no code implementations • 12 Sep 2023 • Prateek Jaiswal, Debdeep Pati, Anirban Bhattacharya, Bani K. Mallick
Both the sub-Gaussian and exponential family models satisfy our general conditions on the reward distribution.
no code implementations • 7 Sep 2022 • Sudipta Banerjee, Prateek Jaiswal, Arun Ross
In this work, we propose a novel de-morphing method that can recover images of both identities simultaneously from a single morphed face image without needing a reference image or prior information about the morphing process.
no code implementations • 23 Jun 2021 • Prateek Jaiswal, Harsha Honnappa, Vinayak A. Rao
Bayesian posteriors afford a principled mechanism to incorporate data and prior knowledge into stochastic optimization problems.
no code implementations • 1 Jan 2021 • Prateek Jaiswal, Harsha Honnappa, Vinayak Rao
This paper proposes a stochastic variational inference (SVI) method for computing an approximate posterior path measure of a Cox process.
no code implementations • pproximateinference AABI Symposium 2019 • Prateek Jaiswal, Harsha Honnappa, Vinayak A. Rao
We study system design problems stated as parameterized stochastic programs with a chance-constraint set.
no code implementations • 4 Nov 2019 • Prateek Jaiswal, Harsha Honnappa, Vinayak A. Rao
We also establish the asymptotic consistency of decision rules obtained from a `naive' variational Bayesian procedure.
no code implementations • 5 Feb 2019 • Prateek Jaiswal, Vinayak A. Rao, Harsha Honnappa
We study the asymptotic consistency properties of $\alpha$-R\'enyi approximate posteriors, a class of variational Bayesian methods that approximate an intractable Bayesian posterior with a member of a tractable family of distributions, the member chosen to minimize the $\alpha$-R\'enyi divergence from the true posterior.