activity
20122024
most citedFrequentist coverage and sup-norm convergence rate in Gaussian process regression

36 citations · 65 across the 13 of their papers we have counts for

collaborators
Showing math.STShow all

11 papers · 1 filter

math.ST2020

Statistical Guarantees for Transformation Based Models with Applications to Implicit Variational Inference

Sean Plummer, Shuang Zhou, Anirban Bhattacharya +2

Transformation-based methods have been an attractive approach in non-parametric inference for problems such as unconditional and conditional density estimation due to their unique…

math.ST2020

Statistical optimality and stability of tangent transform algorithms in logit models

Indrajit Ghosh, Anirban Bhattacharya, Debdeep Pati

A systematic approach to finding variational approximation in an otherwise intractable non-conjugate model is to exploit the general principle of convex duality by minorizing the m…

math.ST20201 cited

Evidence bounds in singular models: probabilistic and variational perspectives

Anirban Bhattacharya, Debdeep Pati, Sean Plummer

The marginal likelihood or evidence in Bayesian statistics contains an intrinsic penalty for larger model sizes and is a fundamental quantity in Bayesian model comparison. Over the…

math.ST20201 cited

Nonasymptotic Laplace approximation under model misspecification

Anirban Bhattacharya, Debdeep Pati

We present non-asymptotic two-sided bounds to the log-marginal likelihood in Bayesian inference. The classical Laplace approximation is recovered as the leading term. Our derivatio…

math.ST2020

Mass-shifting phenomenon of truncated multivariate normal priors

Shuang Zhou, Pallavi Ray, Debdeep Pati +1

We show that lower-dimensional marginal densities of dependent zero-mean normal distributions truncated to the positive orthant exhibit a mass-shifting phenomenon. Despite the trun…

math.ST20179 cited

On Statistical Optimality of Variational Bayes

Debdeep Pati, Anirban Bhattacharya, Yun Yang

The article addresses a long-standing open problem on the justification of using variational Bayes methods for parameter estimation. We provide general conditions for obtaining opt…