Simultaneous Prognosis of Multiple Chronic Conditions from Heterogeneous EHR Data. - Shalmali Joshi; Oluwasanmi Koyejo; Joydeep Ghosh (ICHI, 2015)

Simultaneous Prognosis and Exploratory Analysis of Multiple Chronic Conditions Using Clinical Notes. - Shalmali Joshi; Oluwasanmi Koyejo; Kristine Resurreccion; Joydeep Ghosh (ICHI, 2015)

Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization. - Shalmali Joshi; Suriya Gunasekar; David A. Sontag; Joydeep Ghosh (MLHC, 2016)

Rényi divergence minimization based co-regularized multiview clustering. - Shalmali Joshi; Joydeep Ghosh; Mark Reid; Oluwasanmi Koyejo (Mach. Learn., 2016)

Co-regularized Monotone Retargeting for Semi-supervised LeTOR. - Shalmali Joshi; Rajiv Khanna; Joydeep Ghosh (SDM, 2018)

xGEMs: Generating Examplars to Explain Black-Box Models. - Shalmali Joshi; Oluwasanmi Koyejo; Been Kim; Joydeep Ghosh (CoRR, 2018)

What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use. - Sana Tonekaboni; Shalmali Joshi; Melissa D. McCradden; Anna Goldenberg (MLHC, 2019)

Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems. - Shalmali Joshi; Oluwasanmi Koyejo; Warut Vijitbenjaronk; Been Kim; Joydeep Ghosh (CoRR, 2019)

When Your Only Tool Is A Hammer: Ethical Limitations of Algorithmic Fairness Solutions in Healthcare Machine Learning. - Melissa D. McCradden; Mjaye Mazwi; Shalmali Joshi; James A. Anderson (AIES, 2020)

What went wrong and when? Instance-wise feature importance for time-series black-box models. - Sana Tonekaboni; Shalmali Joshi; Kieran Campbell; David Duvenaud; Anna Goldenberg (NeurIPS, 2020)

Confounding Feature Acquisition for Causal Effect Estimation. - Shirly Wang; Seung Eun Yi; Shalmali Joshi; Marzyeh Ghassemi (ML4H@NeurIPS, 2020)

What went wrong and when? Instance-wise Feature Importance for Time-series Models. - Sana Tonekaboni; Shalmali Joshi; David Duvenaud; Anna Goldenberg (CoRR, 2020)

Sequential Explanations with Mental Model-Based Policies. - Arnold Y. S. Yeung; Shalmali Joshi; Joseph Jay Williams; Frank Rudzicz (CoRR, 2020)

Ethical Machine Learning in Health Care. - Irene Y. Chen; Emma Pierson; Sherri Rose; Shalmali Joshi; Kadija Ferryman; Marzyeh Ghassemi (CoRR, 2020)

Probabilistic Machine Learning for Healthcare. - Irene Y. Chen; Shalmali Joshi; Marzyeh Ghassemi; Rajesh Ranganath (CoRR, 2020)

Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning. - Melissa D. McCradden; Shalmali Joshi; James A. Anderson; Mjaye Mazwi; Anna Goldenberg; Randi Zlotnik Shaul (J. Am. Medical Informatics Assoc., 2020)

An empirical framework for domain generalization in clinical settings. - Haoran Zhang 003; Natalie Dullerud; Laleh Seyyed-Kalantari; Quaid Morris; Shalmali Joshi; Marzyeh Ghassemi (CHIL, 2021)

Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing. - Sindhu C. M. Gowda; Shalmali Joshi; Haoran Zhang 003; Marzyeh Ghassemi (CIKM, 2021)

Can You Fake It Until You Make It?: Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness. - Victoria Cheng; Vinith M. Suriyakumar; Natalie Dullerud; Shalmali Joshi; Marzyeh Ghassemi (FAccT, 2021)

Towards Robust and Reliable Algorithmic Recourse. - Sohini Upadhyay; Shalmali Joshi; Himabindu Lakkaraju (NeurIPS, 2021)

Learning Under Adversarial and Interventional Shifts. - Harvineet Singh; Shalmali Joshi; Finale Doshi-Velez; Himabindu Lakkaraju (CoRR, 2021)

On the Connections between Counterfactual Explanations and Adversarial Examples. - Martin Pawelczyk; Shalmali Joshi; Chirag Agarwal; Sohini Upadhyay; Himabindu Lakkaraju (CoRR, 2021)

Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty. - Shalmali Joshi; Sonali Parbhoo; Finale Doshi-Velez (CoRR, 2021)

Towards Robust Off-Policy Evaluation via Human Inputs. - Harvineet Singh; Shalmali Joshi; Finale Doshi-Velez; Himabindu Lakkaraju (AIES, 2022)

Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. - Martin Pawelczyk; Chirag Agarwal; Shalmali Joshi; Sohini Upadhyay; Himabindu Lakkaraju (AISTATS, 2022)

Counterfactually Guided Policy Transfer in Clinical Settings. - Taylor W. Killian; Marzyeh Ghassemi; Shalmali Joshi (CHIL, 2022)

Machine Learning for Health (ML4H) 2022. - Antonio Parziale; Monica Agrawal; Shengpu Tang; Kristen Severson 001; Luis Oala; Adarsh Subbaswamy; Sayantan Kumar; Elora D. M. Schörverth; Stefan Hegselmann; Helen Zhou; Ghada Zamzmi; Purity Mugambi; et al. (ML4H@NeurIPS, 2022)

Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making. - Sonali Parbhoo; Shalmali Joshi; Finale Doshi-Velez (CoRR, 2022)

Machine Learning for Health symposium 2022 - Extended Abstract track. - Antonio Parziale; Monica Agrawal; Shalmali Joshi; Irene Y. Chen; Shengpu Tang; Luis Oala; Adarsh Subbaswamy (CoRR, 2022)

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. - 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; et al. (FAccT, 2023)

"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts. - Haoran Zhang 003; Harvineet Singh; Marzyeh Ghassemi; Shalmali Joshi (ICML, 2023)

Making machine learning matter to clinicians: model actionability in medical decision-making. - Daniel Ehrmann; Shalmali Joshi; Sebastian D. Goodfellow; Mjaye Mazwi; Danny Eytan (npj Digit. Medicine, 2023)

Learning-to-defer for sequential medical decision-making under uncertainty. - Shalmali Joshi; Sonali Parbhoo; Finale Doshi-Velez (Trans. Mach. Learn. Res., 2023)

A normative framework for artificial intelligence as a sociotechnical system in healthcare. - Melissa D. McCradden; Shalmali Joshi; James A. Anderson; Alex John London (Patterns, 2023)

Towards Safe Policy Learning under Partial Identifiability: A Causal Approach. - Shalmali Joshi; Junzhe Zhang 001; Elias Bareinboim (AAAI, 2024)

Adaptive Labeling for Efficient Out-of-distribution Model Evaluation. - Daksh Mittal; Yuanzhe Ma; Shalmali Joshi; Hongseok Namkoong (NeurIPS, 2024)

AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation. - Shalmali Joshi; Iñigo Urteaga; Wouter A. C. van Amsterdam; George Hripcsak; Pierre A. Elias; Benjamin R. C. Amor; Noémie Elhadad; James C. Fackler; Mark P. Sendak; Jenna Wiens; Kaivalya Deshpande; Yoav Wald; et al. (J. Am. Medical Informatics Assoc., 2025)

A Planning Framework for Adaptive Labeling. - Daksh Mittal; Yuanzhe Ma; Shalmali Joshi; Hongseok Namkoong (CoRR, 2025)

FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records. - Chao Pang; Vincent Jeanselme; Young Sang Choi; Xinzhuo Jiang; Zilin Jing; Aparajita Kashyap; Yuta Kobayashi; Yanwei Li; Florent Pollet; Karthik Natarajan; Shalmali Joshi (CoRR, 2025)

ICYM2I: The illusion of multimodal informativeness under missingness. - Young Sang Choi; Vincent Jeanselme; Pierre A. Elias; Shalmali Joshi (CoRR, 2025)

Path-specific effects for pulse-oximetry guided decisions in critical care. - Kevin Zhang; Yonghan Jung; Divyat Mahajan; Karthikeyan Shanmugam; Shalmali Joshi (CoRR, 2025)

Explainable AI: definition and attributes of a good explanation for health AI. - Evangelia Kyrimi; Scott McLachlan; Jared M. Wohlgemut; Zane B. Perkins; David A. Lagnado; William Marsh 001; Alexander Gimson; Ali Shafti; Ari Ercole; Amitava Banerjee; Ben Glocker; Burkhard Schafer 001; et al. (AI Ethics, 2025)