Mahdi Nikdan

3 Publications

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[3]
2026 | Published | Conference Poster | IST-REx-ID: 21857 | OA
Nicolicioiu, Armand, et al. “Panza: Investigating the Feasibility of Fully-Local Personalized Text Generation.” Third Conference on Parsimony and Learning (Proceedings Track), 81, OpenReview, 2026.
[Accepted Version] View | Files available | Download Accepted Version (ext.)
 
[2]
2024 | Published | Conference Paper | IST-REx-ID: 18117 | OA
Nikdan, Mahdi, et al. “RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 38187–206.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[1]
2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
Nikdan, Mahdi, et al. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 26215–27.
[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, Armand, et al. “Panza: Investigating the Feasibility of Fully-Local Personalized Text Generation.” Third Conference on Parsimony and Learning (Proceedings Track), 81, OpenReview, 2026.
[Accepted Version] View | Files available | Download Accepted Version (ext.)
 
[2]
2024 | Published | Conference Paper | IST-REx-ID: 18117 | OA
Nikdan, Mahdi, et al. “RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 38187–206.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
[1]
2023 | Published | Conference Paper | IST-REx-ID: 14460 | OA
Nikdan, Mahdi, et al. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 26215–27.
[Preprint] View | Download Preprint (ext.) | arXiv
 
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