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141 Publications
2023 | Published | Conference Paper | IST-REx-ID: 13053 | 
Krumes, A., Vladu, A., Kurtic, E., Lampert, C., & Alistarh, D.-A. (2023). CrAM: A Compression-Aware Minimizer. In 11th International Conference on Learning Representations . Kigali, Rwanda : OpenReview.
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2023 | Published | Thesis | IST-REx-ID: 13074 |
Krumes, A. (2023). Efficiency and generalization of sparse neural networks. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:13074
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2023 | Published | Journal Article | IST-REx-ID: 13179 |
Koval, N., Khalanskiy, D., & Alistarh, D.-A. (2023). CQS: A formally-verified framework for fair and abortable synchronization. Proceedings of the ACM on Programming Languages. Association for Computing Machinery . https://doi.org/10.1145/3591230
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2023 | Published | Conference Paper | IST-REx-ID: 14771 |
Iofinova, E. B., Krumes, A., & Alistarh, D.-A. (2023). Bias in pruned vision models: In-depth analysis and countermeasures. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24364–24373). Vancouver, BC, Canada: IEEE. https://doi.org/10.1109/cvpr52729.2023.02334
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2023 | Published | Journal Article | IST-REx-ID: 14815 |
Beznosikov, A., Horvath, S., Richtarik, P., & Safaryan, M. (2023). On biased compression for distributed learning. Journal of Machine Learning Research. Journal of Machine Learning Research.
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2023 | Research Data Reference | IST-REx-ID: 14995 |
Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., & Alistarh, D.-A. (2023). Lincheck: A practical framework for testing concurrent data structures on JVM. Zenodo. https://doi.org/10.5281/ZENODO.7877757
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2023 | Published | Conference Paper | IST-REx-ID: 15363 |
Safaryan, M., Krumes, A., & Alistarh, D.-A. (2023). Knowledge distillation performs partial variance reduction. In 36th Conference on Neural Information Processing Systems (Vol. 36). New Orleans, LA, United States.
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2023 | Published | Conference Paper | IST-REx-ID: 13262 |
Fedorov, A., Hashemi, D., Nadiradze, G., & Alistarh, D.-A. (2023). Provably-efficient and internally-deterministic parallel Union-Find. In Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures (pp. 261–271). Orlando, FL, United States: Association for Computing Machinery. https://doi.org/10.1145/3558481.3591082
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2023 | Published | Conference Paper | IST-REx-ID: 14260 |
Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., & Alistarh, D.-A. (2023). Lincheck: A practical framework for testing concurrent data structures on JVM. In 35th International Conference on Computer Aided Verification (Vol. 13964, pp. 156–169). Paris, France: Springer Nature. https://doi.org/10.1007/978-3-031-37706-8_8
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2023 | Published | Journal Article | IST-REx-ID: 14364 |
Alistarh, D.-A., Aspnes, J., Ellen, F., Gelashvili, R., & Zhu, L. (2023). Why extension-based proofs fail. SIAM Journal on Computing. Society for Industrial and Applied Mathematics. https://doi.org/10.1137/20M1375851
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2023 | Published | Conference Paper | IST-REx-ID: 14458 |
Frantar, E., & Alistarh, D.-A. (2023). SparseGPT: Massive language models can be accurately pruned in one-shot. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 10323–10337). Honolulu, Hawaii, HI, United States: ML Research Press.
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2023 | Published | Conference Paper | IST-REx-ID: 14460 |
Nikdan, M., Pegolotti, T., Iofinova, E. B., Kurtic, E., & Alistarh, D.-A. (2023). 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, pp. 26215–26227). Honolulu, Hawaii, HI, United States: ML Research Press.
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2023 | Published | Conference Paper | IST-REx-ID: 14461 |
Markov, I., Vladu, A., Guo, Q., & Alistarh, D.-A. (2023). Quantized distributed training of large models with convergence guarantees. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 24020–24044). Honolulu, Hawaii, HI, United States: ML Research Press.
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2023 | Published | Conference Paper | IST-REx-ID: 14459 |
Shevchenko, A., Kögler, K., Hassani, H., & Mondelli, M. (2023). Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 31151–31209). Honolulu, Hawaii, HI, United States: ML Research Press.
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2022 | Published | Conference Paper | IST-REx-ID: 17059 |
Frantar, E., & Alistarh, D.-A. (2022). SPDY: Accurate pruning with speedup guarantees. In 39th International Conference on Machine Learning (Vol. 162, pp. 6726–6743). Baltimore, MD, United States: ML Research Press.
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2022 | Published | Conference Paper | IST-REx-ID: 17087 |
Frantar, E., Singh, S. P., & Alistarh, D.-A. (2022). Optimal brain compression: A framework for accurate post-training quantization and pruning. In 36th Conference on Neural Information Processing Systems (Vol. 35). New Orleans, LA, United States: ML Research Press.
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2022 | Published | Conference Paper | IST-REx-ID: 17088 |
Kurtic, E., Campos, D., Nguyen, T., Frantar, E., Kurtz, M., Fineran, B., … Alistarh, D.-A. (2022). The optimal BERT surgeon: Scalable and accurate second-order pruning for large language models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 4163–4181). Abu Dhabi, United Arab Emirates: Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.279
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2022 | Published | Journal Article | IST-REx-ID: 8286 |
Alistarh, D.-A., Nadiradze, G., & Sabour, A. (2022). Dynamic averaging load balancing on cycles. Algorithmica. Virtual, Online; Germany: Springer Nature. https://doi.org/10.1007/s00453-021-00905-9
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2022 | Published | Conference Paper | IST-REx-ID: 12182 |
Pacut, M., Parham, M., Rybicki, J., Schmid, S., Suomela, J., & Tereshchenko, A. (2022). Brief announcement: Temporal locality in online algorithms. In 36th International Symposium on Distributed Computing (Vol. 246). Augusta, GA, United States: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.DISC.2022.52
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2022 | Published | Conference Paper | IST-REx-ID: 12299 |
Iofinova, E. B., Krumes, A., Kurtz, M., & Alistarh, D.-A. (2022). How well do sparse ImageNet models transfer? In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12256–12266). New Orleans, LA, United States: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cvpr52688.2022.01195
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