2 papers
cs.LG2026
Reconstruction-Optimized Expert Pruning for Mixture-of-Experts Language Models
Jelena Markovic-Voronov, Petros Prastakos, Kayhan Behdin +7
Mixture-of-Experts (MoE) models reduce computation through sparse expert activation, but deployment still requires storing a large number of expert parameters. We study post-traini…
stat.ML2025
Differentially Private High-dimensional Variable Selection via Integer Programming
Petros Prastakos, Kayhan Behdin, Rahul Mazumder
Sparse variable selection improves interpretability and generalization in high-dimensional learning by selecting a small subset of informative features. Recent advances in Mixed In…