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151 Publications
2020 | Published | Conference Paper | IST-REx-ID: 7636 | 
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
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2020 | Published | Conference Paper | IST-REx-ID: 15086 |
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.
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2020 | Published | Conference Paper | IST-REx-ID: 15077 |
Alistarh, Dan-Adrian, Dynamic averaging load balancing on cycles. 47th International Colloquium on Automata, Languages, and Programming 168. 2020
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2020 | Published | Conference Paper | IST-REx-ID: 7213 |
Bhatia, Sumit, A persistent homology perspective to the link prediction problem. Complex Networks and their applications VIII 881. 2020
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2020 | Published | Conference Paper | IST-REx-ID: 7803 |
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
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| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 7802 |
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
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| arXiv
2020 | Published | Conference Paper | IST-REx-ID: 7272 |
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.
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2020 | Published | Conference Paper | IST-REx-ID: 9632 |
Singh, S. P., & Alistarh, D.-A. (2020). WoodFisher: Efficient second-order approximation for neural network compression (Vol. 33, pp. 18098–18109). Presented at the NeurIPS: Conference on Neural Information Processing Systems, Vancouver, Canada: Neural Information Processing Systems Foundation.
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2020 | Published | Conference Paper | IST-REx-ID: 9631 |
Aksenov, V., Alistarh, D.-A., & Korhonen, J. (2020). Scalable belief propagation via relaxed scheduling (Vol. 33, pp. 22361–22372). Presented at the NeurIPS: Conference on Neural Information Processing Systems, Vancouver, Canada: Neural Information Processing Systems Foundation.
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2020 | Published | Journal Article | IST-REx-ID: 7224 |
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
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| PubMed | Europe PMC
2020 | Published | Conference Paper | IST-REx-ID: 7635 |
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 (pp. 423–424). San Diego, CA, United States: Association for Computing Machinery. https://doi.org/10.1145/3332466.3374503
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2020 | Published | Conference Paper | IST-REx-ID: 8724 |
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.
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2020 | Published | Conference Paper | IST-REx-ID: 9198 |
Shevchenko, A., & Mondelli, M. (2020). Landscape connectivity and dropout stability of SGD solutions for over-parameterized neural networks. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 8773–8784). ML Research Press.
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2019 | Published | 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
2019 | Published | 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, ICML 2019 (Vol. 2019–June, pp. 12481–12512). Long Beach, CA, United States: IMLS.
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| arXiv
2019 | Published | 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 | Published | 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 | Published | 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 | Published | Conference Poster | IST-REx-ID: 6485
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. https://doi.org/10.1145/3293883.3297000
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2019 | Published | 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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