Ramezani-Kebrya, Ali, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan-Adrian Alistarh, and Daniel M. Roy. “NUQSGD: Provably Communication-Efficient Data-Parallel SGD via Nonuniform Quantization.” Journal of Machine Learning Research. Journal of Machine Learning Research, 2021.

Censor-Hillel, Keren, Michal Dory, Janne Korhonen, and Dean Leitersdorf. “Fast Approximate Shortest Paths in the Congested Clique.”https://doi.org/10.1007/s00446-020-00380-5__Distributed Computing. Springer Nature, 2021.https://doi.org/10.1007/s00446-020-00380-5 .

Czumaj, Artur, Peter Davies, and Merav Parter. “Simple, Deterministic, Constant-Round Coloring in Congested Clique and MPC.”https://doi.org/10.1137/20m1366502__SIAM Journal on Computing. Society for Industrial &; Applied Mathematics, 2021.https://doi.org/10.1137/20m1366502 .

Czumaj, Artur, and Peter Davies. “Exploiting Spontaneous Transmissions for Broadcasting and Leader Election in Radio Networks.”https://doi.org/10.1145/3446383__Journal of the ACM. Association for Computing Machinery, 2021.https://doi.org/10.1145/3446383 .

Alimisis, Foivos, Antonio Orvieto, Gary Becigneul, and Aurelien Lucchi. “Momentum Improves Optimization on Riemannian Manifolds.” In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 130:1351–59. ML Research Press, 2021.

Arbel-Raviv, Maya, Trevor A Brown, and Adam Morrison. “Getting to the Root of Concurrent Binary Search Tree Performance.” In Proceedings of the 2018 USENIX Annual Technical Conference, 295–306. USENIX Association, 2020.

Alistarh, Dan-Adrian, Alexander Fedorov, and Nikita Koval. “In Search of the Fastest Concurrent Union-Find Algorithm.”https://doi.org/10.4230/LIPIcs.OPODIS.2019.15__In 23rd International Conference on Principles of Distributed Systems, 153:15:1-15:16. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.https://doi.org/10.4230/LIPIcs.OPODIS.2019.15 .

Czumaj, Artur, Peter Davies, and Merav Parter. “Simple, Deterministic, Constant-Round Coloring in the Congested Clique.”https://doi.org/10.1145/3382734.3405751__In Proceedings of the 2020 ACM Symposium on Principles of Distributed Computing, 309–18. Association for Computing Machinery, 2020.https://doi.org/10.1145/3382734.3405751 .

Aksenov, Vitaly, Dan-Adrian Alistarh, Alexandra Drozdova, and Amirkeivan Mohtashami. “The Splay-List: A Distribution-Adaptive Concurrent Skip-List.”https://doi.org/10.4230/LIPIcs.DISC.2020.3__In 34th International Symposium on Distributed Computing, 179:3:1-3:18. LIPIcs. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.https://doi.org/10.4230/LIPIcs.DISC.2020.3 .

Singh, Sidak Pal, and Dan-Adrian Alistarh. “WoodFisher: Efficient Second-Order Approximation for Neural Network Compression.” In Advances in Neural Information Processing Systems, 33:18098–109. Curran Associates, 2020.

Aksenov, Vitaly, Dan-Adrian Alistarh, and Janne Korhonen. “Scalable Belief Propagation via Relaxed Scheduling.” In Advances in Neural Information Processing Systems, 33:22361–72. Curran Associates, 2020.

Kurtz, Mark, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William Leiserson, et al. “Inducing and Exploiting Activation Sparsity for Fast Neural Network Inference.” In 37th International Conference on Machine Learning, ICML 2020, 119:5533–43, 2020.

Gurel, Nezihe Merve, Kaan Kara, Alen Stojanov, Tyler Smith, Thomas Lemmin, Dan-Adrian Alistarh, Markus Puschel, and Ce Zhang. “Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications.”https://doi.org/10.1109/TSP.2020.3010355__IEEE Transactions on Signal Processing. IEEE, 2020.https://doi.org/10.1109/TSP.2020.3010355 .

Li, Shigang, Tal Ben-Nun Tal Ben-Nun, Salvatore Di Girolamo, Dan-Adrian Alistarh, and Torsten Hoefler. “Taming Unbalanced Training Workloads in Deep Learning with Partial Collective Operations.”https://doi.org/10.1145/3332466.3374528__In Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 45–61. Association for Computing Machinery, 2020.https://doi.org/10.1145/3332466.3374528 .

Rybicki, Joel, Nerea Abrego, and Otso Ovaskainen. “Habitat Fragmentation and Species Diversity in Competitive Communities.”https://doi.org/10.1111/ele.13450__Ecology Letters. Wiley, 2020.https://doi.org/10.1111/ele.13450 .

Konstantinov, Nikola H, Elias Frantar, Dan-Adrian Alistarh, and Christoph Lampert. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” In Proceedings of the 37th International Conference on Machine Learning, 119:5416–25. ML Research Press, 2020.

Bhatia, Sumit, Bapi Chatterjee, Deepak Nathani, and Manohar Kaul. “A Persistent Homology Perspective to the Link Prediction Problem.”https://doi.org/10.1007/978-3-030-36687-2_3__In Complex Networks and Their Applications VIII, 881:27–39. Springer Nature, 2020.https://doi.org/10.1007/978-3-030-36687-2_3 .

Czumaj, Artur, Peter Davies, and Merav Parter. “Graph Sparsification for Derandomizing Massively Parallel Computation with Low Space.”https://doi.org/10.1145/3350755.3400282__In Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2020), 175–85. Association for Computing Machinery, 2020.https://doi.org/10.1145/3350755.3400282 .

Brown, Trevor A, Aleksandar Prokopec, and Dan-Adrian Alistarh. “Non-Blocking Interpolation Search Trees with Doubly-Logarithmic Running Time.”https://doi.org/10.1145/3332466.3374542__In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 276–91. Association for Computing Machinery, 2020.https://doi.org/10.1145/3332466.3374542 .

Alistarh, Dan-Adrian, Trevor A Brown, and Nandini Singhal. “Memory Tagging: Minimalist Synchronization for Scalable Concurrent Data Structures.”https://doi.org/10.1145/3350755.3400213__In Annual ACM Symposium on Parallelism in Algorithms and Architectures, 37–49. Association for Computing Machinery, 2020.https://doi.org/10.1145/3350755.3400213 .