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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.