Haoran Zhang

According to our database, Haoran Zhang authored at least 23 papers between 2019 and 2024.

Collaborative distances:

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

2024

BendVLM: Test-Time Debiasing of Vision-Language Embeddings.

Walter Gerych, Haoran Zhang, Kimia Hamidieh, Eileen Pan, Maanas Sharma, Thomas Hartvigsen, Marzyeh Ghassemi

CoRR, 2024

LEMoN: Label Error Detection using Multimodal Neighbors.

Haoran Zhang, Aparna Balagopalan, Nassim Oufattole, Hyewon Jeong, Yan Wu, Jiacheng Zhu, Marzyeh Ghassemi

CoRR, 2024

Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium.

Hyewon Jeong, Sarah Jabbour, Yuzhe Yang, Rahul Thapa, Hussein Mozannar, William Jongwon Han, Nikita Mehandru, Michael Wornow, Vladislav Lialin, Xin Liu, Alejandro Lozano, Jiacheng Zhu, Rafal Dariusz Kocielnik, Keith Harrigian, Haoran Zhang, Edward Lee, Milos Vukadinovic, Aparna Balagopalan, Vincent Jeanselme, Katherine Matton, Ilker Demirel, Jason A. Fries, Parisa Rashidi, Brett K. Beaulieu-Jones, Xuhai Orson Xu, Matthew B. A. McDermott, Tristan Naumann, Monica Agrawal, Marinka Zitnik, Berk Ustun, Edward Choi, Kristen Yeom, Gamze Gürsoy, Marzyeh Ghassemi, Emma Pierson, George H. Chen, Sanjat Kanjilal, Michael Oberst, Linying Zhang, Harvineet Singh, Tom Hartvigsen, Helen Zhou, Chinasa T. Okolo

CoRR, 2024

A Closer Look at AUROC and AUPRC under Class Imbalance.

Matthew B. A. McDermott, Lasse Hyldig Hansen, Haoran Zhang, Giovanni Angelotti, Jack Gallifant

CoRR, 2024

Views Can Be Deceiving: Improved SSL Through Feature Space Augmentation.

Kimia Hamidieh, Haoran Zhang, Swami Sankaranarayanan, Marzyeh Ghassemi

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Identifying Implicit Social Biases in Vision-Language Models.

Kimia Hamidieh, Haoran Zhang, Walter Gerych, Thomas Hartvigsen, Marzyeh Ghassemi

Proceedings of the Seventh AAAI/ACM Conference on AI, Ethics, and Society (AIES-24) - Full Archival Papers, October 21-23, 2024, San Jose, California, USA, 2024

2023

The Limits of Fair Medical Imaging AI In The Wild.

Yuzhe Yang, Haoran Zhang, Judy W. Gichoya, Dina Katabi, Marzyeh Ghassemi

CoRR, 2023

Continuous Time Evidential Distributions for Irregular Time Series.

Taylor W. Killian, Haoran Zhang, Thomas Hartvigsen, Ava P. Amini

CoRR, 2023

Machine Learning for Health (ML4H) 2023.

Stefan Hegselmann, Antonio Parziale, Divya Shanmugam, Shengpu Tang, Kristen Severson, Mercy Nyamewaa Asiedu, Serina Chang, Bonaventure F. P. Dossou, Qian Huang, Fahad Kamran, Haoran Zhang, Sujay Nagaraj, Luis Oala, Shan Xu, Chinasa T. Okolo, Helen Zhou, Jessica Dafflon, Caleb Ellington, Sarah Jabbour, Hyewon Jeong, Harry Reyes Nieva, Yuzhe Yang, Ghada Zamzmi, Vishwali Mhasawade, Van Truong, Payal Chandak, Matthew Lee, Peniel Argaw, Kyle Heuton, Harvineet Singh, Thomas Hartvigsen

Proceedings of the Machine Learning for Health, 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

Change is Hard: A Closer Look at Subpopulation Shift.

Yuzhe Yang, Haoran Zhang, Dina Katabi, Marzyeh Ghassemi

Proceedings of the International Conference on Machine Learning, 2023

2022

PAN-cODE: COVID-19 forecasting using conditional latent ODEs.

Ruian Shi, Haoran Zhang, Quaid Morris

J. Am. Medical Informatics Assoc., 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

The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations.

Aparna Balagopalan, Haoran Zhang, Kimia Hamidieh, Thomas Hartvigsen, Frank Rudzicz, Marzyeh Ghassemi

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Improving the Fairness of Chest X-ray Classifiers.

Haoran Zhang, Natalie Dullerud, Karsten Roth, Lauren Oakden-Rayner, Stephen Pfohl, Marzyeh Ghassemi

Proceedings of the Conference on Health, Inference, and Learning, 2022

2021

A comparison of approaches to improve worst-case predictive model performance over patient subpopulations.

Stephen R. Pfohl, Haoran Zhang, Yizhe Xu, Agata Foryciarz, Marzyeh Ghassemi, Nigam H. Shah

CoRR, 2021

Reading Race: AI Recognises Patient's Racial Identity In Medical Images.

Imon Banerjee, Ananth Reddy Bhimireddy, John L. Burns, Leo Anthony Celi, Li-Ching Chen, Ramon Correa, Natalie Dullerud, Marzyeh Ghassemi, Shih-Cheng Huang, Po-Chih Kuo, Matthew P. Lungren, Lyle J. Palmer, Brandon J. Price, Saptarshi Purkayastha, Ayis Pyrros, Luke Oakden-Rayner, Chima Okechukwu, Laleh Seyyed-Kalantari, Hari Trivedi, Ryan Wang, Zachary Zaiman, Haoran Zhang, Judy W. Gichoya

CoRR, 2021

Learning Optimal Predictive Checklists.

Haoran Zhang, Quaid Morris, Berk Ustun, Marzyeh Ghassemi

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

An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare.

Taylor W. Killian, Haoran Zhang, Jayakumar Subramanian, Mehdi Fatemi, Marzyeh Ghassemi

Proceedings of the Machine Learning for Health Workshop, 2020

Hurtful words: quantifying biases in clinical contextual word embeddings.

Haoran Zhang, Amy X. Lu, Mohamed Abdalla, Matthew B. A. McDermott, Marzyeh Ghassemi

Proceedings of the ACM CHIL '20: ACM Conference on Health, 2020

2019

Categorizing Emails Using Machine Learning with Textual Features.

Haoran Zhang, Jagadish Rangrej, Saad Rais, Michael Hillmer, Frank Rudzicz, Kamil Malikov

Proceedings of the Advances in Artificial Intelligence, 2019