Iofinova, Eugenia B, Elena-Alexandra Peste, Mark Kurtz, and Dan-Adrian Alistarh. “How Well Do Sparse ImageNet Models Transfer?”https://doi.org/10.1109/cvpr52688.2022.01195__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 .
LibreCat: https://research-explorer.ista.ac.at/record/12299
DOI: 10.1109/cvpr52688.2022.01195
WoS: 000870759105034
arXiv: 2111.13445

Alistarh, Dan-Adrian, Giorgi Nadiradze, and Amirmojtaba Sabour. “Dynamic Averaging Load Balancing on Cycles.”https://doi.org/10.1007/s00453-021-00905-9__Algorithmica. Springer Nature, 2022.https://doi.org/10.1007/s00453-021-00905-9 .
LibreCat: https://research-explorer.ista.ac.at/record/8286
DOI: 10.1007/s00453-021-00905-9
WoS: 000734004600001
arXiv: 2003.09297

Hoefler, Torsten, Dan-Adrian Alistarh, Tal Ben-Nun, Nikoli Dryden, and Elena-Alexandra Peste. “Sparsity in Deep Learning: Pruning and Growth for Efficient Inference and Training in Neural Networks.” Journal of Machine Learning Research. Journal of Machine Learning Research, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10180
arXiv: 2102.00554

Alistarh, Dan-Adrian, Rati Gelashvili, and Joel Rybicki. “Brief Announcement: Fast Graphical Population Protocols.”https://doi.org/10.4230/LIPIcs.DISC.2021.43__In 35th International Symposium on Distributed Computing, Vol. 209. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021.https://doi.org/10.4230/LIPIcs.DISC.2021.43 .
LibreCat: https://research-explorer.ista.ac.at/record/10218
DOI: 10.4230/LIPIcs.DISC.2021.43
arXiv: 2102.08808

Alistarh, Dan-Adrian, Rati Gelashvili, and Giorgi Nadiradze. “Lower Bounds for Shared-Memory Leader Election under Bounded Write Contention.”https://doi.org/10.4230/LIPIcs.DISC.2021.4__In 35th International Symposium on Distributed Computing, Vol. 209. Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021.https://doi.org/10.4230/LIPIcs.DISC.2021.4 .
LibreCat: https://research-explorer.ista.ac.at/record/10217
DOI: 10.4230/LIPIcs.DISC.2021.4

Chatterjee, Bapi, Sathya Peri, and Muktikanta Sa. “Brief Announcement: Non-Blocking Dynamic Unbounded Graphs with Worst-Case Amortized Bounds.”https://doi.org/10.4230/LIPIcs.DISC.2021.52__In 35th International Symposium on Distributed Computing, Vol. 209. Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021.https://doi.org/10.4230/LIPIcs.DISC.2021.52 .
LibreCat: https://research-explorer.ista.ac.at/record/10216
DOI: 10.4230/LIPIcs.DISC.2021.52
arXiv: 2003.01697

Korhonen, Janne, Ami Paz, Joel Rybicki, Stefan Schmid, and Jukka Suomela. “Brief Announcement: Sinkless Orientation Is Hard Also in the Supported LOCAL Model.”https://doi.org/10.4230/LIPIcs.DISC.2021.58__In 35th International Symposium on Distributed Computing, Vol. 209. Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021.https://doi.org/10.4230/LIPIcs.DISC.2021.58 .
LibreCat: https://research-explorer.ista.ac.at/record/10219
DOI: 10.4230/LIPIcs.DISC.2021.58
arXiv: 2108.02655

Fedorov, Alexander, Nikita Koval, and Dan-Adrian Alistarh. “A Scalable Concurrent Algorithm for Dynamic Connectivity.”https://doi.org/10.1145/3409964.3461810__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 .
LibreCat: https://research-explorer.ista.ac.at/record/10853
DOI: 10.1145/3409964.3461810
arXiv: 2105.08098

Kungurtsev, Vyacheslav, Malcolm Egan, Bapi Chatterjee, and Dan-Adrian Alistarh. “Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees.” In 35th AAAI Conference on Artificial Intelligence, AAAI 2021, 35:8209–16. AAAI Press, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/11436
arXiv: 1905.11845

Alimisis, Foivos, Peter Davies, Bart Vandereycken, and Dan-Adrian Alistarh. “Distributed Principal Component Analysis with Limited Communication.” In Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, 4:2823–34. Neural Information Processing Systems Foundation, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/11452
arXiv: 2110.14391

Frantar, Elias, Eldar Kurtic, and Dan-Adrian Alistarh. “M-FAC: Efficient Matrix-Free Approximations of Second-Order Information.” In 35th Conference on Neural Information Processing Systems, 34:14873–86. Curran Associates, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/11463
arXiv: 2010.08222

Alistarh, Dan-Adrian, and Janne Korhonen. “Towards Tight Communication Lower Bounds for Distributed Optimisation.” In 35th Conference on Neural Information Processing Systems, 34:7254–66. Curran Associates, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/11464
arXiv: 2010.08222

Davies, Peter, Vijaykrishna Gurunanthan, Niusha  Moshrefi, Saleh Ashkboos, and Dan-Adrian Alistarh. “New Bounds for Distributed Mean Estimation and Variance Reduction.” In 9th International Conference on Learning Representations, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/9543
arXiv: 2002.09268

Alistarh, Dan-Adrian, and Peter Davies. “Collecting Coupons Is Faster with Friends.”https://doi.org/10.1007/978-3-030-79527-6_1__In Structural Information and Communication Complexity, 12810:3–12. Springer Nature, 2021.https://doi.org/10.1007/978-3-030-79527-6_1 .
LibreCat: https://research-explorer.ista.ac.at/record/9620
DOI: 10.1007/978-3-030-79527-6_1

Alistarh, Dan-Adrian, Faith Ellen, and Joel Rybicki. “Wait-Free Approximate Agreement on Graphs.”https://doi.org/10.1007/978-3-030-79527-6_6__In Structural Information and Communication Complexity, 12810:87–105. Springer Nature, 2021.https://doi.org/10.1007/978-3-030-79527-6_6 .
LibreCat: https://research-explorer.ista.ac.at/record/9823
DOI: 10.1007/978-3-030-79527-6_6
arXiv: 2103.08949

Peste, Elena-Alexandra, Eugenia B Iofinova, Adrian Vladu, and Dan-Adrian Alistarh. “AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks.” In 35th Conference on Neural Information Processing Systems, 34:8557–70. Curran Associates, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/11458
arXiv: 2106.12379

Alimisis, Foivos, Peter Davies, and Dan-Adrian Alistarh. “Communication-Efficient Distributed Optimization with Quantized Preconditioners.” In Proceedings of the 38th International Conference on Machine Learning, 139:196–206. ML Research Press, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/13147
arXiv: 2102.07214

Li, Shigang, Tal Ben-Nun Tal Ben-Nun, Giorgi Nadiradze, Salvatore Di Girolamo, Nikoli Dryden, Dan-Adrian Alistarh, and Torsten Hoefler. “Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging.”https://doi.org/10.1109/TPDS.2020.3040606__IEEE Transactions on Parallel and Distributed Systems. IEEE, 2021.https://doi.org/10.1109/TPDS.2020.3040606 .
LibreCat: https://research-explorer.ista.ac.at/record/8723
DOI: 10.1109/TPDS.2020.3040606
WoS: 000621405200019
arXiv: 2005.00124

Chatterjee, Bapi, Ivan Walulya, and Philippas Tsigas. “Concurrent Linearizable Nearest Neighbour Search in LockFree-KD-Tree.”https://doi.org/10.1016/j.tcs.2021.06.041__Theoretical Computer Science. Elsevier, 2021.https://doi.org/10.1016/j.tcs.2021.06.041 .
LibreCat: https://research-explorer.ista.ac.at/record/9827
DOI: 10.1016/j.tcs.2021.06.041
WoS: 000694718900004

Alistarh, Dan-Adrian, Martin Töpfer, and Przemysaw Uznaski. “Comparison Dynamics in Population Protocols.”https://doi.org/10.1145/3465084.3467915__In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, 55–65. Association for Computing Machinery, 2021.https://doi.org/10.1145/3465084.3467915 .
LibreCat: https://research-explorer.ista.ac.at/record/9951
DOI: 10.1145/3465084.3467915
WoS: 000744439800005