164 citations · 237 across the 9 of their papers we have counts for
9 papers
Repairing Neural Networks by Leaving the Right Past Behind
Ryutaro Tanno, Melanie F. Pradier, Aditya Nori +1
Prediction failures of machine learning models often arise from deficiencies in training data, such as incorrect labels, outliers, and selection biases. However, such data points t…
Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens
Maia Jacobs, Jeffrey He, Melanie F. Pradier +6
Major depressive disorder is a debilitating disease affecting 264 million people worldwide. While many antidepressant medications are available, few clinical guidelines support cho…
Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise as much as Possible
Melanie F. Pradier, Javier Zazo, Sonali Parbhoo +3
We propose Preferential MoE, a novel human-ML mixture-of-experts model that augments human expertise in decision making with a data-based classifier only when necessary for predict…
Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks
Beau Coker, Melanie F. Pradier, Finale Doshi-Velez
While Bayesian neural networks have many appealing characteristics, current priors do not easily allow users to specify basic properties such as expected lengthscale or amplitude v…
Output-Constrained Bayesian Neural Networks
Wanqian Yang, Lars Lorch, Moritz A. Graule +5
Bayesian neural network (BNN) priors are defined in parameter space, making it hard to encode prior knowledge expressed in function space. We formulate a prior that incorporates fu…
Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies
Stefan G. Stark, Stephanie L. Hyland, Melanie F. Pradier +5
The recent adoption of Electronic Health Records (EHRs) by health care providers has introduced an important source of data that provides detailed and highly specific insights into…