Author

Nabarun Deb

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Open access Oct 2026

No-regret generative modeling via parabolic Monge–Ampère PDE

We introduce a novel generative modeling framework based on a discretized parabolic Monge–Ampère PDE, which emerges as a continuous limit of the Sinkhorn algorithm commonly used in optimal transport. Our method performs iterative refinement in the space of Brenier maps using a mirror gradient descent step. We establish...

Nabarun Deb, Tengyuan Liang · Annals of Statistics · 0 citations

#machine learning Preprint Oct 2026

Sample complexity bounds for categorical Markov random fields via Discrete Diffusions

Many applications in statistics, economics, and physics require sampling from high-dimensional categorical distributions with local dependence structures. Examples include finite memory language models, Ising and Potts systems in statistical physics and protein folding, etc. In modern machine learning, discrete diffusi...

Shivam Kumar, Nabarun Deb · 0 citations

Preprint Sep 2026

LDP for Tensor Forms

The large deviation principle (LDP) is studied for a tensor-weighted functional of i.i.d. random variables, when the sequence of tensors converges under a variant of the"bad"cut norm, and sufficient conditions for uniqueness of the optimizer and existence of constant optimizers are given.

Reihaneh Malekian, Sohom Bhattacharya, Nabarun Deb et al. · 0 citations