Andrew C. Miller

According to our database, Andrew C. Miller authored at least 20 papers between 2014 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2023

Modeling personalized heart rate response to exercise and environmental factors with wearables data.

Achille Nazaret, Sana Tonekaboni, Gregory Darnell, Shirley You Ren, Guillermo Sapiro, Andrew C. Miller

npj Digit. Medicine, 2023

Large-scale Training of Foundation Models for Wearable Biosignals.

Salar Abbaspourazad, Oussama Elachqar, Andrew C. Miller, Saba Emrani, Udhyakumar Nallasamy, Ian Shapiro

CoRR, 2023

Simulation-based Inference for Cardiovascular Models.

Antoine Wehenkel, Jens Behrmann, Andrew C. Miller, Guillermo Sapiro, Ozan Sener, Marco Cuturi, Jörn-Henrik Jacobsen

CoRR, 2023

2022

Learning Invariant Representations with Missing Data.

Mark Goldstein, Jörn-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew C. Miller

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

2021

It's complicated: characterizing the time-varying relationship between cell phone mobility and COVID-19 spread in the US.

Sean Jewell, Joseph Futoma, Lauren Hannah, Andrew C. Miller, Nicholas J. Foti, Emily B. Fox

npj Digit. Medicine, 2021

Breiman's two cultures: You don't have to choose sides.

Andrew C. Miller, Nicholas J. Foti, Emily B. Fox

CoRR, 2021

Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance.

Andrew C. Miller, Leon A. Gatys, Joseph Futoma, Emily B. Fox

Proceedings of the Machine Learning for Healthcare Conference, 2021

2020

Representing and Denoising Wearable ECG Recordings.

Jeffrey Chan, Andrew C. Miller, Emily B. Fox

CoRR, 2020

Learning Insulin-Glucose Dynamics in the Wild.

Andrew C. Miller, Nicholas J. Foti, Emily B. Fox

Proceedings of the Machine Learning for Healthcare Conference, 2020

2019

Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography.

Andrew C. Miller, Ziad Obermeyer, John P. Cunningham, Sendhil Mullainathan

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Measuring the Stability of EHR- and EKG-based Predictive Models.

Andrew C. Miller, Ziad Obermeyer, Sendhil Mullainathan

CoRR, 2018

A Probabilistic Model of Cardiac Physiology and Electrocardiograms.

Andrew C. Miller, Ziad Obermeyer, David M. Blei, John P. Cunningham, Sendhil Mullainathan

CoRR, 2018

Approximate Inference for Constructing Astronomical Catalogs from Images.

Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat

CoRR, 2018

Semi-Amortized Variational Autoencoders.

Yoon Kim, Sam Wiseman, Andrew C. Miller, David A. Sontag, Alexander M. Rush

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Reducing Reparameterization Gradient Variance.

Andrew C. Miller, Nick Foti, Alexander D'Amour, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Variational Boosting: Iteratively Refining Posterior Approximations.

Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams

Proceedings of the 34th International Conference on Machine Learning, 2017

Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems.

Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2015

A Gaussian Process Model of Quasar Spectral Energy Distributions.

Andrew C. Miller, Albert Wu, Jeffrey Regier, Jon McAuliffe, Dustin Lang, Prabhat, David Schlegel, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Celeste: Variational inference for a generative model of astronomical images.

Jeffrey Regier, Andrew C. Miller, Jon McAuliffe, Ryan P. Adams, Matthew D. Hoffman, Dustin Lang, David Schlegel, Prabhat

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball.

Andrew C. Miller, Luke Bornn, Ryan P. Adams, Kirk Goldsberry

Proceedings of the 31th International Conference on Machine Learning, 2014