4 papers · 1 filter
Robust Langevin Sampling under State-Dependent Heavy-Tailed Noise with Applications to LLM Fine-Tuning
M. Berk Sahin, Ege C. Kaya, Arda Fazla +2
Existing convergence guarantees for non-log-concave sampling with stochastic gradient Langevin Monte Carlo (SG-LMC) typically assume globally Lipschitz gradients and uniformly boun…
Variance Reduction for Non-Log-Concave Sampling with Applications to Inverse Problems
M. Berk Sahin, Ahmet Ege Tanriverdi, Behzad Sharif +1
Sampling from high-dimensional, non-log-concave distributions with unnormalized densities is a fundamental challenge in machine learning, particularly when the exact gradient of th…
Zeroth-Order Non-Log-Concave Sampling with Variance Reduction and Applications to Inverse Problems
M. Berk Sahin, Behzad Sharif, Abolfazl Hashemi
Sampling from high-dimensional, non-log-concave distributions with unnormalized densities remains a fundamental challenge in machine learning, particularly in black-box settings wh…
RAMPAGE: RAndomized Mid-Point for debiAsed Gradient Extrapolation
Zhankun Luo, M. Berk Sahin, Antesh Upadhyay +2
A celebrated method for Variational Inequalities (VIs) is Extragradient (EG), which can be viewed as a standard discrete-time integration scheme. With this view in mind, in this pa…