Minimum Cuts of Distance-Regular Digraphs. - Saleh Ashkboos; Gholam Reza Omidi; Fateme Shafiei; Khosro Tajbakhsh (Electron. J. Comb., 2017)

An Efficient Parallel Data Clustering Algorithm Using Isoperimetric Number of Trees. - Ramin Javadi; Saleh Ashkboos (CoRR, 2017)

SparCML: high-performance sparse communication for machine learning. - Cédric Renggli; Saleh Ashkboos; Mehdi Aghagolzadeh; Dan Alistarh; Torsten Hoefler (SC, 2019)

Distributed Mean Estimation with Optimal Error Bounds. - Dan Alistarh; Saleh Ashkboos; Peter Davies 001 (CoRR, 2020)

Degree-based Feature Is All You Need: Science4Cast Report. - Milad Aghajohari; Mohammad Sadegh Akhondzadeh; Saleh Ashkboos; Kamran Chitsaz (IEEE BigData, 2021)

New Bounds For Distributed Mean Estimation and Variance Reduction. - Peter Davies 001; Vijaykrishna Gurunanthan; Niusha Moshrefi; Saleh Ashkboos; Dan Alistarh (ICLR, 2021)

Flare: flexible in-network allreduce. - Daniele De Sensi; Salvatore Di Girolamo; Saleh Ashkboos; Shigang Li 002; Torsten Hoefler (SC, 2021)

Multi-way sparsest cut problem on trees with a control on the number of parts and outliers. - Ramin Javadi; Saleh Ashkboos (Discret. Appl. Math., 2021)

Motif Prediction with Graph Neural Networks. - Maciej Besta; Raphael Grob; Cesare Miglioli; Nicola Bernold; Grzegorz Kwasniewski; Gabriel Gjini; Raghavendra Kanakagiri; Saleh Ashkboos; Lukas Gianinazzi; Nikoli Dryden; Torsten Hoefler (KDD, 2022)

ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts. - Saleh Ashkboos; Langwen Huang; Nikoli Dryden; Tal Ben-Nun; Peter Dueben; Lukas Gianinazzi; Luca Kummer; Torsten Hoefler (NeurIPS, 2022)

ProbGraph: High-Performance and High-Accuracy Graph Mining with Probabilistic Set Representations. - Maciej Besta; Cesare Miglioli; Paolo Sylos Labini; Jakub Tetek; Patrick Iff; Raghavendra Kanakagiri; Saleh Ashkboos; Kacper Janda; Michal Podstawski; Grzegorz Kwasniewski; Niels Gleinig; Flavio Vella; et al. (SC, 2022)

The spatial computer: A model for energy-efficient parallel computation. - Lukas Gianinazzi; Tal Ben-Nun; Saleh Ashkboos; Yves Baumann; Piotr Luczynski; Torsten Hoefler (CoRR, 2022)

ENS-10: A Dataset For Post-Processing Ensemble Weather Forecast. - Saleh Ashkboos; Langwen Huang; Nikoli Dryden; Tal Ben-Nun; Peter Dueben; Lukas Gianinazzi; Luca Kummer; Torsten Hoefler (CoRR, 2022)

GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers. - Elias Frantar; Saleh Ashkboos; Torsten Hoefler; Dan Alistarh (CoRR, 2022)

OPTQ: Accurate Quantization for Generative Pre-trained Transformers. - Elias Frantar; Saleh Ashkboos; Torsten Hoefler; Dan Alistarh (ICLR, 2023)

STen: Productive and Efficient Sparsity in PyTorch. - Andrei Ivanov; Nikoli Dryden; Tal Ben-Nun; Saleh Ashkboos; Torsten Hoefler (CoRR, 2023)

Towards End-to-end 4-Bit Inference on Generative Large Language Models. - Saleh Ashkboos; Ilia Markov; Elias Frantar; Tingxuan Zhong; Xincheng Wang; Jie Ren; Torsten Hoefler; Dan Alistarh (CoRR, 2023)

Arrow Matrix Decompositions. - Lukas Gianinazzi; Alexandros Nikolaos Ziogas; Piotr Luczynski; Saleh Ashkboosh; Langwen Huang; Florian Scheidl; Chio Ge; Armon Carigiet; Maciej Besta; Tal Ben-Nun; Torsten Hoefler (Zenodo, 2023)

QUIK: Towards End-to-end 4-Bit Inference on Generative Large Language Models. - Saleh Ashkboos; Ilia Markov; Elias Frantar; Tingxuan Zhong; Xincheng Wang; Jie Ren; Torsten Hoefler; Dan Alistarh (EMNLP, 2024)

Computational Bottlenecks of Training Small-scale Large Language Models. - Saleh Ashkboos; Seyed Iman Mirzadeh; Keivan Alizadeh-Vahid; Mohammad Hossein Sekhavat; Moin Nabi; Mehrdad Farajtabar; Fartash Faghri (ENLSP, 2024)

SliceGPT: Compress Large Language Models by Deleting Rows and Columns. - Saleh Ashkboos; Maximilian L. Croci; Marcelo Gennari Do Nascimento; Torsten Hoefler; James Hensman (ICLR, 2024)

SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression. - Tim Dettmers; Ruslan Svirschevski; Vage Egiazarian; Denis Kuznedelev; Elias Frantar; Saleh Ashkboos; Alexander Borzunov; Torsten Hoefler; Dan Alistarh (ICLR, 2024)

QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs. - Saleh Ashkboos; Amirkeivan Mohtashami; Maximilian L. Croci; Bo Li; Pashmina Cameron; Martin Jaggi; Dan Alistarh; Torsten Hoefler; James Hensman (NeurIPS, 2024)

Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication. - Lukas Gianinazzi; Alexandros Nikolaos Ziogas; Langwen Huang; Piotr Luczynski; Saleh Ashkboosh; Florian Scheidl; Armon Carigiet; Chio Ge; Nabil Abubaker; Maciej Besta; Tal Ben-Nun; Torsten Hoefler (PPoPP, 2024)

QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs. - Saleh Ashkboos; Amirkeivan Mohtashami; Maximilian L. Croci; Bo Li; Martin Jaggi; Dan Alistarh; Torsten Hoefler; James Hensman (CoRR, 2024)

Computational Bottlenecks of Training Small-scale Large Language Models. - Saleh Ashkboos; Iman Mirzadeh; Keivan Alizadeh; Mohammad Hossein Sekhavat; Moin Nabi; Mehrdad Farajtabar; Fartash Faghri (CoRR, 2024)

EfQAT: An Efficient Framework for Quantization-Aware Training. - Saleh Ashkboos; Bram Verhoef; Torsten Hoefler; Evangelos Eleftheriou; Martino Dazzi (CoRR, 2024)

Energy-Optimal and Low-Depth Algorithmic Primitives for Spatial Dataflow Architectures. - Lukas Gianinazzi; Tal Ben-Nun; Maciej Besta; Saleh Ashkboos; Yves Baumann; Piotr Luczynski; Torsten Hoefler (IPDPS, 2025)

HALO: Hadamard-Assisted Lower-Precision Optimization for LLMs. - Saleh Ashkboos; Mahdi Nikdan; Soroush Tabesh; Roberto L. Castro; Torsten Hoefler; Dan Alistarh (CoRR, 2025)

Quartet: Native FP4 Training Can Be Optimal for Large Language Models. - Roberto L. Castro; Andrei Panferov; Soroush Tabesh; Oliver Sieberling; Jiale Chen 004; Mahdi Nikdan; Saleh Ashkboos; Dan Alistarh (CoRR, 2025)