Author

Ritik Soni

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Preprint Oct 2026

SULA: Robust Langevin Sampling via Drift-Diffusion Saturation

The unadjusted Langevin algorithm (ULA) and its stochastic-gradient variant, SGLD, are workhorses of modern Bayesian machine learning, but can be fragile when faced with a target distribution possessing a non-Lipschitz drift. In such settings, the expected squared jump distance can grow rapidly, leading to numerical in...

Ritik Soni, Dootika Vats · 0 citations

Preprint Sep 2026

Optimal Scaling of Langevin Proposals with Generalized Acceptance Rules

Langevin-based Markov chain Monte Carlo (MCMC) algorithms use gradient information to improve sampling, particularly in high dimensions. Classical optimal scaling theory for these algorithms has largely focused on the Metropolis-Hastings (MH) acceptance rule. However, there has been a recent surge in acceptance rules b...

Ritik Soni, Dootika Vats · 0 citations