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Shalmali Joshi
Shalmali Joshi
Verified email at seas.harvard.edu - Homepage
Title
Cited by
Cited by
Year
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
2622019
Ethical machine learning in healthcare
IY Chen, E Pierson, S Rose, S Joshi, K Ferryman, M Ghassemi
Annual review of biomedical data science 4, 123-144, 2021
151*2021
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
1042019
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
842020
Treating health disparities with artificial intelligence
IY Chen, S Joshi, M Ghassemi
Nature medicine 26 (1), 16-17, 2020
612020
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
432020
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, 799-809, 2020
342020
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
332021
Probabilistic machine learning for healthcare
IY Chen, S Joshi, M Ghassemi, R Ranganath
Annual Review of Biomedical Data Science 4, 393-415, 2021
302021
xGEMs: Generating Examplars to Explain Black-Box Models
S Joshi, O Koyejo, B Kim, J Ghosh
https://arxiv.org/abs/1806.08867, 2018
292018
Identifiable phenotyping using constrained non-negative matrix factorization
S Joshi, S Gunasekar, D Sontag, G Joydeep
Machine Learning for Healthcare Conference, 17-41, 2016
262016
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
252021
Exploring counterfactual explanations through the lens of adversarial examples: A theoretical and empirical analysis
M Pawelczyk, C Agarwal, S Joshi, S Upadhyay, H Lakkaraju
International Conference on Artificial Intelligence and Statistics, 4574-4594, 2022
23*2022
Towards robust and reliable algorithmic recourse
S Upadhyay, S Joshi, H Lakkaraju
Advances in Neural Information Processing Systems 34, 16926-16937, 2021
222021
Sequential explanations with mental model-based policies
A Yeung, S Joshi, JJ Williams, F Rudzicz
arXiv preprint arXiv:2007.09028, 2020
82020
Counterfactually guided policy transfer in clinical settings
TW Killian, M Ghassemi, S Joshi
Conference on Health, Inference, and Learning, 5-31, 2022
72022
Explaining time series by counterfactuals
S Tonekaboni, S Joshi, D Duvenaud, A Goldenberg
72020
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use.’ArXiv 2019
S Tonekaboni, S Joshi, MD McCradden, A Goldenberg
arXiv preprint arXiv:1905.05134, 1905
51905
Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty
S Joshi, S Parbhoo, F Doshi-Velez
arXiv preprint arXiv:2109.06312, 2021
42021
Learning under adversarial and interventional shifts
H Singh, S Joshi, F Doshi-Velez, H Lakkaraju
arXiv preprint arXiv:2103.15933, 2021
42021
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