Author

D. Pati

h-index 22 View on Semantic Scholar

We have 6 of 120 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Preprint Oct 2026

Finite-Rank Logistic Gaussian Processes with Exact Likelihood for Conditional Density Estimation

Conditional density estimation describes how the entire distribution of a response changes with covariates, and in imaging studies also with location. Logistic Gaussian processes (LGP) give a flexible prior for such densities. However, the normalizing constant of an LGP has no closed form, so existing methods approxima...

Jae-Hyun Kim, Indrajit Ghosh, D. Pati et al. · 1 citation

Preprint Oct 2026

Calibration-Free Robust Bayesian Inference for Discrete Unnormalized Models

Bayesian inference for discrete models with intractable normalizing constants is computationally challenging, particularly when the sample space is large or countably infinite. Although some likelihood-based Bayesian methods can circumvent the intractable normalizing constant, their uncertainty quantification may be po...

Jiong-Ran Wang, D. Pati, A. Bhattacharya · 0 citations

#machine learning Preprint Apr 2025

A Generalized Tangent Approximation based Variational Inference Framework for Strongly Super-Gaussian Likelihoods

This article proposes a novel variational framework based on tangent transformation for a broad class of probability models characterized by strongly super-Gaussian likelihoods, thereby inducing conjugacy with Gaussian priors over model parameters in an otherwise intractable setup.

Somjit Roy, Pritam Dey, D. Pati et al. · 0 citations

Preprint Sep 2026

Robust Bayesian Inference for Unnormalized Models with Mixed-Domain Data

Results show that SME-BETEL credible sets are asymptotically calibrated to the sampling variability of the score matching estimator, yielding valid frequentist coverage under model misspecification and prove a Bernstein-von Mises theorem for the SME-BETEL posterior.

Jiong-Ran Wang, D. Pati, A. Bhattacharya · 1 citation

#machine learning Preprint Sep 2026

Posterior Tempering Explains Variance Inflation in Linear and Generalized Linear Thompson Sampling

This work introduces a variant of the Thompson Sampling algorithm that uses a fractional or $\alpha$-posterior instead of the standard posterior, and identifies general regularity conditions on the prior and reward distributions that enable a regret analysis of $\alpha$-TS without assuming any tractable approximation o...

Prateek Jaiswal, D. Pati, A. Bhattacharya et al. · 0 citations

#machine learning Preprint Sep 2025

Probabilistic Symbolic Regression for Equation Discovery via Operator-induced and Regularized Symbolic Forests

This work introduces a probabilistic symbolic regression framework that represents mathematical expressions as ensembles of symbolic trees, and develops posterior concentration guarantees when symbolic expressions approximate the underlying relationship arbitrarily well, with a near-parametric rate when an exact finite...

Somjit Roy, Pritam Dey, B. Mallick et al. · 1 citation · ⚡1