A. Shevchenko, V. Kungurtsev, and M. Mondelli, “Mean-field analysis of piecewise linear solutions for wide ReLU networks,” Journal of Machine Learning Research, vol. 23, no. 130. Journal of Machine Learning Research, pp. 1–55, 2022.
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arXiv: 2111.02278

M. Pacut, M. Parham, J. Rybicki, S. Schmid, J. Suomela, and A. Tereshchenko, “Brief announcement: Temporal locality in online algorithms,” in 36th International Symposium on Distributed Computing, Augusta, GA, United States, 2022, vol. 246.
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DOI: 10.4230/LIPIcs.DISC.2022.52

I. Markov, H. Ramezanikebrya, and D.-A. Alistarh, “CGX: Adaptive system support for communication-efficient deep learning,” in Proceedings of the 23rd ACM/IFIP International Middleware Conference, Quebec, QC, Canada, 2022, pp. 241–254.
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arXiv: 2111.08617

D.-A. Alistarh, J. Rybicki, and S. Voitovych, “Near-optimal leader election in population protocols on graphs,” in Proceedings of the Annual ACM Symposium on Principles of Distributed Computing, Salerno, Italy, 2022, pp. 246–256.
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DOI: 10.1145/3519270.3538435
arXiv: 2205.12597

T. A. Brown, W. Sigouin, and D.-A. Alistarh, “PathCAS: An efficient middle ground for concurrent search data structures,” in Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Seoul, Republic of Korea, 2022, pp. 385–399.
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DOI: 10.1145/3503221.3508410
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A. Postnikova, N. Koval, G. Nadiradze, and D.-A. Alistarh, “Multi-queues can be state-of-the-art priority schedulers,” in Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Seoul, Republic of Korea, 2022, pp. 353–367.
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arXiv: 2109.00657

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

A. Balliu, J. Hirvonen, D. Melnyk, D. Olivetti, J. Rybicki, and J. Suomela, “Local mending,” in International Colloquium on Structural Information and Communication Complexity, Paderborn, Germany, 2022, vol. 13298, pp. 1–20.
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arXiv: 2102.08703

E. B. Iofinova, E.-A. Peste, M. Kurtz, and D.-A. Alistarh, “How well do sparse ImageNet models transfer?,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, United States, 2022, pp. 12256–12266.
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arXiv: 2111.13445

T. Hoefler, D.-A. Alistarh, T. Ben-Nun, N. Dryden, and E.-A. Peste, “Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks,” Journal of Machine Learning Research, vol. 22, no. 241. Journal of Machine Learning Research, pp. 1–124, 2021.
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arXiv: 2102.00554

D.-A. Alistarh, R. Gelashvili, and J. Rybicki, “Brief announcement: Fast graphical population protocols,” in 35th International Symposium on Distributed Computing, Freiburg, Germany, 2021, vol. 209.
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arXiv: 2102.08808

D.-A. Alistarh, R. Gelashvili, and G. Nadiradze, “Lower bounds for shared-memory leader election under bounded write contention,” in 35th International Symposium on Distributed Computing, Freiburg, Germany, 2021, vol. 209.
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B. Chatterjee, S. Peri, and M. Sa, “Brief announcement: Non-blocking dynamic unbounded graphs with worst-case amortized bounds,” in 35th International Symposium on Distributed Computing, Freiburg, Germany, 2021, vol. 209.
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arXiv: 2003.01697

J. Korhonen, A. Paz, J. Rybicki, S. Schmid, and J. Suomela, “Brief announcement: Sinkless orientation is hard also in the supported LOCAL model,” in 35th International Symposium on Distributed Computing, Freiburg, Germany, 2021, vol. 209.
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arXiv: 2108.02655

A. Fedorov, N. Koval, and D.-A. Alistarh, “A scalable concurrent algorithm for dynamic connectivity,” in Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures, Virtual, Online, 2021, pp. 208–220.
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arXiv: 2105.08098

V. Kungurtsev, M. Egan, B. Chatterjee, and D.-A. Alistarh, “Asynchronous optimization methods for efficient training of deep neural networks with guarantees,” in 35th AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual, Online, 2021, vol. 35, no. 9B, pp. 8209–8216.
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arXiv: 1905.11845

F. Alimisis, P. Davies, B. Vandereycken, and D.-A. Alistarh, “Distributed principal component analysis with limited communication,” in Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 4, pp. 2823–2834.
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arXiv: 2110.14391

E. Frantar, E. Kurtic, and D.-A. Alistarh, “M-FAC: Efficient matrix-free approximations of second-order information,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 14873–14886.
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arXiv: 2010.08222

D.-A. Alistarh and J. Korhonen, “Towards tight communication lower bounds for distributed optimisation,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 7254–7266.
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arXiv: 2010.08222

P. Davies, V. Gurunanthan, N. Moshrefi, S. Ashkboos, and D.-A. Alistarh, “New bounds for distributed mean estimation and variance reduction,” in 9th International Conference on Learning Representations, Virtual, 2021.
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arXiv: 2002.09268

D.-A. Alistarh and P. Davies, “Collecting coupons is faster with friends,” in Structural Information and Communication Complexity, Wrocaw, Poland, 2021, vol. 12810, pp. 3–12.
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D.-A. Alistarh, F. Ellen, and J. Rybicki, “Wait-free approximate agreement on graphs,” in Structural Information and Communication Complexity, Wrocaw, Poland, 2021, vol. 12810, pp. 87–105.
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arXiv: 2103.08949

E.-A. Peste, E. B. Iofinova, A. Vladu, and D.-A. Alistarh, “AC/DC: Alternating Compressed/DeCompressed training of deep neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 8557–8570.
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arXiv: 2106.12379

F. Alimisis, P. Davies, and D.-A. Alistarh, “Communication-efficient distributed optimization with quantized preconditioners,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 196–206.
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S. Li et al., “Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging,” IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 7. IEEE, 2021.
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B. Chatterjee, I. Walulya, and P. Tsigas, “Concurrent linearizable nearest neighbour search in LockFree-kD-tree,” Theoretical Computer Science, vol. 886. Elsevier, pp. 27–48, 2021.
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D.-A. Alistarh, M. Töpfer, and P. Uznaski, “Comparison dynamics in population protocols,” in Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, Virtual, Italy, 2021, pp. 55–65.
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A. Czumaj, P. Davies, and M. Parter, “Improved deterministic (+1) coloring in low-space MPC,” in Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, Virtual, Italy, 2021, pp. 469–479.
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A. Czumaj, P. Davies, and M. Parter, “Component stability in low-space massively parallel computation,” in Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, Virtual, Italy, 2021, pp. 481–491.
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G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, and D.-A. Alistarh, “Elastic consistency: A practical consistency model for distributed stochastic gradient descent,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 10, pp. 9037–9045.
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K. Klein et al., “Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement,” in 2021 IEEE Symposium on Security and Privacy , San Francisco, CA, United States, 2021, pp. 268–284.
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K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, and S. Schmid, “Input-dynamic distributed algorithms for communication networks,” in Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, Virtual, Online, 2021, pp. 71–72.
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K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, and S. Schmid, “Input-dynamic distributed algorithms for communication networks,” Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 5, no. 1. Association for Computing Machinery, pp. 1–33, 2021.
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G. Nadiradze, “On achieving scalability through relaxation,” Institute of Science and Technology Austria, 2021.
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G. Nadiradze, A. Sabour, P. Davies, S. Li, and D.-A. Alistarh, “Asynchronous decentralized SGD with quantized and local updates,” in 35th Conference on Neural Information Processing Systems, Sydney, Australia, 2021.
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arXiv: 1910.12308

A. Czumaj, P. Davies, and M. Parter, “Graph sparsification for derandomizing massively parallel computation with low space,” ACM Transactions on Algorithms, vol. 17, no. 2. Association for Computing Machinery, 2021.
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S. Brandt, B. Keller, J. Rybicki, J. Suomela, and J. Uitto, “Efficient load-balancing through distributed token dropping,” in Annual ACM Symposium on Parallelism in Algorithms and Architectures,  Virtual Event, United States, 2021, pp. 129–139.
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D.-A. Alistarh, G. Nadiradze, and A. Sabour, “Dynamic averaging load balancing on cycles,” Algorithmica. Springer Nature, 2021.
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A. Ramezani-Kebrya, F. Faghri, I. Markov, V. Aksenov, D.-A. Alistarh, and D. M. Roy, “NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization,” Journal of Machine Learning Research, vol. 22, no. 114. Journal of Machine Learning Research, p. 143, 2021.
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arXiv: 1908.06077

K. Censor-Hillel, M. Dory, J. Korhonen, and D. Leitersdorf, “Fast approximate shortest paths in the congested clique,” Distributed Computing, vol. 34. Springer Nature, pp. 463–487, 2021.
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A. Czumaj, P. Davies, and M. Parter, “Simple, deterministic, constant-round coloring in congested clique and MPC,” SIAM Journal on Computing, vol. 50, no. 5. Society for Industrial &; Applied Mathematics, pp. 1603–1626, 2021.
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A. Czumaj and P. Davies, “Exploiting spontaneous transmissions for broadcasting and leader election in radio networks,” Journal of the ACM, vol. 68, no. 2. Association for Computing Machinery, 2021.
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F. Alimisis, A. Orvieto, G. Becigneul, and A. Lucchi, “Momentum improves optimization on Riemannian manifolds,” in Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, San Diego, CA, United States; Virtual, 2021, vol. 130, pp. 1351–1359.
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arXiv: 2002.04144

M. Arbel-Raviv, T. A. Brown, and A. Morrison, “Getting to the root of concurrent binary search tree performance,” in Proceedings of the 2018 USENIX Annual Technical Conference, Boston, MA, United States, 2020, pp. 295–306.
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D.-A. Alistarh, A. Fedorov, and N. Koval, “In search of the fastest concurrent union-find algorithm,” in 23rd International Conference on Principles of Distributed Systems, Neuchatal, Switzerland, 2020, vol. 153, p. 15:1-15:16.
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A. Czumaj, P. Davies, and M. Parter, “Simple, deterministic, constant-round coloring in the congested clique,” in Proceedings of the 2020 ACM Symposium on Principles of Distributed Computing, Salerno, Italy, 2020, pp. 309–318.
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V. Aksenov, D.-A. Alistarh, A. Drozdova, and A. Mohtashami, “The splay-list: A distribution-adaptive concurrent skip-list,” in 34th International Symposium on Distributed Computing, Freiburg, Germany, 2020, vol. 179, p. 3:1-3:18.
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arXiv: 2008.01009

S. P. Singh and D.-A. Alistarh, “WoodFisher: Efficient second-order approximation for neural network compression,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 18098–18109.
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arXiv: 2004.14340

V. Aksenov, D.-A. Alistarh, and J. Korhonen, “Scalable belief propagation via relaxed scheduling,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 22361–22372.
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M. Kurtz et al., “Inducing and exploiting activation sparsity for fast neural network inference,” in 37th International Conference on Machine Learning, ICML 2020, Online, 2020, vol. 119, pp. 5533–5543.
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N. M. Gurel et al., “Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications,” IEEE Transactions on Signal Processing, vol. 68. IEEE, pp. 4268–4282, 2020.
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S. Li, T. B.-N. Tal Ben-Nun, S. D. Girolamo, D.-A. Alistarh, and T. Hoefler, “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, San Diego, CA, United States, 2020, pp. 45–61.
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J. Rybicki, N. Abrego, and O. Ovaskainen, “Habitat fragmentation and species diversity in competitive communities,” Ecology Letters, vol. 23, no. 3. Wiley, pp. 506–517, 2020.
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N. H. Konstantinov, E. Frantar, D.-A. Alistarh, and C. Lampert, “On the sample complexity of adversarial multi-source PAC learning,” in Proceedings of the 37th International Conference on Machine Learning, Online, 2020, vol. 119, pp. 5416–5425.
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S. Bhatia, B. Chatterjee, D. Nathani, and M. Kaul, “A persistent homology perspective to the link prediction problem,” in Complex Networks and their applications VIII, Lisbon, Portugal, 2020, vol. 881, pp. 27–39.
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A. Czumaj, P. Davies, and M. Parter, “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), Virtual Event, United States, 2020, no. 7, pp. 175–185.
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T. A. Brown, A. Prokopec, and D.-A. Alistarh, “Non-blocking interpolation search trees with doubly-logarithmic running time,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, San Diego, CA, United States, 2020, pp. 276–291.
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D.-A. Alistarh, T. A. Brown, and N. Singhal, “Memory tagging: Minimalist synchronization for scalable concurrent data structures,” in Annual ACM Symposium on Parallelism in Algorithms and Architectures, Virtual Event, United States, 2020, no. 7, pp. 37–49.
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N. Koval, M. Sokolova, A. Fedorov, D.-A. Alistarh, and D. Tsitelov, “Testing concurrency on the JVM with Lincheck,” in Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, San Diego, CA, United States, 2020, pp. 423–424.
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D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, and L. Zhu, “Brief Announcement: Why Extension-Based Proofs Fail,” in Proceedings of the 39th Symposium on Principles of Distributed Computing, Virtual, Italy, 2020, pp. 54–56.
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S. Brandt, B. Keller, J. Rybicki, J. Suomela, and J. Uitto, “Brief announcement: Efficient load-balancing through distributed token dropping,” in 34th International Symposium on Distributed Computing, Virtual, 2020, vol. 179.
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D.-A. Alistarh, G. Nadiradze, and A. Sabour, “Dynamic averaging load balancing on cycles,” in 47th International Colloquium on Automata, Languages, and Programming, Saarbrücken, Germany, Virtual, 2020, vol. 168.
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F. Faghri, I. Tabrizian, I. Markov, D.-A. Alistarh, D. Roy, and A. Ramezani-Kebrya, “Adaptive gradient quantization for data-parallel SGD,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33.
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V. Jelínek and M. Töpfer, “On grounded L-graphs and their relatives,” Electronic Journal of Combinatorics, vol. 26, no. 3. Electronic Journal of Combinatorics, 2019.
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T. Nowak and J. Rybicki, “Byzantine approximate agreement on graphs,” in 33rd International Symposium on Distributed Computing, Budapest, Hungary, 2019, vol. 146, p. 29:1--29:17.
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B. Chatterjee, S. Peri, M. Sa, and N. Singhal, “A simple and practical concurrent non-blocking unbounded graph with linearizable reachability queries,” in ACM International Conference Proceeding Series, Bangalore, India, 2019, pp. 168–177.
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N. Koval, D.-A. Alistarh, and R. Elizarov, Lock-free channels for programming via communicating sequential processes. ACM Press, 2019, pp. 417–418.
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O. Ovaskainen, J. Rybicki, and N. Abrego, “What can observational data reveal about metacommunity processes?,” Ecography, vol. 42, no. 11. Wiley, pp. 1877–1886, 2019.
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C. Lenzen and J. Rybicki, “Self-stabilising Byzantine clock synchronisation is almost as easy as consensus,” Journal of the ACM, vol. 66, no. 5. ACM, 2019.
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S. Khirirat, M. Johansson, and D.-A. Alistarh, “Gradient compression for communication-limited convex optimization,” in 2018 IEEE Conference on Decision and Control, Miami Beach, FL, United States, 2019.
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C. Renggli, S. Ashkboos, M. Aghagolzadeh, D.-A. Alistarh, and T. Hoefler, “SparCML: High-performance sparse communication for machine learning,” in International Conference for High Performance Computing, Networking, Storage and Analysis, SC, Denver, CO, Unites States, 2019.
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S. Aganezov, I. Zban, V. Aksenov, N. Alexeev, and M. C. Schatz, “Recovering rearranged cancer chromosomes from karyotype graphs,” BMC Bioinformatics, vol. 20. BMC, 2019.
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N. Koval, D.-A. Alistarh, and R. Elizarov, “Scalable FIFO channels for programming via communicating sequential processes,” in 25th Anniversary of Euro-Par, Göttingen, Germany, 2019, vol. 11725, pp. 317–333.
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C. Yu et al., “Distributed learning over unreliable networks,” in 36th International Conference on Machine Learning, ICML 2019, Long Beach, CA, United States, 2019, vol. 2019–June, pp. 12481–12512.
LibreCat: https://research-explorer.ista.ac.at/record/7437
WoS: 000684034307036
arXiv: 1810.07766

D.-A. Alistarh, G. Nadiradze, and N. Koval, “Efficiency guarantees for parallel incremental algorithms under relaxed schedulers,” in 31st ACM Symposium on Parallelism in Algorithms and Architectures, Phoenix, AZ, United States, 2019, pp. 145–154.
LibreCat: https://research-explorer.ista.ac.at/record/6673
DOI: 10.1145/3323165.3323201
WoS: 000507618500018
arXiv: 2003.09363

C. Wendler, D.-A. Alistarh, and M. Püschel, “Powerset convolutional neural networks,” presented at the NIPS: Conference on Neural Information Processing Systems, Vancouver, Canada, 2019, vol. 32, pp. 927–938.
LibreCat: https://research-explorer.ista.ac.at/record/7542
WoS: 000534424300084
arXiv: 1909.02253

K.-T. Foerster, J. Korhonen, J. Rybicki, and S. Schmid, “Does preprocessing help under congestion?,” in Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing, Toronto, ON, Canada, 2019, pp. 259–261.
LibreCat: https://research-explorer.ista.ac.at/record/6935
DOI: 10.1145/3293611.3331581
WoS: 000570442000037
arXiv: 1905.03012

D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, and L. Zhu, “Why extension-based proofs fail,” in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, Phoenix, AZ, United States, 2019, pp. 986–996.
LibreCat: https://research-explorer.ista.ac.at/record/6676
DOI: 10.1145/3313276.3316407
WoS: 000523199100089
arXiv: 1811.01421

K. Censor-Hillel, M. Dory, J. Korhonen, and D. Leitersdorf, “Fast approximate shortest paths in the congested clique,” in Proceedings of the 2019 ACM Symposium on Principles of Distributed Computin, Toronto, ON, Canada, 2019, pp. 74–83.
LibreCat: https://research-explorer.ista.ac.at/record/6933
DOI: 10.1145/3293611.3331633
WoS: 000570442000011
arXiv: 1903.05956

D.-A. Alistarh, J. Aspnes, V. King, and J. Saia, “Communication-efficient randomized consensus,” Distributed Computing, vol. 31, no. 6. Springer, pp. 489–501, 2018.
LibreCat: https://research-explorer.ista.ac.at/record/536
DOI: 10.1007/s00446-017-0315-1

D. Grubic, L. Tam, D.-A. Alistarh, and C. Zhang, “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, Vienna, Austria, 2018, pp. 145–156.
LibreCat: https://research-explorer.ista.ac.at/record/7116
DOI: 10.5441/002/EDBT.2018.14

D.-A. Alistarh, W. Leiserson, A. Matveev, and N. Shavit, “ThreadScan: Automatic and scalable memory reclamation,” ACM Transactions on Parallel Computing, vol. 4, no. 4. Association for Computing Machinery, 2018.
LibreCat: https://research-explorer.ista.ac.at/record/6001
DOI: 10.1145/3201897

A. Polino, R. Pascanu, and D.-A. Alistarh, “Model compression via distillation and quantization,” in 6th International Conference on Learning Representations, Vancouver, Canada, 2018.
LibreCat: https://research-explorer.ista.ac.at/record/7812
arXiv: 1802.05668

M. Arbel Raviv and T. A. Brown, “Harnessing epoch-based reclamation for efficient range queries,” presented at the PPoPP: Principles and Practice of Parallel Programming, Vienna, Austria, 2018, vol. 53, no. 1, pp. 14–27.
LibreCat: https://research-explorer.ista.ac.at/record/397
DOI: 10.1145/3178487.3178489
WoS: 000446161100002

J. Rybicki, E. Kisdi, and J. Anttila, “Model of bacterial toxin-dependent pathogenesis explains infective dose,” PNAS, vol. 115, no. 42. National Academy of Sciences, pp. 10690–10695, 2018.
LibreCat: https://research-explorer.ista.ac.at/record/43
DOI: 10.1073/pnas.1721061115
WoS: 000447491300057

C. Lenzen and J. Rybicki, “Near-optimal self-stabilising counting and firing squads,” Distributed Computing. Springer, 2018.
LibreCat: https://research-explorer.ista.ac.at/record/76
DOI: 10.1007/s00446-018-0342-6
WoS: 000475627800005

E. Gilad, T. A. Brown, M. Oskin, and Y. Etsion, “Snapshot based synchronization: A fast replacement for Hand-over-Hand locking,” presented at the Euro-Par: European Conference on Parallel Processing, Turin, Italy, 2018, vol. 11014, pp. 465–479.
LibreCat: https://research-explorer.ista.ac.at/record/85
DOI: 10.1007/978-3-319-96983-1_33
WoS: 000851042300031

D.-A. Alistarh, C. De Sa, and N. H. Konstantinov, “The convergence of stochastic gradient descent in asynchronous shared memory,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 169–178.
LibreCat: https://research-explorer.ista.ac.at/record/5962
DOI: 10.1145/3212734.3212763
WoS: 000458186900022
arXiv: 1803.08841

D.-A. Alistarh, “A brief tutorial on distributed and concurrent machine learning,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 487–488.
LibreCat: https://research-explorer.ista.ac.at/record/5961
DOI: 10.1145/3212734.3212798
WoS: 000458186900063

D.-A. Alistarh, T. A. Brown, J. Kopinsky, and G. Nadiradze, “Relaxed schedulers can efficiently parallelize iterative algorithms,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 377–386.
LibreCat: https://research-explorer.ista.ac.at/record/5963
DOI: 10.1145/3212734.3212756
WoS: 000458186900048
arXiv: 1808.04155

D.-A. Alistarh, T. A. Brown, J. Kopinsky, J. Z. Li, and G. Nadiradze, “Distributionally linearizable data structures,” in Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, Vienna, Austria, 2018, pp. 133–142.
LibreCat: https://research-explorer.ista.ac.at/record/5965
DOI: 10.1145/3210377.3210411
WoS: 000545269600016
arXiv: 1804.01018

D.-A. Alistarh, S. K. Haider, R. Kübler, and G. Nadiradze, “The transactional conflict problem,” in Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, Vienna, Austria, 2018, pp. 383–392.
LibreCat: https://research-explorer.ista.ac.at/record/5966
DOI: 10.1145/3210377.3210406
WoS: 000545269600046
arXiv: 1804.00947

V. Aksenov, D.-A. Alistarh, and P. Kuznetsov, “Brief Announcement: Performance prediction for coarse-grained locking,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 411–413.
LibreCat: https://research-explorer.ista.ac.at/record/5964
DOI: 10.1145/3212734.3212785
WoS: 000458186900052

A. Stojanov, T. M. Smith, D.-A. Alistarh, and M. Puschel, “Fast quantized arithmetic on : Trading compute for data movement,” in 2018 IEEE International Workshop on Signal Processing Systems, Cape Town, South Africa, 2018, vol. 2018–October.
LibreCat: https://research-explorer.ista.ac.at/record/6031
DOI: 10.1109/SiPS.2018.8598402
WoS: 000465106800060

D.-A. Alistarh, J. Aspnes, and R. Gelashvili, “Space-optimal majority in population protocols,” in Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, United States, 2018, pp. 2221–2239.
LibreCat: https://research-explorer.ista.ac.at/record/7123
DOI: 10.1137/1.9781611975031.144
WoS: 000483921200145
arXiv: 1704.04947

D.-A. Alistarh, Z. Allen-Zhu, and J. Li, “Byzantine stochastic gradient descent,” in Advances in Neural Information Processing Systems, Montreal, Canada, 2018, vol. 2018, pp. 4613–4623.
LibreCat: https://research-explorer.ista.ac.at/record/6558
WoS: 000461823304061
arXiv: 1803.08917

D.-A. Alistarh, T. Hoefler, M. Johansson, N. H. Konstantinov, S. Khirirat, and C. Renggli, “The convergence of sparsified gradient methods,” in Advances in Neural Information Processing Systems 31, Montreal, Canada, 2018, vol. Volume 2018, pp. 5973–5983.
LibreCat: https://research-explorer.ista.ac.at/record/6589
WoS: 000461852000047
arXiv: 1809.10505

G. Baig, B. Radunovic, D.-A. Alistarh, M. Balkwill, T. Karagiannis, and L. Qiu, “Towards unlicensed cellular networks in TV white spaces,” in Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies, Incheon, South Korea, 2017, pp. 2–14.
LibreCat: https://research-explorer.ista.ac.at/record/487
DOI: 10.1145/3143361.3143367

D.-A. Alistarh, J. Kopinsky, J. Li, and G. Nadiradze, “The power of choice in priority scheduling,” in Proceedings of the ACM Symposium on Principles of Distributed Computing, Washington, WA, USA, 2017, vol. Part 9314, pp. 283–292.
LibreCat: https://research-explorer.ista.ac.at/record/791
DOI: 10.1145/3087801.3087810
WoS: 000462995000035

D.-A. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnovi, “QSGD: Communication-efficient SGD via gradient quantization and encoding,” presented at the NIPS: Neural Information Processing System, Long Beach, CA, United States, 2017, vol. 2017, pp. 1710–1721.
LibreCat: https://research-explorer.ista.ac.at/record/431
arXiv: 1610.02132