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Reconstructing the Universe with Variational self-Boosted Sampling
Chirag Modi · Yin Li · David Blei

Fri Jul 22 11:30 AM -- 11:45 AM (PDT) @
in Machine Learning for Astrophysics »
Event URL: https://ml4astro.github.io/icml2022/assets/14.pdf »

Forward modeling approaches in cosmology seek to reconstruct the initial conditions at the beginning of the Universe from the observed survey data.However the high dimensionality of the parameter space poses a challenge to explore the full posterior with traditional algorithms such as Hamiltonian Monte Carlo (HMC) and variational inference (VI).Here we develop a hybrid scheme called variational self-boosted sampling (VBS)that learns a variational approximation for the proposal distribution of HMC with samples generated on the fly, and in turn generates independent samples as proposals for MCMC chain to reduce their auto-correlation length. We use a normalizing flow with Fourier space convolutions as our variational distribution to scale to high dimensions of interest.We show that after a short initial warm-up and training phase, VBS generates better quality of samples than simple VI and reduces the correlation length in the sampling phase by a factor of 10-50 over using only HMC.

Author Information

Chirag Modi (Flatiron Institute)
Yin Li (Flatiron Institute)
David Blei (Columbia University)

David Blei is a Professor of Statistics and Computer Science at Columbia University, and a member of the Columbia Data Science Institute. His research is in statistical machine learning, involving probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference algorithms for massive data. He works on a variety of applications, including text, images, music, social networks, user behavior, and scientific data. David has received several awards for his research, including a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), Blavatnik Faculty Award (2013), and ACM-Infosys Foundation Award (2013). He is a fellow of the ACM.

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