164 citations · 188 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights
Melanie F. Pradier, Weiwei Pan, Jiayu Yao +2
As machine learning systems get widely adopted for high-stake decisions, quantifying uncertainty over predictions becomes crucial. While modern neural networks are making remarkabl…