Shalmali Joshi
Shalmali Joshi
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What clinicians want: contextualizing explainable machine learning for clinical end use
S Tonekaboni, S Joshi, MD McCradden, A Goldenberg
Machine learning for healthcare conference, 359-380, 2019
Towards realistic individual recourse and actionable explanations in black-box decision making systems
S Joshi, O Koyejo, W Vijitbenjaronk, B Kim, J Ghosh
arXiv preprint arXiv:1907.09615, 2019
Treating health disparities with artificial intelligence
IY Chen, S Joshi, M Ghassemi
Nature medicine 26 (1), 16-17, 2020
Ethical limitations of algorithmic fairness solutions in health care machine learning
MD McCradden, S Joshi, M Mazwi, JA Anderson
The Lancet Digital Health 2 (5), e221-e223, 2020
Ethical Machine Learning in Healthcare
IY Chen, E Pierson, S Rose, S Joshi, K Ferryman, M Ghassemi
Annual Review of Biomedical Data Science 4, 2020
Identifiable phenotyping using constrained non-negative matrix factorization
S Joshi, S Gunasekar, D Sontag, G Joydeep
Machine Learning for Healthcare Conference, 17-41, 2016
Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning
MD McCradden, S Joshi, JA Anderson, M Mazwi, A Goldenberg, ...
Journal of the American Medical Informatics Association 27 (12), 2024-2027, 2020
xGEMs: Generating Examplars to Explain Black-Box Models
S Joshi, O Koyejo, B Kim, J Ghosh, 2018
What went wrong and when? Instance-wise feature importance for time-series black-box models
S Tonekaboni, S Joshi, K Campbell, DK Duvenaud, A Goldenberg
Advances in Neural Information Processing Systems 33, 2020
An empirical framework for domain generalization in clinical settings
H Zhang, N Dullerud, L Seyyed-Kalantari, Q Morris, S Joshi, M Ghassemi
Proceedings of the Conference on Health, Inference, and Learning, 279-290, 2021
Probabilistic machine learning for healthcare
IY Chen, S Joshi, M Ghassemi, R Ranganath
Annual Review of Biomedical Data Science 4, 2020
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. 2019
S Tonekaboni, S Joshi, MD McCradden, A Goldenberg
arXiv preprint arXiv:1905.05134, 1905
Can You Fake It Until You Make It? Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness
V Cheng, VM Suriyakumar, N Dullerud, S Joshi, M Ghassemi
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021
Rényi divergence minimization based co-regularized multiview clustering
S Joshi, J Ghosh, M Reid, O Koyejo
Machine Learning 104 (2), 411-439, 2016
When your only tool is a hammer: Ethical limitations of algorithmic fairness solutions in healthcare machine learning
M McCradden, M Mazwi, S Joshi, JA Anderson
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 109-109, 2020
Explaining time series by counterfactuals
S Tonekaboni, S Joshi, D Duvenaud, A Goldenberg
Towards Robust and Reliable Algorithmic Recourse
S Upadhyay, S Joshi, H Lakkaraju
arXiv preprint arXiv:2102.13620, 2021
Sequential Explanations with Mental Model-Based Policies
A Yeung, S Joshi, JJ Williams, F Rudzicz
arXiv preprint arXiv:2007.09028, 2020
Counterfactually guided policy transfer in clinical settings
TW Killian, M Ghassemi, S Joshi
arXiv preprint arXiv:2006.11654, 2020
Co-regularized Monotone Retargeting for Semi-supervised LeTOR
S Joshi, R Khanna, J Ghosh
Proceedings of the 2018 SIAM International Conference on Data Mining, 432-440, 2018
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