Please note that LibreCat no longer supports Internet Explorer versions 8 or 9 (or earlier).
We recommend upgrading to the latest Internet Explorer, Google Chrome, or Firefox.
151 Publications
2023 | Published | Conference Paper | IST-REx-ID: 14771 | 
Iofinova, Eugenia B, Alexandra Krumes, and Dan-Adrian Alistarh. “Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures.” In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 24364–73. IEEE, 2023. https://doi.org/10.1109/cvpr52729.2023.02334.
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14461 |
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.
[Preprint]
View
| Files available
| Download Preprint (ext.)
| arXiv
2023 | Published | Journal Article | IST-REx-ID: 14364 |
Alistarh, Dan-Adrian, James Aspnes, Faith Ellen, Rati Gelashvili, and Leqi Zhu. “Why Extension-Based Proofs Fail.” SIAM Journal on Computing. Society for Industrial and Applied Mathematics, 2023. https://doi.org/10.1137/20M1375851.
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2023 | Published | Conference Paper | IST-REx-ID: 14459 |
Shevchenko, Alexander, 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.
[Preprint]
View
| Files available
| Download Preprint (ext.)
| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 11181 |
Brown, Trevor A, William Sigouin, and Dan-Adrian Alistarh. “PathCAS: An Efficient Middle Ground for Concurrent Search Data Structures.” In Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 385–99. Association for Computing Machinery, 2022. https://doi.org/10.1145/3503221.3508410.
[Published Version]
View
| Files available
| DOI
| WoS
2022 | Published | Conference Paper | IST-REx-ID: 17088 |
Kurtic, Eldar, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, and Dan-Adrian Alistarh. “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, 4163–81. Association for Computational Linguistics, 2022. https://doi.org/10.18653/v1/2022.emnlp-main.279.
[Published Version]
View
| Files available
| DOI
| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 12182 |
Pacut, Maciej, Mahmoud Parham, Joel Rybicki, Stefan Schmid, Jukka Suomela, and Aleksandr Tereshchenko. “Brief Announcement: Temporal Locality in Online Algorithms.” In 36th International Symposium on Distributed Computing, Vol. 246. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022. https://doi.org/10.4230/LIPIcs.DISC.2022.52.
[Published Version]
View
| Files available
| DOI
2022 | Published | Conference Paper | IST-REx-ID: 11707 |
Balliu, Alkida, Juho Hirvonen, Darya Melnyk, Dennis Olivetti, Joel Rybicki, and Jukka Suomela. “Local Mending.” In International Colloquium on Structural Information and Communication Complexity, edited by Merav Parter, 13298:1–20. LNCS. Springer Nature, 2022. https://doi.org/10.1007/978-3-031-09993-9_1.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 11183 |
Nikabadi, Amir, and Janne Korhonen. “Beyond Distributed Subgraph Detection: Induced Subgraphs, Multicolored Problems and Graph Parameters.” 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.2021.15.
[Published Version]
View
| Files available
| DOI
2022 | Published | Conference Paper | IST-REx-ID: 11180 |
Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.” In Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 353–67. Association for Computing Machinery, 2022. https://doi.org/10.1145/3503221.3508432.
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2022 | Research Data Reference | IST-REx-ID: 13076 |
Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.” Zenodo, 2022. https://doi.org/10.5281/ZENODO.5733408.
[Published Version]
View
| Files available
| DOI
| Download Published Version (ext.)
2022 | Published | Conference Paper | IST-REx-ID: 11844 |
Alistarh, Dan-Adrian, Joel Rybicki, and Sasha Voitovych. “Near-Optimal Leader Election in Population Protocols on Graphs.” In Proceedings of the Annual ACM Symposium on Principles of Distributed Computing, 246–56. Association for Computing Machinery, 2022. https://doi.org/10.1145/3519270.3538435.
[Published Version]
View
| Files available
| DOI
| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 17087 |
Frantar, Elias, Sidak Pal Singh, and Dan-Adrian Alistarh. “Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning.” In 36th Conference on Neural Information Processing Systems, Vol. 35. ML Research Press, 2022.
[Submitted Version]
View
| Files available
| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 17059 |
Frantar, Elias, and Dan-Adrian Alistarh. “SPDY: Accurate Pruning with Speedup Guarantees.” In 39th International Conference on Machine Learning, 162:6726–43. ML Research Press, 2022.
[Published Version]
View
| Files available
| WoS
2022 | Published | Conference Paper | IST-REx-ID: 11184 |
Alistarh, Dan-Adrian, Rati Gelashvili, and Joel Rybicki. “Fast Graphical Population Protocols.” 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.
[Published Version]
View
| Files available
| DOI
| arXiv
2022 | Published | Journal Article | IST-REx-ID: 8286 |
Alistarh, Dan-Adrian, Dynamic averaging load balancing on cycles. Algorithmica 84 (4). 2022
[Published Version]
View
| Files available
| DOI
| WoS
| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 12299 |
Iofinova, Eugenia B, Alexandra Krumes, Mark Kurtz, and Dan-Adrian Alistarh. “How Well Do Sparse ImageNet Models Transfer?” In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 12256–66. Institute of Electrical and Electronics Engineers, 2022. https://doi.org/10.1109/cvpr52688.2022.01195.
[Preprint]
View
| Files available
| DOI
| Download Preprint (ext.)
| WoS
| arXiv
2022 | Published | Conference Paper | IST-REx-ID: 12780 |
Markov, Ilia, Hamidreza Ramezanikebrya, and Dan-Adrian Alistarh. “CGX: Adaptive System Support for Communication-Efficient Deep Learning.” In Proceedings of the 23rd ACM/IFIP International Middleware Conference, 241–54. Association for Computing Machinery, 2022. https://doi.org/10.1145/3528535.3565248.
[Published Version]
View
| Files available
| DOI
| arXiv
2022 | Published | Journal Article | IST-REx-ID: 11420 |
Shevchenko, Alexander, Vyacheslav Kungurtsev, and Marco Mondelli. “Mean-Field Analysis of Piecewise Linear Solutions for Wide ReLU Networks.” Journal of Machine Learning Research. Journal of Machine Learning Research, 2022.
[Published Version]
View
| Files available
| arXiv
2021 | Published | Conference Paper | IST-REx-ID: 10853 |
Fedorov, Alexander, Nikita Koval, and Dan-Adrian Alistarh. “A Scalable Concurrent Algorithm for Dynamic Connectivity.” In Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures, 208–20. Association for Computing Machinery, 2021. https://doi.org/10.1145/3409964.3461810.
[Preprint]
View
| DOI
| Download Preprint (ext.)
| arXiv