N. Koval, D. Khalanskiy, and D.-A. Alistarh, “CQS: A formally-verified framework for fair and abortable synchronization,” Proceedings of the ACM on Programming Languages, vol. 7. Association for Computing Machinery , 2023.
LibreCat: https://research-explorer.ista.ac.at/record/13179
DOI: 10.1145/3591230

D.-A. Alistarh, F. Ellen, and J. Rybicki, “Wait-free approximate agreement on graphs,” Theoretical Computer Science, vol. 948, no. 2. Elsevier, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/12566
DOI: 10.1016/j.tcs.2023.113733
WoS: 000934262700001

E.-A. Peste, “Efficiency and generalization of sparse neural networks,” Institute of Science and Technology Austria, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/13074
DOI: 10.15479/at:ista:13074

V. Aksenov, D.-A. Alistarh, A. Drozdova, and A. Mohtashami, “The splay-list: A distribution-adaptive concurrent skip-list,” Distributed Computing, vol. 36. Springer Nature, pp. 395–418, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/12330
DOI: 10.1007/s00446-022-00441-x
WoS: 000913424000001
arXiv: 2008.01009

I. Markov, A. Vladu, Q. Guo, and D.-A. Alistarh, “Quantized distributed training of large models with convergence guarantees,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 24020–24044.
LibreCat: https://research-explorer.ista.ac.at/record/14461
arXiv: 2302.02390

A. Shevchenko, K. Kögler, H. Hassani, and M. Mondelli, “Fundamental limits of two-layer autoencoders, and achieving them with gradient methods,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 31151–31209.
LibreCat: https://research-explorer.ista.ac.at/record/14459
arXiv: 2212.13468

M. Nikdan, T. Pegolotti, E. B. Iofinova, E. Kurtic, and D.-A. Alistarh, “SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 26215–26227.
LibreCat: https://research-explorer.ista.ac.at/record/14460
arXiv: 2302.04852

E. Frantar and D.-A. Alistarh, “SparseGPT: Massive language models can be accurately pruned in one-shot,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 10323–10337.
LibreCat: https://research-explorer.ista.ac.at/record/14458
arXiv: 2301.00774

D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, and L. Zhu, “Why extension-based proofs fail,” SIAM Journal on Computing, vol. 52, no. 4. Society for Industrial and Applied Mathematics, pp. 913–944, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14364
DOI: 10.1137/20M1375851
WoS: 001082972300004
arXiv: 1811.01421

E. B. Iofinova, E.-A. Peste, and D.-A. Alistarh, “Bias in pruned vision models: In-depth analysis and countermeasures,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 2023, pp. 24364–24373.
LibreCat: https://research-explorer.ista.ac.at/record/14771
DOI: 10.1109/cvpr52729.2023.02334
WoS: 001062531308068
arXiv: 2304.12622

A. Beznosikov, S. Horvath, P. Richtarik, and M. Safaryan, “On biased compression for distributed learning,” Journal of Machine Learning Research, vol. 24. Journal of Machine Learning Research, pp. 1–50, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14815
WoS: 001111578500001
arXiv: 2002.12410

N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, and D.-A. Alistarh, “Lincheck: A practical framework for testing concurrent data structures on JVM,” in 35th International Conference on Computer Aided Verification , Paris, France, 2023, vol. 13964, pp. 156–169.
LibreCat: https://research-explorer.ista.ac.at/record/14260
DOI: 10.1007/978-3-031-37706-8_8

N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, and D.-A. Alistarh, “Lincheck: A practical framework for testing concurrent data structures on JVM.” Zenodo, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14995
DOI: 10.5281/ZENODO.7877757

A. Fedorov, D. Hashemi, G. Nadiradze, and D.-A. Alistarh, “Provably-efficient and internally-deterministic parallel Union-Find,” in Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures, Orlando, FL, United States, 2023, pp. 261–271.
LibreCat: https://research-explorer.ista.ac.at/record/13262
DOI: 10.1145/3558481.3591082
arXiv: 2304.09331

M. Safaryan, A. Krumes, and D.-A. Alistarh, “Knowledge distillation performs partial variance reduction,” in 36th Conference on Neural Information Processing Systems, New Orleans, LA, United States, 2023, vol. 36.
LibreCat: https://research-explorer.ista.ac.at/record/15363
arXiv: 2305.17581

D.-A. Alistarh, R. Gelashvili, and J. Rybicki, “Fast graphical population protocols,” in 25th International Conference on Principles of Distributed Systems, Strasbourg, France, 2022, vol. 217.
LibreCat: https://research-explorer.ista.ac.at/record/11184
DOI: 10.4230/LIPIcs.OPODIS.2021.14
arXiv: 2102.08808

A. Nikabadi and J. Korhonen, “Beyond distributed subgraph detection: Induced subgraphs, multicolored problems and graph parameters,” in 25th International Conference on Principles of Distributed Systems, Strasbourg, France, 2022, vol. 217.
LibreCat: https://research-explorer.ista.ac.at/record/11183
DOI: 10.4230/LIPIcs.OPODIS.2021.15

A. Shevchenko, V. Kungurtsev, and M. Mondelli, “Mean-field analysis of piecewise linear solutions for wide ReLU networks,” Journal of Machine Learning Research, vol. 23, no. 130. Journal of Machine Learning Research, pp. 1–55, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/11420
arXiv: 2111.02278

M. Pacut, M. Parham, J. Rybicki, S. Schmid, J. Suomela, and A. Tereshchenko, “Brief announcement: Temporal locality in online algorithms,” in 36th International Symposium on Distributed Computing, Augusta, GA, United States, 2022, vol. 246.
LibreCat: https://research-explorer.ista.ac.at/record/12182
DOI: 10.4230/LIPIcs.DISC.2022.52

I. Markov, H. Ramezanikebrya, and D.-A. Alistarh, “CGX: Adaptive system support for communication-efficient deep learning,” in Proceedings of the 23rd ACM/IFIP International Middleware Conference, Quebec, QC, Canada, 2022, pp. 241–254.
LibreCat: https://research-explorer.ista.ac.at/record/12780
DOI: 10.1145/3528535.3565248
arXiv: 2111.08617