Shalmali Joshi

According to our database, Shalmali Joshi authored at least 35 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Towards Safe Policy Learning under Partial Identifiability: A Causal Approach.

Shalmali Joshi, Junzhe Zhang, Elias Bareinboim

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

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, November, 2023

Learning-to-defer for sequential medical decision-making under uncertainty.

Shalmali Joshi, Sonali Parbhoo, Finale Doshi-Velez

Trans. Mach. Learn. Res., 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

"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts.

Haoran Zhang, Harvineet Singh, Marzyeh Ghassemi, Shalmali Joshi

Proceedings of the International Conference on Machine Learning, 2023

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, Irvine Sihlahla, Oswa Shafei, Anna Goldenberg

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

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

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 (ML4H) 2022.

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

Proceedings of the Machine Learning for Health, 2022

Counterfactually Guided Policy Transfer in Clinical Settings.

Taylor W. Killian, Marzyeh Ghassemi, Shalmali Joshi

Proceedings of the Conference on Health, Inference, and Learning, 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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Towards Robust Off-Policy Evaluation via Human Inputs.

Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju

Proceedings of the AIES '22: AAAI/ACM Conference on AI, Ethics, and Society, Oxford, United Kingdom, May 19, 2022

2021

Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty.

Shalmali Joshi, Sonali Parbhoo, Finale Doshi-Velez

CoRR, 2021

On the Connections between Counterfactual Explanations and Adversarial Examples.

Martin Pawelczyk, Shalmali Joshi, Chirag Agarwal, Sohini Upadhyay, Himabindu Lakkaraju

CoRR, 2021

Learning Under Adversarial and Interventional Shifts.

Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju

CoRR, 2021

Towards Robust and Reliable Algorithmic Recourse.

Sohini Upadhyay, Shalmali Joshi, Himabindu Lakkaraju

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 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

Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing.

Sindhu C. M. Gowda, Shalmali Joshi, Haoran Zhang, Marzyeh Ghassemi

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

An empirical framework for domain generalization in clinical settings.

Haoran Zhang, Natalie Dullerud, Laleh Seyyed-Kalantari, Quaid Morris, Shalmali Joshi, Marzyeh Ghassemi

Proceedings of the ACM CHIL '21: ACM Conference on Health, 2021

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

Probabilistic Machine Learning for Healthcare.

Irene Y. Chen, Shalmali Joshi, Marzyeh Ghassemi, Rajesh Ranganath

CoRR, 2020

Ethical Machine Learning in Health Care.

Irene Y. Chen, Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, Marzyeh Ghassemi

CoRR, 2020

Sequential Explanations with Mental Model-Based Policies.

Arnold Y. S. Yeung, Shalmali Joshi, Joseph Jay Williams, Frank Rudzicz

CoRR, 2020

What went wrong and when? Instance-wise Feature Importance for Time-series Models.

Sana Tonekaboni, Shalmali Joshi, David Duvenaud, Anna Goldenberg

CoRR, 2020

Confounding Feature Acquisition for Causal Effect Estimation.

Shirly Wang, Seung Eun Yi, Shalmali Joshi, Marzyeh Ghassemi

Proceedings of the Machine Learning for Health Workshop, 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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Proceedings of the AIES '20: AAAI/ACM Conference on AI, 2020

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

What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use.

Sana Tonekaboni, Shalmali Joshi, Melissa D. McCradden, Anna Goldenberg

Proceedings of the Machine Learning for Healthcare Conference, 2019

2018

xGEMs: Generating Examplars to Explain Black-Box Models.

Shalmali Joshi, Oluwasanmi Koyejo, Been Kim, Joydeep Ghosh

CoRR, 2018

Co-regularized Monotone Retargeting for Semi-supervised LeTOR.

Shalmali Joshi, Rajiv Khanna, Joydeep Ghosh

Proceedings of the 2018 SIAM International Conference on Data Mining, 2018

2016

Rényi divergence minimization based co-regularized multiview clustering.

Shalmali Joshi, Joydeep Ghosh, Mark Reid, Oluwasanmi Koyejo

Mach. Learn., 2016

Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization.

Shalmali Joshi, Suriya Gunasekar, David A. Sontag, Joydeep Ghosh

Proceedings of the 1st Machine Learning in Health Care, 2016

2015

Simultaneous Prognosis and Exploratory Analysis of Multiple Chronic Conditions Using Clinical Notes.

Shalmali Joshi, Oluwasanmi Koyejo, Kristine Resurreccion, Joydeep Ghosh

Proceedings of the 2015 International Conference on Healthcare Informatics, 2015

Simultaneous Prognosis of Multiple Chronic Conditions from Heterogeneous EHR Data.

Shalmali Joshi, Oluwasanmi Koyejo, Joydeep Ghosh

Proceedings of the 2015 International Conference on Healthcare Informatics, 2015