Author

Sourabh Bhattacharya

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#machine learning Preprint Aug 2026

Dirichlet Process Mixtures of Trees with Gaussian Process Splits: A Bayesian Nonparametric Framework with Posterior Contraction Rate

We propose a Bayesian nonparametric mixture of regression trees with a Dirichlet process prior over tree-parameter pairs, enabling data-driven selection of ensemble size and unifying CART, BART, random forests, and boosting. A novel splitting rule driven by the posterior predictive of a Gaussian process within each ter...

Subhasish Basak, Anik Roy, Sourabh Bhattacharya · 0 citations

Preprint Aug 2026

Recursive Gaussian Processes and the Bayesian Brain

This synthesis positions RGPs as both a principled computational tool and a candidate model for the brain's predictive machinery, generating testable predictions for laminar-specific dynamics and spectral asymmetries between feedforward and feedback processing.

Moumita Das, Dipanjan Ray, Sourabh Bhattacharya · 0 citations

Preprint Aug 2026

The Bayesian Reflex: A Predictive Coding Engine for Artificial Intelligence

Predictive coding offers a powerful theory of cortical computation, but corresponding scalable algorithmic implementations for artificial intelligence have remained elusive. This paper introduces the Bayesian reflex, a computational framework that directly instantiates predictive coding through three pillars: belief ma...

Sourabh Bhattacharya · 0 citations