2 citations · 3 across the 10 of their papers we have counts for
12 papers
Dual-Phase Flow Matching for Probabilistic Source Detection and Measurement in Astronomical Images
Yicun Duan, Jeffrey Regier
Using the largest digital camera ever built, the NSF-DOE Vera C. Rubin Observatory's 10-year Legacy Survey of Space and Time (LSST) will image over half the night sky. Most astrono…
Neural Posterior Estimation for Tomographic Weak Lensing Mass Mapping
Tim White, Shreyas Chandrashekaran, Camille Avestruz +2
Weak gravitational lensing shear and convergence trace the distribution of baryonic and dark matter across space, making them a powerful probe of cosmic structure. Inferring shear…
Neural Posterior Estimation for Inferring Weak Lensing Shear
Tim White, Dingrui Tao, Camille Avestruz +2
The prevailing approach to inferring weak gravitational lensing shear from images involves detecting galaxies, estimating their ellipticities, and calibrating these estimates to co…
Simulation-Based Inference for Probabilistic Galaxy Detection and Deblending
Ismael Mendoza, Derek Hansen, Runjing Liu +6
Stage-IV dark energy wide-field surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will observe an unprecedented number density of galaxies. As…
Neural Posterior Estimation for Cataloging Astronomical Images from the Legacy Survey of Space and Time
Yicun Duan, Xinyue Li, Camille Avestruz +2
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will commence full-scale operations in 2026, yielding an unprecedented volume of astronomical images. Construct…
Neural Posterior Estimation with Autoregressive Tiling for Detecting Objects in Astronomical Images
Jeffrey Regier
Upcoming astronomical surveys will produce petabytes of high-resolution images of the night sky, providing information about billions of stars and galaxies. Detecting and character…
A new tail bound for the sum of bounded independent random variables
Jackson Loper, Jeffrey Regier
We construct a new tail bound for the sum of independent random variables for situations in which the expected value of the sum is known and each random variable lies within a spec…
Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions
Aakash Patel, Tianqing Zhang, Camille Avestruz +2
Neural posterior estimation (NPE), a type of amortized variational inference, is a computationally efficient means of constructing probabilistic catalogs of light sources from astr…
Globally Convergent Variational Inference
Declan McNamara, Jackson Loper, Jeffrey Regier
In variational inference (VI), an approximation of the posterior distribution is selected from a family of distributions through numerical optimization. With the most common variat…
Neural Posterior Estimation for Stochastic Epidemic Modeling
Prayag Chatha, Fan Bu, Jeffrey Regier +2
Stochastic infectious disease models capture uncertainty in public health outcomes and have become increasingly popular in epidemiological practice. However, calibrating these mode…
Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference
Declan McNamara, Jackson Loper, Jeffrey Regier
For training an encoder network to perform amortized variational inference, the Kullback-Leibler (KL) divergence from the exact posterior to its approximation, known as the inclusi…
Variational Inference with Coverage Guarantees in Simulation-Based Inference
Yash Patel, Declan McNamara, Jackson Loper +2
Amortized variational inference is an often employed framework in simulation-based inference that produces a posterior approximation that can be rapidly computed given any new obse…