73 citations · 104 across the 9 of their papers we have counts for
11 papers · 1 filter
Modeling Mobile Health Users as Reinforcement Learning Agents
Eura Shin, Siddharth Swaroop, Weiwei Pan +2
Mobile health (mHealth) technologies empower patients to adopt/maintain healthy behaviors in their daily lives, by providing interventions (e.g. push notifications) tailored to the…
Wide Mean-Field Bayesian Neural Networks Ignore the Data
Beau Coker, Wessel P. Bruinsma, David R. Burt +2
Bayesian neural networks (BNNs) combine the expressive power of deep learning with the advantages of Bayesian formalism. In recent years, the analysis of wide, deep BNNs has provid…
Promises and Pitfalls of Black-Box Concept Learning Models
Anita Mahinpei, Justin Clark, Isaac Lage +2
Machine learning models that incorporate concept learning as an intermediate step in their decision making process can match the performance of black-box predictive models while re…
Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data
Beau Coker, Weiwei Pan, Finale Doshi-Velez
Variational inference enables approximate posterior inference of the highly over-parameterized neural networks that are popular in modern machine learning. Unfortunately, such post…
Ensembles of Locally Independent Prediction Models
Andrew Slavin Ross, Weiwei Pan, Leo Anthony Celi +1
Ensembles depend on diversity for improved performance. Many ensemble training methods, therefore, attempt to optimize for diversity, which they almost always define in terms of di…
Quality of Uncertainty Quantification for Bayesian Neural Network Inference
Jiayu Yao, Weiwei Pan, Soumya Ghosh +1
Bayesian Neural Networks (BNNs) place priors over the parameters in a neural network. Inference in BNNs, however, is difficult; all inference methods for BNNs are approximate. In t…