15 papers
Adaptive Resolution for Finite-Rank Gaussian Processes
Jaehoan Kim, Anirban Bhattacharya, Debdeep Pati
Finite-rank approximations are widely used to scale Gaussian process (GP) regression, but their posterior behavior can differ from that of the corresponding parent GP prior. We stu…
Robust Simulation Based Inference Through Robust Optimal Transport
Peter Matthew Jacobs, Lekha Patel, Anirban Bhattacharya +1
When a statistical model lacks analytically tractable likelihoods, parametric statistical inference based on data generated from an unknown underlying distrib…
A Generalized Tangent Approximation based Variational Inference Framework for Strongly Super-Gaussian Likelihoods
Somjit Roy, Pritam Dey, Debdeep Pati +1
Variational inference, as an alternative to Markov chain Monte Carlo sampling, has played a transformative role in enabling scalable computation for complex Bayesian models. Nevert…
Stability of Sequential and Parallel Coordinate Ascent Variational Inference
Debdeep Pati
We highlight a striking difference in behavior between two widely used variants of coordinate ascent variational inference: the sequential and parallel algorithms. While such diffe…
Frequentist Regret Analysis of Gaussian Process Thompson Sampling via Fractional Posteriors
Somjit Roy, Prateek Jaiswal, Anirban Bhattacharya +2
We study Gaussian Process Thompson Sampling (GP-TS) for sequential decision-making over compact, continuous action spaces and provide a frequentist regret analysis based on fractio…
Robust Bayesian Inference on Riemannian Submanifold
Rong Tang, Anirban Bhattacharya, Debdeep Pati +1
Manifold-valued parameters routinely arise in modern statistical applications such as in medical imaging, robotics, and computer vision, to name a few. While traditional Bayesian a…