8 citations · 25 across the 12 of their papers we have counts for
13 papers
Individual Fairness under Uncertainty
Wenbin Zhang, Zichong Wang, Juyong Kim +4
Algorithmic fairness, the research field of making machine learning (ML) algorithms fair, is an established area in ML. As ML technologies expand their application domains, includi…
Fair Decision-making Under Uncertainty
Wenbin Zhang, Jeremy C. Weiss
There has been concern within the artificial intelligence (AI) community and the broader society regarding the potential lack of fairness of AI-based decision-making systems. Surpr…
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographics
Shu Hu, George H. Chen
We propose a general approach for training survival analysis models that minimizes a worst-case error across all subpopulations that are large enough (occurring with at least a use…
Learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19
Helen Zhou, Cheng Cheng, Kelly J. Shields +4
With COVID-19 now pervasive, identification of high-risk individuals is crucial. Using data from a major healthcare provider in Southwestern Pennsylvania, we develop survival model…
Longitudinal Fairness with Censorship
Wenbin Zhang, Jeremy C. Weiss
Recent works in artificial intelligence fairness attempt to mitigate discrimination by proposing constrained optimization programs that achieve parity for some fairness statistic.…
Fairness Amidst Non-IID Graph Data: A Literature Review
Wenbin Zhang, Shuigeng Zhou, Toby Walsh +1
The growing importance of understanding and addressing algorithmic bias in artificial intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the…
FARF: A Fair and Adaptive Random Forests Classifier
Wenbin Zhang, Albert Bifet, Xiangliang Zhang +2
As Artificial Intelligence (AI) is used in more applications, the need to consider and mitigate biases from the learned models has followed. Most works in developing fair learning…
Unpacking the Drop in COVID-19 Case Fatality Rates: A Study of National and Florida Line-Level Data
Cheng Cheng, Helen Zhou, Jeremy C. Weiss +1
Since the COVID-19 pandemic first reached the United States, the case fatality rate has fallen precipitously. Several possible explanations have been floated, including greater det…
Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate
George H. Chen, Linhong Li, Ren Zuo +2
We present a neural network framework for learning a survival model to predict a time-to-event outcome while simultaneously learning a topic model that reveals feature relationship…
Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist Clinicians with COVID-19 ECMO Planning
Helen Zhou, Cheng Cheng, Zachary C. Lipton +2
Respiratory complications due to coronavirus disease COVID-19 have claimed tens of thousands of lives in 2020. Many cases of COVID-19 escalate from Severe Acute Respiratory Syndrom…
ESPRIT: Explaining Solutions to Physical Reasoning Tasks
Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan +7
Neural networks lack the ability to reason about qualitative physics and so cannot generalize to scenarios and tasks unseen during training. We propose ESPRIT, a framework for comm…
Harmonic Mean Point Processes: Proportional Rate Error Minimization for Obtundation Prediction
Yoonjung Kim, Jeremy C. Weiss
In healthcare, the highest risk individuals for morbidity and mortality are rarely those with the greatest modifiable risk. By contrast, many machine learning formulations implicit…
Survival-Supervised Topic Modeling with Anchor Words: Characterizing Pancreatitis Outcomes
George H. Chen, Jeremy C. Weiss
We introduce a new approach for topic modeling that is supervised by survival analysis. Specifically, we build on recent work on unsupervised topic modeling with so-called anchor w…