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174 Publications
2019 |
Published |
Conference Paper |
IST-REx-ID: 6931 |
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
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| arXiv
earlier version | 2019 |
Published |
Conference Paper |
IST-REx-ID: 6933 |
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
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| arXiv
2019 |
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Conference Paper |
IST-REx-ID: 6935 |
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
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| arXiv
2019 |
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Journal Article |
IST-REx-ID: 6936 |
Ovaskainen, O., Rybicki, J., & Abrego, N. (2019). What can observational data reveal about metacommunity processes? Ecography. Wiley. https://doi.org/10.1111/ecog.04444
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2019 |
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Journal Article |
IST-REx-ID: 6972 |
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
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| arXiv
2019 |
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Conference Paper |
IST-REx-ID: 7122
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
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2019 |
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Conference Paper |
IST-REx-ID: 7201 |
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
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| arXiv
2019 |
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Journal Article |
IST-REx-ID: 7214 |
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
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2019 |
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Conference Paper |
IST-REx-ID: 7228
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
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2019 |
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Conference Paper |
IST-REx-ID: 7542 |
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.
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| arXiv
2019 |
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Conference Paper |
IST-REx-ID: 7437 |
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 (Vol. 2019–June, pp. 12481–12512). Long Beach, CA, United States: IMLS.
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| arXiv
2019 |
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Conference Paper |
IST-REx-ID: 5947 |
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
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| arXiv
2018 |
Published |
Journal Article |
IST-REx-ID: 536 |
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
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2018 |
Published |
Conference Paper |
IST-REx-ID: 5961
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. https://doi.org/10.1145/3212734.3212798
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2018 |
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Conference Paper |
IST-REx-ID: 5962 |
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. https://doi.org/10.1145/3212734.3212763
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| arXiv
2018 |
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Conference Paper |
IST-REx-ID: 5963 |
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. https://doi.org/10.1145/3212734.3212756
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| arXiv
2018 |
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Conference Paper |
IST-REx-ID: 5964 |
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. https://doi.org/10.1145/3212734.3212785
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2018 |
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Conference Paper |
IST-REx-ID: 5965 |
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. https://doi.org/10.1145/3210377.3210411
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| arXiv
2018 |
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Conference Paper |
IST-REx-ID: 5966 |
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. https://doi.org/10.1145/3210377.3210406
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| arXiv
2018 |
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Journal Article |
IST-REx-ID: 6001
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
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