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ORCIDShalmali Joshi
Person information
- affiliation: Vector Institute, Toronto, Canada
- affiliation (former): Harvard University, SEAS, USA
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2020 – today
- 2024
[c19]Shalmali Joshi, Junzhe Zhang, Elias Bareinboim:
Towards Safe Policy Learning under Partial Identifiability: A Causal Approach. AAAI 2024: 13004-13012- 2023
- [j5]Daniel Ehrmann
, Shalmali Joshi, Sebastian D. Goodfellow, Mjaye Mazwi, Danny Eytan:
Making machine learning matter to clinicians: model actionability in medical decision-making. npj Digit. Medicine 6 (2023) - [j4]Melissa D. McCradden, Shalmali Joshi, James A. Anderson, Alex John London:
A normative framework for artificial intelligence as a sociotechnical system in healthcare. Patterns 4(11): 100864 (2023) - [j3]Shalmali Joshi, Sonali Parbhoo, Finale Doshi-Velez:
Learning-to-defer for sequential medical decision-making under uncertainty. Trans. Mach. Learn. Res. 2023 (2023) - [c18]Melissa D. McCradden, Oluwadara Odusi, Shalmali Joshi, Ismail Akrout, Kagiso Ndlovu, Ben Glocker, Gabriel Maicas, Xiaoxuan Liu, Mjaye Mazwi, Tee Garnett, Lauren Oakden-Rayner, Myrtede Alfred, Irvine Sihlahla, Oswa Shafei, Anna Goldenberg:
What's fair is... fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning: JustEFAB. FAccT 2023: 1505-1519 - [c17]Haoran Zhang, Harvineet Singh, Marzyeh Ghassemi, Shalmali Joshi:
"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts. ICML 2023: 41550-41578 - [e2]Kaivalya Deshpande, Madalina Fiterau, Shalmali Joshi, Zachary C. Lipton, Rajesh Ranganath, Iñigo Urteaga, Serene Yeung:
Machine Learning for Healthcare Conference, MLHC 2023, 11-12 August 2023, New York, USA. Proceedings of Machine Learning Research 219, PMLR 2023 [contents] - 2022
- [c16]Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju:
Towards Robust Off-Policy Evaluation via Human Inputs. AIES 2022: 686-699 - [c15]Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, Himabindu Lakkaraju:
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. AISTATS 2022: 4574-4594 - [c14]Taylor W. Killian, Marzyeh Ghassemi, Shalmali Joshi:
Counterfactually Guided Policy Transfer in Clinical Settings. CHIL 2022: 5-31 - [c13]Antonio Parziale, Monica Agrawal, Shengpu Tang, Kristen Severson, Luis Oala, Adarsh Subbaswamy, Sayantan Kumar, Elora D. M. Schörverth, Stefan Hegselmann, Helen Zhou, Ghada Zamzmi, Purity Mugambi, Elena Sizikova, Girmaw Abebe Tadesse, Yuyin Zhou, Taylor W. Killian, Haoran Zhang, Fahad Kamran, Andrea Hobby, Mars Huang, Ahmed M. Alaa, Harvineet Singh, Irene Y. Chen, Shalmali Joshi:
Machine Learning for Health (ML4H) 2022. ML4H@NeurIPS 2022: 1-11 - [e1]Antonio Parziale, Monica Agrawal, Shalmali Joshi, Irene Y. Chen, Shengpu Tang, Luis Oala, Adarsh Subbaswamy:
Machine Learning for Health, ML4H 2022, 28 November 2022, New Orleans, Lousiana, USA & Virtual. Proceedings of Machine Learning Research 193, PMLR 2022 [contents] - [i20]Sonali Parbhoo, Shalmali Joshi, Finale Doshi-Velez:
Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making. CoRR abs/2201.08262 (2022) - [i19]Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju:
Towards Robust Off-Policy Evaluation via Human Inputs. CoRR abs/2209.08682 (2022) - [i18]Haoran Zhang, Harvineet Singh, Marzyeh Ghassemi, Shalmali Joshi:
"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts. CoRR abs/2210.10769 (2022) - [i17]Antonio Parziale, Monica Agrawal, Shalmali Joshi, Irene Y. Chen, Shengpu Tang, Luis Oala, Adarsh Subbaswamy:
Machine Learning for Health symposium 2022 - Extended Abstract track. CoRR abs/2211.15564 (2022) - 2021
- [c12]Haoran Zhang, Natalie Dullerud, Laleh Seyyed-Kalantari, Quaid Morris, Shalmali Joshi, Marzyeh Ghassemi:
An empirical framework for domain generalization in clinical settings. CHIL 2021: 279-290 - [c11]Sindhu C. M. Gowda, Shalmali Joshi, Haoran Zhang, Marzyeh Ghassemi:
Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing. CIKM 2021: 606-616 - [c10]Victoria Cheng, Vinith M. Suriyakumar, Natalie Dullerud, Shalmali Joshi, Marzyeh Ghassemi:
Can You Fake It Until You Make It?: Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness. FAccT 2021: 149-160 - [c9]Sohini Upadhyay, Shalmali Joshi, Himabindu Lakkaraju:
Towards Robust and Reliable Algorithmic Recourse. NeurIPS 2021: 16926-16937 - [i16]Sohini Upadhyay, Shalmali Joshi, Himabindu Lakkaraju:
Towards Robust and Reliable Algorithmic Recourse. CoRR abs/2102.13620 (2021) - [i15]Haoran Zhang, Natalie Dullerud, Laleh Seyyed-Kalantari, Quaid Morris, Shalmali Joshi, Marzyeh Ghassemi:
An Empirical Framework for Domain Generalization in Clinical Settings. CoRR abs/2103.11163 (2021) - [i14]Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju:
Learning Under Adversarial and Interventional Shifts. CoRR abs/2103.15933 (2021) - [i13]Martin Pawelczyk, Shalmali Joshi, Chirag Agarwal, Sohini Upadhyay, Himabindu Lakkaraju:
On the Connections between Counterfactual Explanations and Adversarial Examples. CoRR abs/2106.09992 (2021) - [i12]Sindhu C. M. Gowda, Shalmali Joshi, Haoran Zhang, Marzyeh Ghassemi:
Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing. CoRR abs/2108.12510 (2021) - [i11]Shalmali Joshi, Sonali Parbhoo, Finale Doshi-Velez:
Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty. CoRR abs/2109.06312 (2021) - 2020
- [j2]Melissa D. McCradden, Shalmali Joshi, James A. Anderson, Mjaye Mazwi, Anna Goldenberg, Randi Zlotnik Shaul:
Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning. J. Am. Medical Informatics Assoc. 27(12): 2024-2027 (2020) - [c8]Melissa D. McCradden, Mjaye Mazwi, Shalmali Joshi, James A. Anderson:
When Your Only Tool Is A Hammer: Ethical Limitations of Algorithmic Fairness Solutions in Healthcare Machine Learning. AIES 2020: 109 - [c7]Sana Tonekaboni, Shalmali Joshi, Kieran Campbell, David Duvenaud, Anna Goldenberg:
What went wrong and when? Instance-wise feature importance for time-series black-box models. NeurIPS 2020 - [c6]Shirly Wang, Seung Eun Yi, Shalmali Joshi, Marzyeh Ghassemi:
Confounding Feature Acquisition for Causal Effect Estimation. ML4H@NeurIPS 2020: 379-396 - [i10]Sana Tonekaboni, Shalmali Joshi, David Duvenaud, Anna Goldenberg:
What went wrong and when? Instance-wise Feature Importance for Time-series Models. CoRR abs/2003.02821 (2020) - [i9]Taylor W. Killian, Marzyeh Ghassemi, Shalmali Joshi:
Counterfactually Guided Policy Transfer in Clinical Settings. CoRR abs/2006.11654 (2020) - [i8]Arnold Y. S. Yeung, Shalmali Joshi, Joseph Jay Williams, Frank Rudzicz:
Sequential Explanations with Mental Model-Based Policies. CoRR abs/2007.09028 (2020) - [i7]Irene Y. Chen, Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, Marzyeh Ghassemi:
Ethical Machine Learning in Health Care. CoRR abs/2009.10576 (2020) - [i6]Irene Y. Chen, Shalmali Joshi, Marzyeh Ghassemi, Rajesh Ranganath:
Probabilistic Machine Learning for Healthcare. CoRR abs/2009.11087 (2020) - [i5]Shirly Wang, Seung Eun Yi, Shalmali Joshi, Marzyeh Ghassemi:
Confounding Feature Acquisition for Causal Effect Estimation. CoRR abs/2011.08753 (2020)
2010 – 2019
- 2019
- [c5]Sana Tonekaboni, Shalmali Joshi, Melissa D. McCradden, Anna Goldenberg:
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. MLHC 2019: 359-380 - [i4]Sana Tonekaboni, Shalmali Joshi, Melissa D. McCradden, Anna Goldenberg:
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. CoRR abs/1905.05134 (2019) - [i3]Shalmali Joshi, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, Joydeep Ghosh:
Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems. CoRR abs/1907.09615 (2019) - 2018
- [c4]Shalmali Joshi, Rajiv Khanna, Joydeep Ghosh:
Co-regularized Monotone Retargeting for Semi-supervised LeTOR. SDM 2018: 432-440 - [i2]Shalmali Joshi, Oluwasanmi Koyejo, Been Kim, Joydeep Ghosh:
xGEMs: Generating Examplars to Explain Black-Box Models. CoRR abs/1806.08867 (2018) - 2016
- [j1]Shalmali Joshi, Joydeep Ghosh, Mark Reid, Oluwasanmi Koyejo:
Rényi divergence minimization based co-regularized multiview clustering. Mach. Learn. 104(2-3): 411-439 (2016) - [c3]Shalmali Joshi, Suriya Gunasekar, David A. Sontag, Joydeep Ghosh:
Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization. MLHC 2016: 17-41 - [i1]Shalmali Joshi, Suriya Gunasekar, David A. Sontag, Joydeep Ghosh:
Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization. CoRR abs/1608.00704 (2016) - 2015
- [c2]Shalmali Joshi, Oluwasanmi Koyejo, Kristine Resurreccion, Joydeep Ghosh:
Simultaneous Prognosis and Exploratory Analysis of Multiple Chronic Conditions Using Clinical Notes. ICHI 2015: 243-252 - [c1]Shalmali Joshi, Oluwasanmi Koyejo, Joydeep Ghosh:
Simultaneous Prognosis of Multiple Chronic Conditions from Heterogeneous EHR Data. ICHI 2015: 497
Coauthor Index
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