Kurtic, Eldar, Torsten Hoefler, and Dan-Adrian Alistarh. “How to Prune Your Language Model: Recovering Accuracy on the ‘Sparsity May Cry’ Benchmark.” In Proceedings of Machine Learning Research, 234:542–53. ML Research Press, 2024.
LibreCat: https://research-explorer.ista.ac.at/record/15011
arXiv: 2312.13547

Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. “Fast and Scalable Channels in Kotlin Coroutines.”https://doi.org/10.1145/3572848.3577481__In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 107–18. Association for Computing Machinery, 2023.https://doi.org/10.1145/3572848.3577481 .
LibreCat: https://research-explorer.ista.ac.at/record/12735
DOI: 10.1145/3572848.3577481
arXiv: 2211.04986

Aksenov, Vitaly, Trevor A Brown, Alexander Fedorov, and Ilya Kokorin. Unexpected Scaling in Path Copying Trees. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery, 2023.https://doi.org/10.1145/3572848.3577512 .
LibreCat: https://research-explorer.ista.ac.at/record/12736
DOI: 10.1145/3572848.3577512

Peste, Elena-Alexandra, Adrian Vladu, Eldar Kurtic, Christoph Lampert, and Dan-Adrian Alistarh. “CrAM: A Compression-Aware Minimizer.” In 11th International Conference on Learning Representations , n.d.
LibreCat: https://research-explorer.ista.ac.at/record/13053
arXiv: 2207.14200

Koval, Nikita, Dmitry Khalanskiy, and Dan-Adrian Alistarh. “CQS: A Formally-Verified Framework for Fair and Abortable Synchronization.”https://doi.org/10.1145/3591230__Proceedings of the ACM on Programming Languages. Association for Computing Machinery , 2023.https://doi.org/10.1145/3591230 .
LibreCat: https://research-explorer.ista.ac.at/record/13179
DOI: 10.1145/3591230

Alistarh, Dan-Adrian, Faith Ellen, and Joel Rybicki. “Wait-Free Approximate Agreement on Graphs.”https://doi.org/10.1016/j.tcs.2023.113733__Theoretical Computer Science. Elsevier, 2023.https://doi.org/10.1016/j.tcs.2023.113733 .
LibreCat: https://research-explorer.ista.ac.at/record/12566
DOI: 10.1016/j.tcs.2023.113733
WoS: 000934262700001

Peste, Elena-Alexandra. “Efficiency and Generalization of Sparse Neural Networks.”https://doi.org/10.15479/at:ista:13074__Institute of Science and Technology Austria, 2023.https://doi.org/10.15479/at:ista:13074 .
LibreCat: https://research-explorer.ista.ac.at/record/13074
DOI: 10.15479/at:ista:13074

Aksenov, Vitalii, Dan-Adrian Alistarh, Alexandra Drozdova, and Amirkeivan Mohtashami. “The Splay-List: A Distribution-Adaptive Concurrent Skip-List.”https://doi.org/10.1007/s00446-022-00441-x__Distributed Computing. Springer Nature, 2023.https://doi.org/10.1007/s00446-022-00441-x .
LibreCat: https://research-explorer.ista.ac.at/record/12330
DOI: 10.1007/s00446-022-00441-x
WoS: 000913424000001
arXiv: 2008.01009

Markov, Ilia, Adrian Vladu, Qi Guo, and Dan-Adrian Alistarh. “Quantized Distributed Training of Large Models with Convergence Guarantees.” In Proceedings of the 40th International Conference on Machine Learning, 202:24020–44. ML Research Press, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14461
arXiv: 2302.02390

Shevchenko, Aleksandr, Kevin Kögler, Hamed Hassani, and Marco Mondelli. “Fundamental Limits of Two-Layer Autoencoders, and Achieving Them with Gradient Methods.” In Proceedings of the 40th International Conference on Machine Learning, 202:31151–209. ML Research Press, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14459
arXiv: 2212.13468

Nikdan, Mahdi, Tommaso Pegolotti, Eugenia B Iofinova, Eldar Kurtic, and Dan-Adrian Alistarh. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” In Proceedings of the 40th International Conference on Machine Learning, 202:26215–27. ML Research Press, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14460
arXiv: 2302.04852

Frantar, Elias, and Dan-Adrian Alistarh. “SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.” In Proceedings of the 40th International Conference on Machine Learning, 202:10323–37. ML Research Press, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14458
arXiv: 2301.00774

Alistarh, Dan-Adrian, James Aspnes, Faith Ellen, Rati Gelashvili, and Leqi Zhu. “Why Extension-Based Proofs Fail.”https://doi.org/10.1137/20M1375851__SIAM Journal on Computing. Society for Industrial and Applied Mathematics, 2023.https://doi.org/10.1137/20M1375851 .
LibreCat: https://research-explorer.ista.ac.at/record/14364
DOI: 10.1137/20M1375851
WoS: 001082972300004
arXiv: 1811.01421

Iofinova, Eugenia B, Elena-Alexandra Peste, and Dan-Adrian Alistarh. “Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures.”https://doi.org/10.1109/cvpr52729.2023.02334__In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 24364–73. IEEE, 2023.https://doi.org/10.1109/cvpr52729.2023.02334 .
LibreCat: https://research-explorer.ista.ac.at/record/14771
DOI: 10.1109/cvpr52729.2023.02334
WoS: 001062531308068
arXiv: 2304.12622

Beznosikov, Aleksandr, Samuel Horvath, Peter Richtarik, and Mher Safaryan. “On Biased Compression for Distributed Learning.” Journal of Machine Learning Research. Journal of Machine Learning Research, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14815
WoS: 001111578500001
arXiv: 2002.12410

Koval, Nikita, Alexander Fedorov, Maria Sokolova, Dmitry Tsitelov, and Dan-Adrian Alistarh. “Lincheck: A Practical Framework for Testing Concurrent Data Structures on JVM.”https://doi.org/10.1007/978-3-031-37706-8_8__In 35th International Conference on Computer Aided Verification , 13964:156–69. Springer Nature, 2023.https://doi.org/10.1007/978-3-031-37706-8_8 .
LibreCat: https://research-explorer.ista.ac.at/record/14260
DOI: 10.1007/978-3-031-37706-8_8

Koval, Nikita, Alexander Fedorov, Maria Sokolova, Dmitry Tsitelov, and Dan-Adrian Alistarh. “Lincheck: A Practical Framework for Testing Concurrent Data Structures on JVM.”https://doi.org/10.5281/ZENODO.7877757__Zenodo, 2023.https://doi.org/10.5281/ZENODO.7877757 .
LibreCat: https://research-explorer.ista.ac.at/record/14995
DOI: 10.5281/ZENODO.7877757

Fedorov, Alexander, Diba Hashemi, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Provably-Efficient and Internally-Deterministic Parallel Union-Find.”https://doi.org/10.1145/3558481.3591082__In Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures, 261–71. Association for Computing Machinery, 2023.https://doi.org/10.1145/3558481.3591082 .
LibreCat: https://research-explorer.ista.ac.at/record/13262
DOI: 10.1145/3558481.3591082
arXiv: 2304.09331

Safaryan, Mher, Alexandra Krumes, and Dan-Adrian Alistarh. “Knowledge Distillation Performs Partial Variance Reduction.” In 36th Conference on Neural Information Processing Systems, Vol. 36, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/15363
arXiv: 2305.17581

Alistarh, Dan-Adrian, Rati Gelashvili, and Joel Rybicki. “Fast Graphical Population Protocols.”https://doi.org/10.4230/LIPIcs.OPODIS.2021.14__In 25th International Conference on Principles of Distributed Systems, edited by Quentin Bramas, Vincent Gramoli, and Alessia Milani, Vol. 217. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022.https://doi.org/10.4230/LIPIcs.OPODIS.2021.14 .
LibreCat: https://research-explorer.ista.ac.at/record/11184
DOI: 10.4230/LIPIcs.OPODIS.2021.14
arXiv: 2102.08808