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Nadiradze G, Sabour A, Davies P, Li S, Alistarh D-A. Asynchronous decentralized SGD with quantized and local updates. In: 35th Conference on Neural Information Processing Systems. Neural Information Processing Systems Foundation; 2021.

Czumaj A, Davies P, Parter M. Graph sparsification for derandomizing massively parallel computation with low space. ACM Transactions on Algorithms. 2021;17(2). doi:10.1145/3451992

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Alistarh D-A, Nadiradze G, Sabour A. Dynamic averaging load balancing on cycles. Algorithmica. 2021. doi:10.1007/s00453-021-00905-9

Ramezani-Kebrya A, Faghri F, Markov I, Aksenov V, Alistarh D-A, Roy DM. NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research. 2021;22(114):143.

Censor-Hillel K, Dory M, Korhonen J, Leitersdorf D. Fast approximate shortest paths in the congested clique. Distributed Computing. 2021;34:463-487. doi:10.1007/s00446-020-00380-5

Czumaj A, Davies P, Parter M. Simple, deterministic, constant-round coloring in congested clique and MPC. SIAM Journal on Computing. 2021;50(5):1603-1626. doi:10.1137/20m1366502

Czumaj A, Davies P. Exploiting spontaneous transmissions for broadcasting and leader election in radio networks. Journal of the ACM. 2021;68(2). doi:10.1145/3446383

Alimisis F, Orvieto A, Becigneul G, Lucchi A. Momentum improves optimization on Riemannian manifolds. In: Proceedings of the 24th International Conference on Artificial Intelligence and Statistics. Vol 130. ML Research Press; 2021:1351-1359.

Arbel-Raviv M, Brown TA, Morrison A. Getting to the root of concurrent binary search tree performance. In: Proceedings of the 2018 USENIX Annual Technical Conference. USENIX Association; 2020:295-306.

Alistarh D-A, Fedorov A, Koval N. In search of the fastest concurrent union-find algorithm. In: 23rd International Conference on Principles of Distributed Systems. Vol 153. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2020:15:1-15:16. doi:10.4230/LIPIcs.OPODIS.2019.15

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Aksenov V, Alistarh D-A, Drozdova A, Mohtashami A. The splay-list: A distribution-adaptive concurrent skip-list. In: 34th International Symposium on Distributed Computing. Vol 179. LIPIcs. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2020:3:1-3:18. doi:10.4230/LIPIcs.DISC.2020.3

Singh SP, Alistarh D-A. WoodFisher: Efficient second-order approximation for neural network compression. In: Advances in Neural Information Processing Systems. Vol 33. Curran Associates; 2020:18098-18109.

Aksenov V, Alistarh D-A, Korhonen J. Scalable belief propagation via relaxed scheduling. In: Advances in Neural Information Processing Systems. Vol 33. Curran Associates; 2020:22361-22372.

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Gurel NM, Kara K, Stojanov A, et al. Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. IEEE Transactions on Signal Processing. 2020;68:4268-4282. doi:10.1109/TSP.2020.3010355

Li S, Tal Ben-Nun TB-N, Girolamo SD, Alistarh D-A, Hoefler T. Taming unbalanced training workloads in deep learning with partial collective operations. In: Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery; 2020:45-61. doi:10.1145/3332466.3374528

Rybicki J, Abrego N, Ovaskainen O. Habitat fragmentation and species diversity in competitive communities. Ecology Letters. 2020;23(3):506-517. doi:10.1111/ele.13450

Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. On the sample complexity of adversarial multi-source PAC learning. In: Proceedings of the 37th International Conference on Machine Learning. Vol 119. ML Research Press; 2020:5416-5425.

Bhatia S, Chatterjee B, Nathani D, Kaul M. A persistent homology perspective to the link prediction problem. In: Complex Networks and Their Applications VIII. Vol 881. Springer Nature; 2020:27-39. doi:10.1007/978-3-030-36687-2_3

Czumaj A, Davies P, Parter M. Graph sparsification for derandomizing massively parallel computation with low space. In: Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2020). Association for Computing Machinery; 2020:175-185. doi:10.1145/3350755.3400282

Brown TA, Prokopec A, Alistarh D-A. Non-blocking interpolation search trees with doubly-logarithmic running time. In: Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. Association for Computing Machinery; 2020:276-291. doi:10.1145/3332466.3374542

Alistarh D-A, Brown TA, Singhal N. Memory tagging: Minimalist synchronization for scalable concurrent data structures. In: Annual ACM Symposium on Parallelism in Algorithms and Architectures. Association for Computing Machinery; 2020:37-49. doi:10.1145/3350755.3400213

Koval N, Sokolova M, Fedorov A, Alistarh D-A, Tsitelov D. Testing concurrency on the JVM with Lincheck. In: Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP. Association for Computing Machinery; 2020:423-424. doi:10.1145/3332466.3374503

Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. Brief Announcement: Why Extension-Based Proofs Fail. In: Proceedings of the 39th Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2020:54-56. doi:10.1145/3382734.3405743

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Alistarh D-A, Nadiradze G, Sabour A. Dynamic averaging load balancing on cycles. In: 47th International Colloquium on Automata, Languages, and Programming. Vol 168. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2020. doi:10.4230/LIPIcs.ICALP.2020.7

Faghri F, Tabrizian I, Markov I, Alistarh D-A, Roy D, Ramezani-Kebrya A. Adaptive gradient quantization for data-parallel SGD. In: Advances in Neural Information Processing Systems. Vol 33. Neural Information Processing Systems Foundation; 2020.

Jelínek V, Töpfer M. On grounded L-graphs and their relatives. Electronic Journal of Combinatorics. 2019;26(3). doi:10.37236/8096

Nowak T, Rybicki J. Byzantine approximate agreement on graphs. In: 33rd International Symposium on Distributed Computing. Vol 146. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2019:29:1--29:17. doi:10.4230/LIPICS.DISC.2019.29

Chatterjee B, Peri S, Sa M, Singhal N. A simple and practical concurrent non-blocking unbounded graph with linearizable reachability queries. In: ACM International Conference Proceeding Series. ACM; 2019:168-177. doi:10.1145/3288599.3288617

Koval N, Alistarh D-A, Elizarov R. Lock-Free Channels for Programming via Communicating Sequential Processes. ACM Press; 2019:417-418. doi:10.1145/3293883.3297000

Ovaskainen O, Rybicki J, Abrego N. What can observational data reveal about metacommunity processes? Ecography. 2019;42(11):1877-1886. doi:10.1111/ecog.04444

Lenzen C, Rybicki J. Self-stabilising Byzantine clock synchronisation is almost as easy as consensus. Journal of the ACM. 2019;66(5). doi:10.1145/3339471

Khirirat S, Johansson M, Alistarh D-A. Gradient compression for communication-limited convex optimization. In: 2018 IEEE Conference on Decision and Control. IEEE; 2019. doi:10.1109/cdc.2018.8619625

Renggli C, Ashkboos S, Aghagolzadeh M, Alistarh D-A, Hoefler T. SparCML: High-performance sparse communication for machine learning. In: International Conference for High Performance Computing, Networking, Storage and Analysis, SC. ACM; 2019. doi:10.1145/3295500.3356222

Aganezov S, Zban I, Aksenov V, Alexeev N, Schatz MC. Recovering rearranged cancer chromosomes from karyotype graphs. BMC Bioinformatics. 2019;20. doi:10.1186/s12859-019-3208-4

Koval N, Alistarh D-A, Elizarov R. Scalable FIFO channels for programming via communicating sequential processes. In: 25th Anniversary of Euro-Par. Vol 11725. Springer Nature; 2019:317-333. doi:10.1007/978-3-030-29400-7_23

Yu C, Tang H, Renggli C, et al. Distributed learning over unreliable networks. In: 36th International Conference on Machine Learning, ICML 2019. Vol 2019-June. IMLS; 2019:12481-12512.

Alistarh D-A, Nadiradze G, Koval N. Efficiency guarantees for parallel incremental algorithms under relaxed schedulers. In: 31st ACM Symposium on Parallelism in Algorithms and Architectures. ACM Press; 2019:145-154. doi:10.1145/3323165.3323201

Wendler C, Alistarh D-A, Püschel M. Powerset convolutional neural networks. In: Vol 32. Neural Information Processing Systems Foundation; 2019:927-938.

Foerster K-T, Korhonen J, Rybicki J, Schmid S. Does preprocessing help under congestion? In: Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing. ACM; 2019:259-261. doi:10.1145/3293611.3331581

Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. Why extension-based proofs fail. In: Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. ACM Press; 2019:986-996. doi:10.1145/3313276.3316407

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Alistarh D-A, Aspnes J, King V, Saia J. Communication-efficient randomized consensus. Distributed Computing. 2018;31(6):489-501. doi:10.1007/s00446-017-0315-1

Grubic D, Tam L, Alistarh D-A, Zhang C. Synchronous multi-GPU training for deep learning with low-precision communications: An empirical study. In: Proceedings of the 21st International Conference on Extending Database Technology. OpenProceedings; 2018:145-156. doi:10.5441/002/EDBT.2018.14

Alistarh D-A, Leiserson W, Matveev A, Shavit N. ThreadScan: Automatic and scalable memory reclamation. ACM Transactions on Parallel Computing. 2018;4(4). doi:10.1145/3201897

Polino A, Pascanu R, Alistarh D-A. Model compression via distillation and quantization. In: 6th International Conference on Learning Representations. ; 2018.

Arbel Raviv M, Brown TA. Harnessing epoch-based reclamation for efficient range queries. In: Vol 53. ACM; 2018:14-27. doi:10.1145/3178487.3178489

Rybicki J, Kisdi E, Anttila J. Model of bacterial toxin-dependent pathogenesis explains infective dose. PNAS. 2018;115(42):10690-10695. doi:10.1073/pnas.1721061115

Lenzen C, Rybicki J. Near-optimal self-stabilising counting and firing squads. Distributed Computing. 2018. doi:10.1007/s00446-018-0342-6

Gilad E, Brown TA, Oskin M, Etsion Y. Snapshot based synchronization: A fast replacement for Hand-over-Hand locking. In: Vol 11014. Springer; 2018:465-479. doi:10.1007/978-3-319-96983-1_33

Alistarh D-A, De Sa C, Konstantinov NH. The convergence of stochastic gradient descent in asynchronous shared memory. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. ACM Press; 2018:169-178. doi:10.1145/3212734.3212763

Alistarh D-A. A brief tutorial on distributed and concurrent machine learning. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. ACM Press; 2018:487-488. doi:10.1145/3212734.3212798

Alistarh D-A, Brown TA, Kopinsky J, Nadiradze G. Relaxed schedulers can efficiently parallelize iterative algorithms. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. ACM Press; 2018:377-386. doi:10.1145/3212734.3212756

Alistarh D-A, Brown TA, Kopinsky J, Li JZ, Nadiradze G. Distributionally linearizable data structures. In: Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18. ACM Press; 2018:133-142. doi:10.1145/3210377.3210411

Alistarh D-A, Haider SK, Kübler R, Nadiradze G. The transactional conflict problem. In: Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18. ACM Press; 2018:383-392. doi:10.1145/3210377.3210406

Aksenov V, Alistarh D-A, Kuznetsov P. Brief Announcement: Performance prediction for coarse-grained locking. In: Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. ACM Press; 2018:411-413. doi:10.1145/3212734.3212785

Stojanov A, Smith TM, Alistarh D-A, Puschel M. Fast quantized arithmetic on : Trading compute for data movement. In: 2018 IEEE International Workshop on Signal Processing Systems. Vol 2018-October. IEEE; 2018. doi:10.1109/SiPS.2018.8598402

Alistarh D-A, Aspnes J, Gelashvili R. Space-optimal majority in population protocols. In: Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms. ACM; 2018:2221-2239. doi:10.1137/1.9781611975031.144

Alistarh D-A, Allen-Zhu Z, Li J. Byzantine stochastic gradient descent. In: Advances in Neural Information Processing Systems. Vol 2018. Neural Information Processing Systems Foundation; 2018:4613-4623.

Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli C. The convergence of sparsified gradient methods. In: Advances in Neural Information Processing Systems 31. Vol Volume 2018. Neural Information Processing Systems Foundation; 2018:5973-5983.

Baig G, Radunovic B, Alistarh D-A, Balkwill M, Karagiannis T, Qiu L. Towards unlicensed cellular networks in TV white spaces. In: Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies. ACM; 2017:2-14. doi:10.1145/3143361.3143367

Alistarh D-A, Kopinsky J, Li J, Nadiradze G. The power of choice in priority scheduling. In: Proceedings of the ACM Symposium on Principles of Distributed Computing. Vol Part 9314. ACM; 2017:283-292. doi:10.1145/3087801.3087810

Alistarh D-A, Grubic D, Li J, Tomioka R, Vojnovi M. QSGD: Communication-efficient SGD via gradient quantization and encoding. In: Vol 2017. Neural Information Processing Systems Foundation; 2017:1710-1721.

Zhang H, Li J, Kara K, Alistarh D-A, Liu J, Zhang C. ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning. In: Proceedings of Machine Learning Research. Vol 70. ML Research Press; 2017:4035-4043.