Author

Mehdi Makni

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#artificial intelligence Preprint Sep 2026

ThinQuant: Scalable Rotation Learning for Weight and Activation Quantization of LLMs

ThinQuant is introduced, a data selection procedure which reduces the required number of calibration data points and an exact reduction of the associated optimization on this reduced calibration set using an efficient ADMM algorithm that iteratively employs thin matrix updates at every step, hence the name ThinQuant.

Mehdi Makni, Ryan Lucas, R. Mazumder · 0 citations

#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