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 .

Nikabadi, Amir, and Janne Korhonen. “Beyond Distributed Subgraph Detection: Induced Subgraphs, Multicolored Problems and Graph Parameters.”https://doi.org/10.4230/LIPIcs.OPODIS.2021.15__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 .

Shevchenko, Aleksandr, 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.

Pacut, Maciej, Mahmoud Parham, Joel Rybicki, Stefan Schmid, Jukka Suomela, and Aleksandr Tereshchenko. “Brief Announcement: Temporal Locality in Online Algorithms.”https://doi.org/10.4230/LIPIcs.DISC.2022.52__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 .

Markov, Ilia, Hamidreza Ramezanikebrya, and Dan-Adrian Alistarh. “CGX: Adaptive System Support for Communication-Efficient Deep Learning.”https://doi.org/10.1145/3528535.3565248__In Proceedings of the 23rd ACM/IFIP International Middleware Conference, 241–54. Association for Computing Machinery, 2022.https://doi.org/10.1145/3528535.3565248 .

Alistarh, Dan-Adrian, Joel Rybicki, and Sasha Voitovych. “Near-Optimal Leader Election in Population Protocols on Graphs.”https://doi.org/10.1145/3519270.3538435__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 .

Brown, Trevor A, William Sigouin, and Dan-Adrian Alistarh. “PathCAS: An Efficient Middle Ground for Concurrent Search Data Structures.”https://doi.org/10.1145/3503221.3508410__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 .

Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.”https://doi.org/10.1145/3503221.3508432__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 .

Postnikova, Anastasiia, Nikita Koval, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Multi-Queues Can Be State-of-the-Art Priority Schedulers.”https://doi.org/10.5281/ZENODO.5733408__Zenodo, 2022.https://doi.org/10.5281/ZENODO.5733408 .

Balliu, Alkida, Juho Hirvonen, Darya Melnyk, Dennis Olivetti, Joel Rybicki, and Jukka Suomela. “Local Mending.”https://doi.org/10.1007/978-3-031-09993-9_1__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 .

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 .

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 .

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.

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 .

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 .

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 .

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 .

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 .

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.

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.