Klein, K., Pascual Perez, G., Walter, M., Kamath Hosdurg, C., Capretto, M., Cueto Noval, M.,https://doi.org/10.1109/sp40001.2021.00035__Pietrzak, K. Z. (2021). Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement. In 2021 IEEE Symposium on Security and Privacy  (pp. 268–284). San Francisco, CA, United States: IEEE.https://doi.org/10.1109/sp40001.2021.00035 

Foerster, K.-T., Korhonen, J., Paz, A., Rybicki, J., &; Schmid, S. (2021). Input-dynamic distributed algorithms for communication networks. In Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems (pp. 71–72). Virtual, Online: Association for Computing Machinery.https://doi.org/10.1145/3410220.3453923 

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. Association for Computing Machinery.https://doi.org/10.1145/3447384 

Nadiradze, G. (2021). On achieving scalability through relaxation. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:10429 

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

Czumaj, A., Davies, P., &; Parter, M. (2021). Graph sparsification for derandomizing massively parallel computation with low space. ACM Transactions on Algorithms. Association for Computing Machinery.https://doi.org/10.1145/3451992 

Brandt, S., Keller, B., Rybicki, J., Suomela, J., &; Uitto, J. (2021). Efficient load-balancing through distributed token dropping. In Annual ACM Symposium on Parallelism in Algorithms and Architectures (pp. 129–139).https://doi.org/10.1145/3409964.3461785__Virtual Event, United States.https://doi.org/10.1145/3409964.3461785 

Alistarh, D.-A., Nadiradze, G., &; Sabour, A. (2021). Dynamic averaging load balancing on cycles. Algorithmica. Virtual, Online; Germany: Springer Nature.https://doi.org/10.1007/s00453-021-00905-9 

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

Censor-Hillel, K., Dory, M., Korhonen, J., &; Leitersdorf, D. (2021). Fast approximate shortest paths in the congested clique. Distributed Computing. Springer Nature.https://doi.org/10.1007/s00446-020-00380-5 

Czumaj, A., Davies, P., &; Parter, M. (2021). Simple, deterministic, constant-round coloring in congested clique and MPC. SIAM Journal on Computing. Society for Industrial &; Applied Mathematics.https://doi.org/10.1137/20m1366502 

Czumaj, A., &; Davies, P. (2021). Exploiting spontaneous transmissions for broadcasting and leader election in radio networks. Journal of the ACM. Association for Computing Machinery.https://doi.org/10.1145/3446383 

Alimisis, F., Orvieto, A., Becigneul, G., &; Lucchi, A. (2021). Momentum improves optimization on Riemannian manifolds. In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (Vol. 130, pp. 1351–1359). San Diego, CA, United States; Virtual: ML Research Press.

Arbel-Raviv, M., Brown, T. A., &; Morrison, A. (2020). Getting to the root of concurrent binary search tree performance. In Proceedings of the 2018 USENIX Annual Technical Conference (pp. 295–306). Boston, MA, United States: USENIX Association.

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

Czumaj, A., Davies, P., &; Parter, M. (2020). Simple, deterministic, constant-round coloring in the congested clique. In Proceedings of the 2020 ACM Symposium on Principles of Distributed Computing (pp. 309–318). Salerno, Italy: Association for Computing Machinery.https://doi.org/10.1145/3382734.3405751 

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

Singh, S. P., &; Alistarh, D.-A. (2020). WoodFisher: Efficient second-order approximation for neural network compression. In Advances in Neural Information Processing Systems (Vol. 33, pp. 18098–18109). Vancouver, Canada: Curran Associates.

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

Kurtz, M., Kopinsky, J., Gelashvili, R., Matveev, A., Carr, J., Goin, M.,  Alistarh, D.-A. (2020). Inducing and exploiting activation sparsity for fast neural network inference. In 37th International Conference on Machine Learning, ICML 2020 (Vol. 119, pp. 5533–5543). Online.

Gurel, N. M., Kara, K., Stojanov, A., Smith, T., Lemmin, T., Alistarh, D.-A.,https://doi.org/10.1109/TSP.2020.3010355__Zhang, C. (2020). Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. IEEE Transactions on Signal Processing. IEEE.https://doi.org/10.1109/TSP.2020.3010355 

Li, S., Tal Ben-Nun, T. B.-N., Girolamo, S. D., Alistarh, D.-A., &; Hoefler, T. (2020). 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 (pp. 45–61). San Diego, CA, United States: Association for Computing Machinery.https://doi.org/10.1145/3332466.3374528 

Rybicki, J., Abrego, N., &; Ovaskainen, O. (2020). Habitat fragmentation and species diversity in competitive communities. Ecology Letters. Wiley.https://doi.org/10.1111/ele.13450 

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

Bhatia, S., Chatterjee, B., Nathani, D., &; Kaul, M. (2020). A persistent homology perspective to the link prediction problem. In Complex Networks and their applications VIII (Vol. 881, pp. 27–39). Lisbon, Portugal: Springer Nature.https://doi.org/10.1007/978-3-030-36687-2_3 

Czumaj, A., Davies, P., &; Parter, M. (2020). 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) (pp. 175–185). Virtual Event, United States: Association for Computing Machinery.https://doi.org/10.1145/3350755.3400282 

Brown, T. A., Prokopec, A., &; Alistarh, D.-A. (2020). Non-blocking interpolation search trees with doubly-logarithmic running time. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 276–291). San Diego, CA, United States: Association for Computing Machinery.https://doi.org/10.1145/3332466.3374542 

Alistarh, D.-A., Brown, T. A., &; Singhal, N. (2020). Memory tagging: Minimalist synchronization for scalable concurrent data structures. In Annual ACM Symposium on Parallelism in Algorithms and Architectures (pp. 37–49). Virtual Event, United States: Association for Computing Machinery.https://doi.org/10.1145/3350755.3400213 

Koval, N., Sokolova, M., Fedorov, A., Alistarh, D.-A., &; Tsitelov, D. (2020). Testing concurrency on the JVM with Lincheck. In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP (pp. 423–424). San Diego, CA, United States: Association for Computing Machinery.https://doi.org/10.1145/3332466.3374503 

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

Brandt, S., Keller, B., Rybicki, J., Suomela, J., &; Uitto, J. (2020). Brief announcement: Efficient load-balancing through distributed token dropping. In 34th International Symposium on Distributed Computing (Vol. 179). Virtual: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.DISC.2020.40 

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

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

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

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

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

Koval, N., Alistarh, D.-A., &; Elizarov, R. (2019). Lock-free channels for programming via communicating sequential processes. Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming (pp. 417–418). Washington, NY, United States: ACM Press.https://doi.org/10.1145/3293883.3297000 

Ovaskainen, O., Rybicki, J., &; Abrego, N. (2019). What can observational data reveal about metacommunity processes? Ecography. Wiley.https://doi.org/10.1111/ecog.04444 

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

Khirirat, S., Johansson, M., &; Alistarh, D.-A. (2019). Gradient compression for communication-limited convex optimization. In 2018 IEEE Conference on Decision and Control. Miami Beach, FL, United States: IEEE.https://doi.org/10.1109/cdc.2018.8619625 

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

Aganezov, S., Zban, I., Aksenov, V., Alexeev, N., &; Schatz, M. C. (2019). Recovering rearranged cancer chromosomes from karyotype graphs. BMC Bioinformatics. BMC.https://doi.org/10.1186/s12859-019-3208-4 

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

Yu, C., Tang, H., Renggli, C., Kassing, S., Singla, A., Alistarh, D.-A.,  Liu, J. (2019). Distributed learning over unreliable networks. In 36th International Conference on Machine Learning, ICML 2019 (Vol. 2019–June, pp. 12481–12512). Long Beach, CA, United States: IMLS.

Alistarh, D.-A., Nadiradze, G., &; Koval, N. (2019). Efficiency guarantees for parallel incremental algorithms under relaxed schedulers. In 31st ACM Symposium on Parallelism in Algorithms and Architectures (pp. 145–154). Phoenix, AZ, United States: ACM Press.https://doi.org/10.1145/3323165.3323201 

Wendler, C., Alistarh, D.-A., &; Püschel, M. (2019). Powerset convolutional neural networks (Vol. 32, pp. 927–938). Presented at the NIPS: Conference on Neural Information Processing Systems, Vancouver, Canada: Neural Information Processing Systems Foundation.

Foerster, K.-T., Korhonen, J., Rybicki, J., &; Schmid, S. (2019). Does preprocessing help under congestion? In Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing (pp. 259–261). Toronto, ON, Canada: ACM.https://doi.org/10.1145/3293611.3331581 

Alistarh, D.-A., Aspnes, J., Ellen, F., Gelashvili, R., &; Zhu, L. (2019). Why extension-based proofs fail. In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing (pp. 986–996). Phoenix, AZ, United States: ACM Press.https://doi.org/10.1145/3313276.3316407 

Censor-Hillel, K., Dory, M., Korhonen, J., &; Leitersdorf, D. (2019). Fast approximate shortest paths in the congested clique. In Proceedings of the 2019 ACM Symposium on Principles of Distributed Computin (pp. 74–83). Toronto, ON, Canada: ACM.https://doi.org/10.1145/3293611.3331633 

Alistarh, D.-A., Aspnes, J., King, V., &; Saia, J. (2018). Communication-efficient randomized consensus. Distributed Computing. Springer.https://doi.org/10.1007/s00446-017-0315-1 

Grubic, D., Tam, L., Alistarh, D.-A., &; Zhang, C. (2018). 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 (pp. 145–156). Vienna, Austria: OpenProceedings.https://doi.org/10.5441/002/EDBT.2018.14 

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

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

Arbel Raviv, M., &; Brown, T. A. (2018). Harnessing epoch-based reclamation for efficient range queries (Vol. 53, pp. 14–27). Presented at the PPoPP: Principles and Practice of Parallel Programming, Vienna, Austria: ACM.https://doi.org/10.1145/3178487.3178489 

Rybicki, J., Kisdi, E., &; Anttila, J. (2018). Model of bacterial toxin-dependent pathogenesis explains infective dose. PNAS. National Academy of Sciences.https://doi.org/10.1073/pnas.1721061115 

Lenzen, C., &; Rybicki, J. (2018). Near-optimal self-stabilising counting and firing squads. Distributed Computing. Springer.https://doi.org/10.1007/s00446-018-0342-6 

Gilad, E., Brown, T. A., Oskin, M., &; Etsion, Y. (2018). Snapshot based synchronization: A fast replacement for Hand-over-Hand locking (Vol. 11014, pp. 465–479). Presented at the Euro-Par: European Conference on Parallel Processing, Turin, Italy: Springer.https://doi.org/10.1007/978-3-319-96983-1_33 

Alistarh, D.-A., De Sa, C., &; Konstantinov, N. H. (2018). The convergence of stochastic gradient descent in asynchronous shared memory. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 169–178). Egham, United Kingdom: ACM Press.https://doi.org/10.1145/3212734.3212763 

Alistarh, D.-A. (2018). A brief tutorial on distributed and concurrent machine learning. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 487–488). Egham, United Kingdom: ACM Press.https://doi.org/10.1145/3212734.3212798 

Alistarh, D.-A., Brown, T. A., Kopinsky, J., &; Nadiradze, G. (2018). Relaxed schedulers can efficiently parallelize iterative algorithms. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 377–386). Egham, United Kingdom: ACM Press.https://doi.org/10.1145/3212734.3212756 

Alistarh, D.-A., Brown, T. A., Kopinsky, J., Li, J. Z., &; Nadiradze, G. (2018). Distributionally linearizable data structures. In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18 (pp. 133–142). Vienna, Austria: ACM Press.https://doi.org/10.1145/3210377.3210411 

Alistarh, D.-A., Haider, S. K., Kübler, R., &; Nadiradze, G. (2018). The transactional conflict problem. In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18 (pp. 383–392). Vienna, Austria: ACM Press.https://doi.org/10.1145/3210377.3210406 

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

Stojanov, A., Smith, T. M., Alistarh, D.-A., &; Puschel, M. (2018). Fast quantized arithmetic on : Trading compute for data movement. In 2018 IEEE International Workshop on Signal Processing Systems (Vol. 2018–October). Cape Town, South Africa: IEEE.https://doi.org/10.1109/SiPS.2018.8598402 

Alistarh, D.-A., Aspnes, J., &; Gelashvili, R. (2018). Space-optimal majority in population protocols. In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 2221–2239). New Orleans, LA, United States: ACM.https://doi.org/10.1137/1.9781611975031.144 

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

Alistarh, D.-A., Hoefler, T., Johansson, M., Konstantinov, N. H., Khirirat, S., &; Renggli, C. (2018). The convergence of sparsified gradient methods. In Advances in Neural Information Processing Systems 31 (Vol. Volume 2018, pp. 5973–5983). Montreal, Canada: Neural Information Processing Systems Foundation.

Baig, G., Radunovic, B., Alistarh, D.-A., Balkwill, M., Karagiannis, T., &; Qiu, L. (2017). Towards unlicensed cellular networks in TV white spaces. In Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies (pp. 2–14). Incheon, South Korea: ACM.https://doi.org/10.1145/3143361.3143367 

Alistarh, D.-A., Kopinsky, J., Li, J., &; Nadiradze, G. (2017). The power of choice in priority scheduling. In Proceedings of the ACM Symposium on Principles of Distributed Computing (Vol. Part 9314, pp. 283–292). Washington, WA, USA: ACM.https://doi.org/10.1145/3087801.3087810 

Alistarh, D.-A., Grubic, D., Li, J., Tomioka, R., &; Vojnovi, M. (2017). QSGD: Communication-efficient SGD via gradient quantization and encoding (Vol. 2017, pp. 1710–1721). Presented at the NIPS: Neural Information Processing System, Long Beach, CA, United States: Neural Information Processing Systems Foundation.

Zhang, H., Li, J., Kara, K., Alistarh, D.-A., Liu, J., &; Zhang, C. (2017). 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, pp. 4035–4043). Sydney, Australia: ML Research Press.