3 citations · 3 across the 6 of their papers we have counts for
3 papers · 1 filter
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…
HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMs
Mehdi Makni, Kayhan Behdin, Zheng Xu +2
The impressive capabilities of large foundation models come at a cost of substantial computing resources to serve them. Compressing these pre-trained models is of practical interes…
Recovering Quantized Data with Missing Information Using Bilinear Factorization and Augmented Lagrangian Method
Ashkan Esmaeili, Kayhan Behdin, Sina Al-E-Mohammad +1
In this paper, we propose a novel approach in order to recover a quantized matrix with missing information. We propose a regularized convex cost function composed of a log-likeliho…