3 papers
math.ST2026
How Many Directions Must a Truncated Diffusion Sampler Retain? Matching Bounds Under Power-Law Spectra
Radmehr Karimian, Ali Mohades, Johannes Lederer
Diffusion samplers can reduce computation by generating selected spectral coordinates and filling the remaining directions with noise. How many directions must they retain? We stud…
cs.LG2026
-FUM: Federated Unlearning via min--max and -divergence
Radmehr Karimian, Amirhossein Bagheri, Meghdad Kurmanji +2
Federated learning (FL) enables collaborative training while keeping raw data on clients. Deletion requests and the discovery of poisoned data create a need to remove selected cont…
cs.LG2024
Robust Semi-supervised Learning via -Divergence and -Rényi Divergence
Gholamali Aminian, Amirhossien Bagheri, Mahyar JafariNodeh +2
This paper investigates a range of empirical risk functions and regularization methods suitable for self-training methods in semi-supervised learning. These approaches draw inspira…