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SULA: Robust Langevin Sampling via Drift-Diffusion Saturation
Ritik Soni, Dootika Vats
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 d…
stat.CO2026
Optimal Scaling of Langevin Proposals with Generalized Acceptance Rules
Ritik Soni, Dootika Vats
Langevin-based Markov chain Monte Carlo (MCMC) algorithms use gradient information to improve sampling, particularly in high dimensions. Classical optimal scaling theory for these…
stat.CO2026
Hamiltonian Monte Carlo for (Physics) Dummies
Arghya Mukherjee, Dootika Vats
Sampling-based inference has seen a surge of interest in recent years. Hamiltonian Monte Carlo (HMC) has emerged as a powerful algorithm that leverages concepts from Hamiltonian dy…