6 papers
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…
Unified High-Probability Analysis of Stochastic Variance-Reduced Estimation
Zhankun Luo, Antesh Upadhyay, M. Berk Sahin +3
Stochastic estimators are fundamental to large-scale optimization, where population quantities must be inferred from noisy oracle observations. Although influential methods such as…
AGA3DNet: Anatomy-Guided Gaussian Priors with Multi-view xLSTM for 3D Brain MRI Subtype Classification
Peiyu Duan, Xueqi Guo, Sepehr Farhand +5
Accurate 3D brain MRI subtype classification benefits from both localized anatomical cues and long-range contextual reasoning. We present AGA3DNet, a report-grounded framework that…
Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models
M. Berk Sahin, Dilek Yalcinkaya, Abolfazl Hashemi +1
Accelerated magnetic resonance imaging (MRI) enabled by the training of deep learning (DL)-based image recon. models requires large and diverse raw k-space datasets. In most clinic…
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…