Koval N, Fedorov A, Sokolova M, Tsitelov D, Alistarh D-A. 2023. Lincheck: A practical framework for testing concurrent data structures on JVM. 35th International Conference on Computer Aided Verification . CAV: Computer Aided Verification, LNCS, vol. 13964, 156–169.

Koval N, Fedorov A, Sokolova M, Tsitelov D, Alistarh D-A. 2023. Lincheck: A practical framework for testing concurrent data structures on JVM, Zenodo,10.5281/ZENODO.7877757 .

Fedorov A, Hashemi D, Nadiradze G, Alistarh D-A. 2023. Provably-efficient and internally-deterministic parallel Union-Find. Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures. SPAA: Symposium on Parallelism in Algorithms and Architectures, 261–271.

Alistarh D-A, Gelashvili R, Rybicki J. 2022. Fast graphical population protocols. 25th International Conference on Principles of Distributed Systems. OPODIS, LIPIcs, vol. 217, 14.

Nikabadi A, Korhonen J. 2022. Beyond distributed subgraph detection: Induced subgraphs, multicolored problems and graph parameters. 25th International Conference on Principles of Distributed Systems. OPODIS, LIPIcs, vol. 217, 15.

Shevchenko A, Kungurtsev V, Mondelli M. 2022. Mean-field analysis of piecewise linear solutions for wide ReLU networks. Journal of Machine Learning Research. 23(130), 1–55.

Pacut M, Parham M, Rybicki J, Schmid S, Suomela J, Tereshchenko A. 2022. Brief announcement: Temporal locality in online algorithms. 36th International Symposium on Distributed Computing. DISC: Symposium on Distributed Computing vol. 246, 52.

Markov I, Ramezanikebrya H, Alistarh D-A. 2022. CGX: Adaptive system support for communication-efficient deep learning. Proceedings of the 23rd ACM/IFIP International Middleware Conference. Middleware: International Middleware Conference, 241–254.

Alistarh D-A, Rybicki J, Voitovych S. 2022. Near-optimal leader election in population protocols on graphs. Proceedings of the Annual ACM Symposium on Principles of Distributed Computing. PODC: Symposium on Principles of Distributed Computing, 246–256.

Brown TA, Sigouin W, Alistarh D-A. 2022. PathCAS: An efficient middle ground for concurrent search data structures. Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. PPoPP: Sympopsium on Principles and Practice of Parallel Programming, 385–399.

Postnikova A, Koval N, Nadiradze G, Alistarh D-A. 2022. Multi-queues can be state-of-the-art priority schedulers. Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. PPoPP: Sympopsium on Principles and Practice of Parallel Programming, 353–367.

Postnikova A, Koval N, Nadiradze G, Alistarh D-A. 2022. Multi-queues can be state-of-the-art priority schedulers, Zenodo,10.5281/ZENODO.5733408 .

Balliu A, Hirvonen J, Melnyk D, Olivetti D, Rybicki J, Suomela J. 2022. Local mending. International Colloquium on Structural Information and Communication Complexity. SIROCCO: Structural Information and Communication ComplexityLNCS vol. 13298, 1–20.

Iofinova EB, Peste E-A, Kurtz M, Alistarh D-A. 2022. How well do sparse ImageNet models transfer? 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR: Computer Vision and Pattern Recognition, 12256–12266.

Hoefler T, Alistarh D-A, Ben-Nun T, Dryden N, Peste E-A. 2021. Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks. Journal of Machine Learning Research. 22(241), 1–124.

Alistarh D-A, Gelashvili R, Rybicki J. 2021. Brief announcement: Fast graphical population protocols. 35th International Symposium on Distributed Computing. DISC: Distributed Computing , LIPIcs, vol. 209, 43.

Alistarh D-A, Gelashvili R, Nadiradze G. 2021. Lower bounds for shared-memory leader election under bounded write contention. 35th International Symposium on Distributed Computing. DISC: Distributed Computing, LIPIcs, vol. 209, 4.

Chatterjee B, Peri S, Sa M. 2021. Brief announcement: Non-blocking dynamic unbounded graphs with worst-case amortized bounds. 35th International Symposium on Distributed Computing. DISC: Distributed Computing, LIPIcs, vol. 209, 52.

Korhonen J, Paz A, Rybicki J, Schmid S, Suomela J. 2021. Brief announcement: Sinkless orientation is hard also in the supported LOCAL model. 35th International Symposium on Distributed Computing. DISC: Distributed Computing , LIPIcs, vol. 209, 58.

Fedorov A, Koval N, Alistarh D-A. 2021. A scalable concurrent algorithm for dynamic connectivity. Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures. SPAA: Symposium on Parallelism in Algorithms and Architectures, 208–220.

Kungurtsev V, Egan M, Chatterjee B, Alistarh D-A. 2021. Asynchronous optimization methods for efficient training of deep neural networks with guarantees. 35th AAAI Conference on Artificial Intelligence, AAAI 2021. AAAI: Conference on Artificial Intelligence vol. 35, 8209–8216.

Alimisis F, Davies P, Vandereycken B, Alistarh D-A. 2021. Distributed principal component analysis with limited communication. Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 4, 2823–2834.

Frantar E, Kurtic E, Alistarh D-A. 2021. M-FAC: Efficient matrix-free approximations of second-order information. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 14873–14886.

Alistarh D-A, Korhonen J. 2021. Towards tight communication lower bounds for distributed optimisation. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 7254–7266.

Davies P, Gurunanthan V, Moshrefi N, Ashkboos S, Alistarh D-A. 2021. New bounds for distributed mean estimation and variance reduction. 9th International Conference on Learning Representations.  ICLR: International Conference on Learning Representations.

Alistarh D-A, Davies P. 2021. Collecting coupons is faster with friends. Structural Information and Communication Complexity.  SIROCCO: International Colloquium on Structural Information and Communication Complexity, LNCS, vol. 12810, 3–12.

Alistarh D-A, Ellen F, Rybicki J. 2021. Wait-free approximate agreement on graphs. Structural Information and Communication Complexity. SIROCCO: Structural Information and Communication Complexity, LNCS, vol. 12810, 87–105.

Peste E-A, Iofinova EB, Vladu A, Alistarh D-A. 2021. AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 8557–8570.

Alimisis F, Davies P, Alistarh D-A. 2021. Communication-efficient distributed optimization with quantized preconditioners. Proceedings of the 38th International Conference on Machine Learning. International Conference on Machine Learning vol. 139, 196–206.

Li S, Tal Ben-Nun TB-N, Nadiradze G, Girolamo SD, Dryden N, Alistarh D-A, Hoefler T. 2021. Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging. IEEE Transactions on Parallel and Distributed Systems. 32(7), 9271898.

Chatterjee B, Walulya I, Tsigas P. 2021. Concurrent linearizable nearest neighbour search in LockFree-kD-tree. Theoretical Computer Science. 886, 27–48.

Alistarh D-A, Töpfer M, Uznaski P. 2021. Comparison dynamics in population protocols. Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing. PODC: Symposium on Principles of Distributed Computing, 55–65.

Czumaj A, Davies P, Parter M. 2021. Improved deterministic (+1) coloring in low-space MPC. Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing. PODC: Symposium on Principles of Distributed Computing, 469–479.

Czumaj A, Davies P, Parter M. 2021. Component stability in low-space massively parallel computation. Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing. PODC: Principles of Distributed Computing, 481–491.

Nadiradze G, Markov I, Chatterjee B, Kungurtsev V, Alistarh D-A. 2021. Elastic consistency: A practical consistency model for distributed stochastic gradient descent. Proceedings of the AAAI Conference on Artificial Intelligence. AAAI: Association for the Advancement of Artificial Intelligence vol. 35, 9037–9045.

Klein K, Pascual Perez G, Walter M, Kamath Hosdurg C, Capretto M, Cueto Noval M, Markov I, Yeo MX, Alwen JF, Pietrzak KZ. 2021. Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement. 2021 IEEE Symposium on Security and Privacy . SP: Symposium on Security and Privacy, 268–284.

Foerster K-T, Korhonen J, Paz A, Rybicki J, Schmid S. 2021. Input-dynamic distributed algorithms for communication networks. Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems. SIGMETRICS: International Conference on Measurement and Modeling of Computer Systems, 71–72.

Foerster K-T, Korhonen J, Paz A, Rybicki J, Schmid S. 2021. Input-dynamic distributed algorithms for communication networks. Proceedings of the ACM on Measurement and Analysis of Computing Systems. 5(1), 1–33.

Nadiradze G. 2021. On achieving scalability through relaxation. Institute of Science and Technology Austria.

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

Czumaj A, Davies P, Parter M. 2021. Graph sparsification for derandomizing massively parallel computation with low space. ACM Transactions on Algorithms. 17(2), 16.

Brandt S, Keller B, Rybicki J, Suomela J, Uitto J. 2021. Efficient load-balancing through distributed token dropping. Annual ACM Symposium on Parallelism in Algorithms and Architectures. SPAA: Symposium on Parallelism in Algorithms and Architectures , 129–139.

Alistarh D-A, Nadiradze G, Sabour A. 2021. Dynamic averaging load balancing on cycles. Algorithmica.

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

Censor-Hillel K, Dory M, Korhonen J, Leitersdorf D. 2021. Fast approximate shortest paths in the congested clique. Distributed Computing. 34, 463–487.

Czumaj A, Davies P, Parter M. 2021. Simple, deterministic, constant-round coloring in congested clique and MPC. SIAM Journal on Computing. 50(5), 1603–1626.

Czumaj A, Davies P. 2021. Exploiting spontaneous transmissions for broadcasting and leader election in radio networks. Journal of the ACM. 68(2), 13.

Alimisis F, Orvieto A, Becigneul G, Lucchi A. 2021. Momentum improves optimization on Riemannian manifolds. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and Statistics, PMLR, vol. 130, 1351–1359.

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

Alistarh D-A, Fedorov A, Koval N. 2020. In search of the fastest concurrent union-find algorithm. 23rd International Conference on Principles of Distributed Systems. OPODIS: International Conference on Principles of Distributed Systems, LIPIcs, vol. 153, 15:1-15:16.

Czumaj A, Davies P, Parter M. 2020. Simple, deterministic, constant-round coloring in the congested clique. Proceedings of the 2020 ACM Symposium on Principles of Distributed Computing. PODC: Symposium on Principles of Distributed Computing, 309–318.

Aksenov V, Alistarh D-A, Drozdova A, Mohtashami A. 2020. The splay-list: A distribution-adaptive concurrent skip-list. 34th International Symposium on Distributed Computing. DISC: Symposium on Distributed ComputingLIPIcs vol. 179, 3:1-3:18.

Singh SP, Alistarh D-A. 2020. WoodFisher: Efficient second-order approximation for neural network compression. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 18098–18109.

Aksenov V, Alistarh D-A, Korhonen J. 2020. Scalable belief propagation via relaxed scheduling. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 22361–22372.

Kurtz M, Kopinsky J, Gelashvili R, Matveev A, Carr J, Goin M, Leiserson W, Moore S, Nell B, Shavit N, Alistarh D-A. 2020. Inducing and exploiting activation sparsity for fast neural network inference. 37th International Conference on Machine Learning, ICML 2020. ICML: International Conference on Machine Learning vol. 119, 5533–5543.

Gurel NM, Kara K, Stojanov A, Smith T, Lemmin T, Alistarh D-A, Puschel M, Zhang C. 2020. Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. IEEE Transactions on Signal Processing. 68, 4268–4282.

Li S, Tal Ben-Nun TB-N, Girolamo SD, Alistarh D-A, Hoefler T. 2020. Taming unbalanced training workloads in deep learning with partial collective operations. Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. PPoPP: Sympopsium on Principles and Practice of Parallel Programming, 45–61.

Rybicki J, Abrego N, Ovaskainen O. 2020. Habitat fragmentation and species diversity in competitive communities. Ecology Letters. 23(3), 506–517.

Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. 2020. On the sample complexity of adversarial multi-source PAC learning. Proceedings of the 37th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 119, 5416–5425.

Bhatia S, Chatterjee B, Nathani D, Kaul M. 2020. A persistent homology perspective to the link prediction problem. Complex Networks and their applications VIII. COMPLEX: International Conference on Complex Networks and their Applications, SCI, vol. 881, 27–39.

Czumaj A, Davies P, Parter M. 2020. Graph sparsification for derandomizing massively parallel computation with low space. Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2020). SPAA: Symposium on Parallelism in Algorithms and Architectures, 175–185.

Brown TA, Prokopec A, Alistarh D-A. 2020. Non-blocking interpolation search trees with doubly-logarithmic running time. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. PPOPP: Principles and Practice of Parallel Programming, 276–291.

Alistarh D-A, Brown TA, Singhal N. 2020. Memory tagging: Minimalist synchronization for scalable concurrent data structures. Annual ACM Symposium on Parallelism in Algorithms and Architectures. SPAA: Symposium on Parallelism in Algorithms and Architectures, 37–49.

Koval N, Sokolova M, Fedorov A, Alistarh D-A, Tsitelov D. 2020. Testing concurrency on the JVM with Lincheck. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP. PPOPP: Principles and Practice of Parallel Programming, 423–424.

Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. 2020. Brief Announcement: Why Extension-Based Proofs Fail. Proceedings of the 39th Symposium on Principles of Distributed Computing. PODC: Principles of Distributed Computing, 54–56.

Brandt S, Keller B, Rybicki J, Suomela J, Uitto J. 2020. Brief announcement: Efficient load-balancing through distributed token dropping. 34th International Symposium on Distributed Computing. DISC: Symposium on Distributed Computing, LIPIcs, vol. 179, 40.

Alistarh D-A, Nadiradze G, Sabour A. 2020. Dynamic averaging load balancing on cycles. 47th International Colloquium on Automata, Languages, and Programming. ICALP: International Colloquium on Automata, Languages, and Programming, LIPIcs, vol. 168, 7.

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

Jelínek V, Töpfer M. 2019. On grounded L-graphs and their relatives. Electronic Journal of Combinatorics. 26(3), .17.

Nowak T, Rybicki J. 2019. Byzantine approximate agreement on graphs. 33rd International Symposium on Distributed Computing. DISC: International Symposium on Distributed Computing, LIPIcs, vol. 146, 29:1--29:17.

Chatterjee B, Peri S, Sa M, Singhal N. 2019. A simple and practical concurrent non-blocking unbounded graph with linearizable reachability queries. ACM International Conference Proceeding Series. ICDCN: Conference on Distributed Computing and Networking, 168–177.

Koval N, Alistarh D-A, Elizarov R. 2019. Lock-free channels for programming via communicating sequential processes, ACM Press,p.

Ovaskainen O, Rybicki J, Abrego N. 2019. What can observational data reveal about metacommunity processes? Ecography. 42(11), 1877–1886.

Lenzen C, Rybicki J. 2019. Self-stabilising Byzantine clock synchronisation is almost as easy as consensus. Journal of the ACM. 66(5), 32.

Khirirat S, Johansson M, Alistarh D-A. 2019. Gradient compression for communication-limited convex optimization. 2018 IEEE Conference on Decision and Control. CDC: Conference on Decision and Control, 8619625.

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

Aganezov S, Zban I, Aksenov V, Alexeev N, Schatz MC. 2019. Recovering rearranged cancer chromosomes from karyotype graphs. BMC Bioinformatics. 20, 641.

Koval N, Alistarh D-A, Elizarov R. 2019. Scalable FIFO channels for programming via communicating sequential processes. 25th Anniversary of Euro-Par. Euro-Par: European Conference on Parallel Processing, LNCS, vol. 11725, 317–333.

Yu C, Tang H, Renggli C, Kassing S, Singla A, Alistarh D-A, Zhang C, Liu J. 2019. Distributed learning over unreliable networks. 36th International Conference on Machine Learning, ICML 2019. ICML: International Conference on Machine Learning vol. 2019–June, 12481–12512.

Alistarh D-A, Nadiradze G, Koval N. 2019. Efficiency guarantees for parallel incremental algorithms under relaxed schedulers. 31st ACM Symposium on Parallelism in Algorithms and Architectures. SPAA: Symposium on Parallelism in Algorithms and Architectures, 145–154.

Wendler C, Alistarh D-A, Püschel M. 2019. Powerset convolutional neural networks. NIPS: Conference on Neural Information Processing Systems vol. 32, 927–938.

Foerster K-T, Korhonen J, Rybicki J, Schmid S. 2019. Does preprocessing help under congestion? Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing. PODC: Symposium on Principles of Distributed Computing, 259–261.

Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. 2019. Why extension-based proofs fail. Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. STOC: Symposium on Theory of Computing, 986–996.

Censor-Hillel K, Dory M, Korhonen J, Leitersdorf D. 2019. Fast approximate shortest paths in the congested clique. Proceedings of the 2019 ACM Symposium on Principles of Distributed Computin. PODC: Symposium on Principles of Distributed Computing, 74–83.

Alistarh D-A, Aspnes J, King V, Saia J. 2018. Communication-efficient randomized consensus. Distributed Computing. 31(6), 489–501.

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

Alistarh D-A, Leiserson W, Matveev A, Shavit N. 2018. ThreadScan: Automatic and scalable memory reclamation. ACM Transactions on Parallel Computing. 4(4), 18.

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

Arbel Raviv M, Brown TA. 2018. Harnessing epoch-based reclamation for efficient range queries. PPoPP: Principles and Practice of Parallel Programming, PPoPP, vol. 53, 14–27.

Rybicki J, Kisdi E, Anttila J. 2018. Model of bacterial toxin-dependent pathogenesis explains infective dose. PNAS. 115(42), 10690–10695.

Lenzen C, Rybicki J. 2018. Near-optimal self-stabilising counting and firing squads. Distributed Computing.

Gilad E, Brown TA, Oskin M, Etsion Y. 2018. Snapshot based synchronization: A fast replacement for Hand-over-Hand locking. Euro-Par: European Conference on Parallel Processing, LNCS, vol. 11014, 465–479.

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

Alistarh D-A. 2018. A brief tutorial on distributed and concurrent machine learning. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. PODC: Principles of Distributed Computing, 487–488.

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

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

Alistarh D-A, Haider SK, Kübler R, Nadiradze G. 2018. The transactional conflict problem. Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18. SPAA: Symposium on Parallelism in Algorithms and Architectures, 383–392.

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

Stojanov A, Smith TM, Alistarh D-A, Puschel M. 2018. Fast quantized arithmetic on : Trading compute for data movement. 2018 IEEE International Workshop on Signal Processing Systems. SiPS: Workshop on Signal Processing Systems vol. 2018–October, 8598402.

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