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.

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.

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.

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 .

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 .

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.

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.

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 .

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 .

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 .

Czumaj, Artur, Peter Davies, and Merav Parter. “Improved Deterministic (+1) Coloring in Low-Space MPC.”https://doi.org/10.1145/3465084.3467937__In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, 469–479. Association for Computing Machinery, 2021.https://doi.org/10.1145/3465084.3467937 .

Czumaj, Artur, Peter Davies, and Merav Parter. “Component Stability in Low-Space Massively Parallel Computation.”https://doi.org/10.1145/3465084.3467903__In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, 481–491. Association for Computing Machinery, 2021.https://doi.org/10.1145/3465084.3467903 .

Nadiradze, Giorgi, Ilia Markov, Bapi Chatterjee, Vyacheslav  Kungurtsev, and Dan-Adrian Alistarh. “Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent.” In Proceedings of the AAAI Conference on Artificial Intelligence, 35:9037–45, 2021.

Klein, Karen, Guillermo Pascual Perez, Michael Walter, Chethan Kamath Hosdurg, Margarita Capretto, Miguel Cueto Noval, Ilia Markov, Michelle X Yeo, Joel F Alwen, and Krzysztof Z Pietrzak. “Keep the Dirt: Tainted TreeKEM, Adaptively and Actively Secure Continuous Group Key Agreement.”https://doi.org/10.1109/sp40001.2021.00035__In 2021 IEEE Symposium on Security and Privacy , 268–84. IEEE, 2021.https://doi.org/10.1109/sp40001.2021.00035 .

Foerster, Klaus-Tycho, Janne Korhonen, Ami Paz, Joel Rybicki, and Stefan Schmid. “Input-Dynamic Distributed Algorithms for Communication Networks.”https://doi.org/10.1145/3410220.3453923__In Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, 71–72. Association for Computing Machinery, 2021.https://doi.org/10.1145/3410220.3453923 .

Foerster, Klaus-Tycho, Janne Korhonen, Ami Paz, Joel Rybicki, and Stefan Schmid. “Input-Dynamic Distributed Algorithms for Communication Networks.”https://doi.org/10.1145/3447384__Proceedings of the ACM on Measurement and Analysis of Computing Systems. Association for Computing Machinery, 2021.https://doi.org/10.1145/3447384 .

Nadiradze, Giorgi. “On Achieving Scalability through Relaxation.”https://doi.org/10.15479/at:ista:10429__Institute of Science and Technology Austria, 2021.https://doi.org/10.15479/at:ista:10429 .

Nadiradze, Giorgi, Amirmojtaba Sabour, Peter Davies, Shigang Li, and Dan-Adrian Alistarh. “Asynchronous Decentralized SGD with Quantized and Local Updates.” In 35th Conference on Neural Information Processing Systems. Neural Information Processing Systems Foundation, 2021.

Czumaj, Artur, Peter Davies, and Merav Parter. “Graph Sparsification for Derandomizing Massively Parallel Computation with Low Space.”https://doi.org/10.1145/3451992__ACM Transactions on Algorithms. Association for Computing Machinery, 2021.https://doi.org/10.1145/3451992 .

Brandt, Sebastian, Barbara Keller, Joel Rybicki, Jukka Suomela, and Jara Uitto. “Efficient Load-Balancing through Distributed Token Dropping.”https://doi.org/10.1145/3409964.3461785__In Annual ACM Symposium on Parallelism in Algorithms and Architectures, 129–39, 2021.https://doi.org/10.1145/3409964.3461785 .