Joydeep Ghosh

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What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use

May 13, 2019
Sana Tonekaboni, Shalmali Joshi, Melissa D McCradden, Anna Goldenberg

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xGEMs: Generating Examplars to Explain Black-Box Models

Jun 22, 2018
Shalmali Joshi, Oluwasanmi Koyejo, Been Kim, Joydeep Ghosh

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Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization

Sep 20, 2016
Shalmali Joshi, Suriya Gunasekar, David Sontag, Joydeep Ghosh

* Presented at 2016 Machine Learning and Healthcare Conference (MLHC 2016), Los Angeles, CA 

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