960 citations · 3.4k across the 60 of their papers we have counts for
64 papers
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras +2
Designing deep neural network classifiers that perform robustly on distributions differing from the available training data is an active area of machine learning research. However,…
The Limits of Fair Medical Imaging AI In The Wild
Yuzhe Yang, Haoran Zhang, Judy W Gichoya +2
As artificial intelligence (AI) rapidly approaches human-level performance in medical imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Prior…
Event-Based Contrastive Learning for Medical Time Series
Hyewon Jeong, Nassim Oufattole, Matthew Mcdermott +4
In clinical practice, one often needs to identify whether a patient is at high risk of adverse outcomes after some key medical event. For example, quantifying the risk of adverse o…
Deep Metric Learning for the Hemodynamics Inference with Electrocardiogram Signals
Hyewon Jeong, Collin M. Stultz, Marzyeh Ghassemi
Heart failure is a debilitating condition that affects millions of people worldwide and has a significant impact on their quality of life and mortality rates. An objective assessme…
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
VisAlign: Dataset for Measuring the Degree of Alignment between AI and Humans in Visual Perception
Jiyoung Lee, Seungho Kim, Seunghyun Won +6
AI alignment refers to models acting towards human-intended goals, preferences, or ethical principles. Given that most large-scale deep learning models act as black boxes and canno…
Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit
Ming Ying Yang, Gloria Hyunjung Kwak, Tom Pollard +2
Social determinants of health (SDOH) -- the conditions in which people live, grow, and age -- play a crucial role in a person's health and well-being. There is a large, compelling…
In the Name of Fairness: Assessing the Bias in Clinical Record De-identification
Yuxin Xiao, Shulammite Lim, Tom Joseph Pollard +1
Data sharing is crucial for open science and reproducible research, but the legal sharing of clinical data requires the removal of protected health information from electronic heal…
AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT
Edward H. Lee, Brendan Kelly, Emre Altinmakas +27
While it is well known that population differences from genetics, sex, race, and environmental factors contribute to disease, AI studies in medicine have largely focused on locoreg…
Improving Mutual Information Estimation with Annealed and Energy-Based Bounds
Rob Brekelmans, Sicong Huang, Marzyeh Ghassemi +3
Mutual information (MI) is a fundamental quantity in information theory and machine learning. However, direct estimation of MI is intractable, even if the true joint probability de…
Change is Hard: A Closer Look at Subpopulation Shift
Yuzhe Yang, Haoran Zhang, Dina Katabi +1
Machine learning models often perform poorly on subgroups that are underrepresented in the training data. Yet, little is understood on the variation in mechanisms that cause subpop…
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning
Taylor W. Killian, Sonali Parbhoo, Marzyeh Ghassemi
In safety-critical decision-making scenarios being able to identify worst-case outcomes, or dead-ends is crucial in order to develop safe and reliable policies in practice. These s…
Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors
Thomas Hartvigsen, Swami Sankaranarayanan, Hamid Palangi +2
Deployed language models decay over time due to shifting inputs, changing user needs, or emergent world-knowledge gaps. When such problems are identified, we want to make targeted…
"Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts
Haoran Zhang, Harvineet Singh, Marzyeh Ghassemi +1
Machine learning models frequently experience performance drops under distribution shifts. The underlying cause of such shifts may be multiple simultaneous factors such as changes…
If Influence Functions are the Answer, Then What is the Question?
Juhan Bae, Nathan Ng, Alston Lo +2
Influence functions efficiently estimate the effect of removing a single training data point on a model's learned parameters. While influence estimates align well with leave-one-ou…
Predicting Out-of-Domain Generalization with Neighborhood Invariance
Nathan Ng, Neha Hulkund, Kyunghyun Cho +1
Developing and deploying machine learning models safely depends on the ability to characterize and compare their abilities to generalize to new environments. Although recent work h…
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
Aparna Balagopalan, Haoran Zhang, Kimia Hamidieh +3
Machine learning models in safety-critical settings like healthcare are often blackboxes: they contain a large number of parameters which are not transparent to users. Post-hoc exp…
When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction
Vinith M. Suriyakumar, Marzyeh Ghassemi, Berk Ustun
Machine learning models are often personalized with categorical attributes that are protected, sensitive, self-reported, or costly to acquire. In this work, we show models that are…
Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations
Hammaad Adam, Ming Ying Yang, Kenrick Cato +6
Clinical notes are becoming an increasingly important data source for machine learning (ML) applications in healthcare. Prior research has shown that deploying ML models can perpet…
Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning
Natalie Dullerud, Karsten Roth, Kimia Hamidieh +2
Deep metric learning (DML) enables learning with less supervision through its emphasis on the similarity structure of representations. There has been much work on improving general…
Improving the Fairness of Chest X-ray Classifiers
Haoran Zhang, Natalie Dullerud, Karsten Roth +3
Deep learning models have reached or surpassed human-level performance in the field of medical imaging, especially in disease diagnosis using chest x-rays. However, prior work has…
Semi-Markov Offline Reinforcement Learning for Healthcare
Mehdi Fatemi, Mary Wu, Jeremy Petch +5
Reinforcement learning (RL) tasks are typically framed as Markov Decision Processes (MDPs), assuming that decisions are made at fixed time intervals. However, many applications of…
Learning Optimal Predictive Checklists
Haoran Zhang, Quaid Morris, Berk Ustun +1
Checklists are simple decision aids that are often used to promote safety and reliability in clinical applications. In this paper, we present a method to learn checklists for clini…
Quantifying the Task-Specific Information in Text-Based Classifications
Zining Zhu, Aparna Balagopalan, Marzyeh Ghassemi +1
Recently, neural natural language models have attained state-of-the-art performance on a wide variety of tasks, but the high performance can result from superficial, surface-level…