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ORCIDAndrew C. Miller
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2020 – today
- 2024
[c12]Salar Abbaspourazad, Oussama Elachqar, Andrew C. Miller, Saba Emrani, Udhyakumar Nallasamy, Ian Shapiro:
Large-scale Training of Foundation Models for Wearable Biosignals. ICLR 2024- 2023
- [j2]Achille Nazaret, Sana Tonekaboni, Gregory Darnell, Shirley You Ren, Guillermo Sapiro, Andrew C. Miller
:
Modeling personalized heart rate response to exercise and environmental factors with wearables data. npj Digit. Medicine 6 (2023) - [i12]Antoine Wehenkel, Jens Behrmann, Andrew C. Miller, Guillermo Sapiro, Ozan Sener, Marco Cuturi, Jörn-Henrik Jacobsen:
Simulation-based Inference for Cardiovascular Models. CoRR abs/2307.13918 (2023) - [i11]Salar Abbaspourazad, Oussama Elachqar, Andrew C. Miller, Saba Emrani, Udhyakumar Nallasamy, Ian Shapiro:
Large-scale Training of Foundation Models for Wearable Biosignals. CoRR abs/2312.05409 (2023) - 2022
- [c11]Mark Goldstein, Jörn-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew C. Miller:
Learning Invariant Representations with Missing Data. CLeaR 2022: 290-301 - 2021
- [j1]Sean Jewell, Joseph Futoma, Lauren Hannah, Andrew C. Miller, Nicholas J. Foti, Emily B. Fox:
It's complicated: characterizing the time-varying relationship between cell phone mobility and COVID-19 spread in the US. npj Digit. Medicine 4 (2021) - [c10]Andrew C. Miller, Leon A. Gatys, Joseph Futoma, Emily B. Fox:
Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance. MLHC 2021: 308-336 - [i10]Andrew C. Miller, Nicholas J. Foti, Emily B. Fox:
Breiman's two cultures: You don't have to choose sides. CoRR abs/2104.12219 (2021) - [i9]Andrew C. Miller, Leon A. Gatys, Joseph Futoma, Emily B. Fox:
Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance. CoRR abs/2104.12231 (2021) - [i8]Mark Goldstein, Jörn-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Manas Puli, Rajesh Ranganath, Andrew C. Miller:
Learning Invariant Representations with Missing Data. CoRR abs/2112.00881 (2021) - 2020
- [c9]Andrew C. Miller, Nicholas J. Foti, Emily B. Fox:
Learning Insulin-Glucose Dynamics in the Wild. MLHC 2020: 172-197 - [i7]Andrew C. Miller, Nicholas J. Foti, Emily B. Fox:
Learning Insulin-Glucose Dynamics in the Wild. CoRR abs/2008.02852 (2020) - [i6]Jeffrey Chan, Andrew C. Miller, Emily B. Fox:
Representing and Denoising Wearable ECG Recordings. CoRR abs/2012.00110 (2020)
2010 – 2019
- 2019
- [c8]Andrew C. Miller, Ziad Obermeyer, John P. Cunningham, Sendhil Mullainathan:
Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography. ICML 2019: 4585-4594 - 2018
- [c7]Yoon Kim, Sam Wiseman, Andrew C. Miller, David A. Sontag, Alexander M. Rush:
Semi-Amortized Variational Autoencoders. ICML 2018: 2683-2692 - [i5]Yoon Kim, Sam Wiseman, Andrew C. Miller, David A. Sontag, Alexander M. Rush:
Semi-Amortized Variational Autoencoders. CoRR abs/1802.02550 (2018) - [i4]Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat:
Approximate Inference for Constructing Astronomical Catalogs from Images. CoRR abs/1803.00113 (2018) - [i3]Andrew C. Miller, Ziad Obermeyer, David M. Blei, John P. Cunningham, Sendhil Mullainathan:
A Probabilistic Model of Cardiac Physiology and Electrocardiograms. CoRR abs/1812.00209 (2018) - [i2]Andrew C. Miller, Ziad Obermeyer, Sendhil Mullainathan:
Measuring the Stability of EHR- and EKG-based Predictive Models. CoRR abs/1812.00210 (2018) - 2017
- [c6]Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski:
Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. AISTATS 2017: 914-922 - [c5]Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams:
Variational Boosting: Iteratively Refining Posterior Approximations. ICML 2017: 2420-2429 - [c4]Andrew C. Miller, Nick Foti, Alexander D'Amour, Ryan P. Adams:
Reducing Reparameterization Gradient Variance. NIPS 2017: 3708-3718 - 2016
- [i1]Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams:
Variational Boosting: Iteratively Refining Posterior Approximations. CoRR abs/1611.06585 (2016) - 2015
- [c3]Jeffrey Regier, Andrew C. Miller, Jon McAuliffe, Ryan P. Adams, Matthew D. Hoffman, Dustin Lang, David Schlegel, Prabhat:
Celeste: Variational inference for a generative model of astronomical images. ICML 2015: 2095-2103 - [c2]Andrew C. Miller, Albert Wu, Jeffrey Regier, Jon McAuliffe, Dustin Lang, Prabhat, David Schlegel, Ryan P. Adams:
A Gaussian Process Model of Quasar Spectral Energy Distributions. NIPS 2015: 2494-2502 - 2014
- [c1]Andrew C. Miller, Luke Bornn, Ryan P. Adams, Kirk Goldsberry:
Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball. ICML 2014: 235-243
Coauthor Index
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