Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball. - Andrew C. Miller; Luke Bornn; Ryan P. Adams; Kirk Goldsberry (ICML, 2014)

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 (ICML, 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 (NIPS, 2015)

Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. - Scott W. Linderman; Matthew J. Johnson 002; Andrew C. Miller; Ryan P. Adams; David M. Blei; Liam Paninski (AISTATS, 2017)

Variational Boosting: Iteratively Refining Posterior Approximations. - Andrew C. Miller; Nicholas J. Foti; Ryan P. Adams (ICML, 2017)

Reducing Reparameterization Gradient Variance. - Andrew C. Miller; Nick Foti; Alexander D'Amour; Ryan P. Adams (NIPS, 2017)

Semi-Amortized Variational Autoencoders. - Yoon Kim; Sam Wiseman; Andrew C. Miller; David A. Sontag; Alexander M. Rush (ICML, 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)

A Probabilistic Model of Cardiac Physiology and Electrocardiograms. - Andrew C. Miller; Ziad Obermeyer; David M. Blei; John P. Cunningham; Sendhil Mullainathan (CoRR, 2018)

Measuring the Stability of EHR- and EKG-based Predictive Models. - Andrew C. Miller; Ziad Obermeyer; Sendhil Mullainathan (CoRR, 2018)

Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography. - Andrew C. Miller; Ziad Obermeyer; John P. Cunningham; Sendhil Mullainathan (ICML, 2019)

Learning Insulin-Glucose Dynamics in the Wild. - Andrew C. Miller; Nicholas J. Foti; Emily B. Fox (MLHC, 2020)

Representing and Denoising Wearable ECG Recordings. - Jeffrey Chan; Andrew C. Miller; Emily B. Fox (CoRR, 2020)

Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance. - Andrew C. Miller; Leon A. Gatys; Joseph Futoma; Emily B. Fox (MLHC, 2021)

Breiman's two cultures: You don't have to choose sides. - Andrew C. Miller; Nicholas J. Foti; Emily B. Fox (CoRR, 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)

Learning Invariant Representations with Missing Data. - Mark Goldstein; Jörn-Henrik Jacobsen; Olina Chau; Adriel Saporta; Aahlad Manas Puli; Rajesh Ranganath; Andrew C. Miller (CLeaR, 2022)

Simulation-based Inference for Cardiovascular Models. - Antoine Wehenkel; Jens Behrmann; Andrew C. Miller; Guillermo Sapiro; Ozan Sener; Marco Cuturi; Jörn-Henrik Jacobsen (CoRR, 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 (ICLR, 2024)

Considerations for Distribution Shift Robustness of Diagnostic Models in Healthcare. - Arno Blaas; Adam Golinski; Andrew C. Miller; Luca Zappella; Jörn-Henrik Jacobsen; Christina Heinze-Deml (CoRR, 2024)

Wearable Accelerometer Foundation Models for Health via Knowledge Distillation. - Salar Abbaspourazad; Anshuman Mishra; Joseph Futoma; Andrew C. Miller; Ian Shapiro (CoRR, 2024)

Do LLMs "know" internally when they follow instructions? - Juyeon Heo; Christina Heinze-Deml; Oussama Elachqar; Kwan Ho Ryan Chan; Shirley You Ren; Andrew C. Miller; Udhyakumar Nallasamy; Jaya Narain (ICLR, 2025)