Mahdi Nikdan

3 Publications

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[3]
2026 | Published | Conference Poster | IST-REx-ID: 21857 | OA
Nicolicioiu A, Iofinova EB, Jovanovic A, et al. Panza: Investigating the Feasibility of Fully-Local Personalized Text Generation. OpenReview; 2026.
[Accepted Version] View | Files available | Download Accepted Version (ext.)
 
[2]
2024 | Published | Conference Paper | IST-REx-ID: 18117 | OA
Nikdan M, Tabesh S, Crncevic E, Alistarh D-A. RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:38187-38206.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[1]
2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
Nikdan M, Pegolotti T, Iofinova EB, Kurtic E, Alistarh D-A. SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:26215-26227.
[Preprint] View | Download Preprint (ext.) | arXiv
 
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3 Publications

Mark all

[3]
2026 | Published | Conference Poster | IST-REx-ID: 21857 | OA
Nicolicioiu A, Iofinova EB, Jovanovic A, et al. Panza: Investigating the Feasibility of Fully-Local Personalized Text Generation. OpenReview; 2026.
[Accepted Version] View | Files available | Download Accepted Version (ext.)
 
[2]
2024 | Published | Conference Paper | IST-REx-ID: 18117 | OA
Nikdan M, Tabesh S, Crncevic E, Alistarh D-A. RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:38187-38206.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[1]
2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
Nikdan M, Pegolotti T, Iofinova EB, Kurtic E, Alistarh D-A. SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:26215-26227.
[Preprint] View | Download Preprint (ext.) | arXiv
 
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