Author

Rahul Mazumder

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#machine learning May 2026

ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models

This work proposes ADMM-Q, a novel weight quantization algorithm that considers the layer-wise quantization problem, based on a combinatorial variant of the Alternating Direction Method of Multipliers (ADMM).

Ryan Lucas, Mehdi Makni, Xiang Meng et al. · arXiv.org · 1 citation

#machine learning Apr 2026

MOONSHOT : A Framework for Multi-Objective Pruning of Vision and Large Language Models

This work proposes MOONSHOT, a general and flexible framework that extends any single-objective pruning method into a multi-objective formulation by jointly optimizing both the layer-wise reconstruction error and second-order Taylor approximation of the training loss.

Gabriel Afriat, Xiangui Meng, Shibal Ibrahim et al. · Trans. Mach. Learn. Res. · 0 citations