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Shalmali Joshi
Person information
- affiliation: Vector Institute, Toronto, Canada
- affiliation (former): Harvard University, SEAS, USA
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2020 – today
- 2026
- [i30]Aparajita Kashyap, Sara Matijevic, Noémie Elhadad, Steven A. Kushner, Shalmali Joshi:
A pipeline for enabling path-specific causal fairness in observational health data. CoRR abs/2601.09841 (2026) - [i29]Zilin Jing, Vincent Jeanselme, Yuta Kobayashi, Simon A. Lee, Chao Pang, Aparajita Kashyap, Yanwei Li, Xinzhuo Jiang, Shalmali Joshi:
One Loss to Rule Them All: Marked Time-to-Event for Structured EHR Foundation Models. CoRR abs/2602.00541 (2026) - 2025
- [j8]Evangelia Kyrimi, Scott McLachlan, Jared M. Wohlgemut, Zane B. Perkins, David A. Lagnado, William Marsh, Alexander Gimson, Ali Shafti, Ari Ercole
, Amitava Banerjee, Ben Glocker, Burkhard Schafer, Constantine Gatsonis, Crina Grosan, Danielle Sent
Explainable AI: definition and attributes of a good explanation for health AI. AI Ethics 5(4): 3883-3896 (2025) - [j7]Shalmali Joshi, Iñigo Urteaga, Wouter A. C. van Amsterdam, George Hripcsak, Pierre A. Elias
AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation. J. Am. Medical Informatics Assoc. 32(3): 589-594 (2025) - [j6]Monica Agrawal, Irene Y. Chen, Freya Gulamali, Shalmali Joshi:
The evaluation illusion of large language models in medicine. npj Digit. Medicine 8(1) (2025) - [c21]Mert Ketenci, Vincent Jeanselme, Harry Reyes Nieva, Shalmali Joshi, Noémie Elhadad:
ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression. MLHC 2025: 1-35 - [e4]Monica Agrawal, Kaivalya Deshpande, Matthew Engelhard, Shalmali Joshi, Shengpu Tang, Iñigo Urteaga:
Proceedings of the Machine Learning for Healthcare Conference (MLHC 2025), 15-16 August 2025, Mayo Clinic, Rochester, MN, USA. Proceedings of Machine Learning Research 298, PMLR 2025 [contents] - [i28]Daksh Mittal, Yuanzhe Ma, Shalmali Joshi, Hongseok Namkoong:
A Planning Framework for Adaptive Labeling. CoRR abs/2502.06076 (2025) - [i27]Chao Pang, Vincent Jeanselme, Young Sang Choi, Xinzhuo Jiang, Zilin Jing, Aparajita Kashyap, Yuta Kobayashi, Yanwei Li
FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records. CoRR abs/2505.16941 (2025) - [i26]Young Sang Choi, Vincent Jeanselme, Pierre A. Elias, Shalmali Joshi:
ICYM2I: The illusion of multimodal informativeness under missingness. CoRR abs/2505.16953 (2025) - [i25]Kevin Zhang, Yonghan Jung, Divyat Mahajan, Karthikeyan Shanmugam, Shalmali Joshi:
Path-specific effects for pulse-oximetry guided decisions in critical care. CoRR abs/2506.12371 (2025) - [i24]Chao Pang, Jiheum Park, Xinzhuo Jiang, Nishanth Parameshwar Pavinkurve, Krishna S. Kalluri, Shalmali Joshi, Noémie Elhadad, Karthik Natarajan:
CEHR-XGPT: A Scalable Multi-Task Foundation Model for Electronic Health Records. CoRR abs/2509.03643 (2025) - [i23]Mert Ketenci, Vincent Jeanselme, Harry Reyes Nieva, Shalmali Joshi, Noémie Elhadad:
ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression. CoRR abs/2509.07108 (2025) - [i22]Yuta Kobayashi, Zilin Jing, Jiayu Yao, Hongseok Namkoong, Shalmali Joshi:
Learning-To-Measure: In-context Active Feature Acquisition. CoRR abs/2510.12624 (2025) - [i21]Yuta Kobayashi, Vincent Jeanselme, Shalmali Joshi:
Mind the data gap: Missingness Still Shapes Large Language Model Prognoses. CoRR abs/2512.00479 (2025) - 2024
- [c20]Shalmali Joshi, Junzhe Zhang, Elias Bareinboim:
Towards Safe Policy Learning under Partial Identifiability: A Causal Approach. AAAI 2024: 13004-13012 - [c19]Daksh Mittal, Yuanzhe Ma, Shalmali Joshi, Hongseok Namkoong:
Adaptive Labeling for Efficient Out-of-distribution Model Evaluation. NeurIPS 2024 - [e3]Kaivalya Deshpande, Madalina Fiterau, Shalmali Joshi, Zachary C. Lipton, Rajesh Ranganath, Iñigo Urteaga:
Machine Learning for Healthcare Conference, 16-17 August 2024, Toronto, Canada. Proceedings of Machine Learning Research 252, PMLR 2024 [contents] - 2023
- [j5]Daniel Ehrmann
Making machine learning matter to clinicians: model actionability in medical decision-making. npj Digit. Medicine 6 (2023) - [j4]Melissa D. McCradden
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
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
An empirical framework for domain generalization in clinical settings. CHIL 2021: 279-290 - [c11]Sindhu C. M. Gowda, Shalmali Joshi, Haoran Zhang
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
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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last updated on 2026-04-22 03:47 CEST by the dblp team
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