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151 Publications
2018 | Published | 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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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 | Published | 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 | Published | 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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2018 | Published | 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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2018 | Published | Conference Paper | IST-REx-ID: 6589 |
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.
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2018 | Published | Conference Paper | IST-REx-ID: 7812 |
Polino, A., Pascanu, R., & Alistarh, D.-A. (2018). Model compression via distillation and quantization. In 6th International Conference on Learning Representations. Vancouver, Canada.
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2017 | Published | Conference Paper | IST-REx-ID: 487
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
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2017 | Published | Conference Paper | IST-REx-ID: 432 |
Zhang, Hantian, ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning. Proceedings of Machine Learning Research 70. 2017
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2017 | Published | Conference Paper | IST-REx-ID: 431 |
Alistarh, Dan-Adrian, QSGD: Communication-efficient SGD via gradient quantization and encoding. 2017. 2017
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2017 | Published | Conference Paper | IST-REx-ID: 791 |
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 F129314, pp. 283–292). Washington, WA, USA: ACM. https://doi.org/10.1145/3087801.3087810
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