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Alistarh, Dan-Adrian, et al. “Communication-Efficient Randomized Consensus.”10.1007/s00446-017-0315-1__Distributed Computing, vol. 31, no. 6, Springer, 2018, pp. 489–501, doi:10.1007/s00446-017-0315-1.
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Alistarh, Dan-Adrian, et al. “ThreadScan: Automatic and Scalable Memory Reclamation.”10.1145/3201897__ACM Transactions on Parallel Computing, vol. 4, no. 4, 18, Association for Computing Machinery, 2018, doi:10.1145/3201897.
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Rybicki, Joel, et al. “Model of Bacterial Toxin-Dependent Pathogenesis Explains Infective Dose.”10.1073/pnas.1721061115__PNAS, vol. 115, no. 42, National Academy of Sciences, 2018, pp. 10690–95, doi:10.1073/pnas.1721061115.
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Alistarh, Dan-Adrian, et al. “The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory.”10.1145/3212734.3212763__Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, ACM Press, 2018, pp. 169–78, doi:10.1145/3212734.3212763.
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Alistarh, Dan-Adrian. “A Brief Tutorial on Distributed and Concurrent Machine Learning.”10.1145/3212734.3212798__Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, ACM Press, 2018, pp. 487–88, doi:10.1145/3212734.3212798.
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Alistarh, Dan-Adrian, et al. “Relaxed Schedulers Can Efficiently Parallelize Iterative Algorithms.”10.1145/3212734.3212756__Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, ACM Press, 2018, pp. 377–86, doi:10.1145/3212734.3212756.
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Alistarh, Dan-Adrian, et al. “Distributionally Linearizable Data Structures.”10.1145/3210377.3210411__Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, ACM Press, 2018, pp. 133–42, doi:10.1145/3210377.3210411.
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Alistarh, Dan-Adrian, et al. “The Transactional Conflict Problem.”10.1145/3210377.3210406__Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, ACM Press, 2018, pp. 383–92, doi:10.1145/3210377.3210406.
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Aksenov, Vitaly, et al. “Brief Announcement: Performance Prediction for Coarse-Grained Locking.”10.1145/3212734.3212785__Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, ACM Press, 2018, pp. 411–13, doi:10.1145/3212734.3212785.
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Stojanov, Alen, et al. “Fast Quantized Arithmetic on : Trading Compute for Data Movement.”10.1109/SiPS.2018.8598402__2018 IEEE International Workshop on Signal Processing Systems, vol. 2018–October, 8598402, IEEE, 2018, doi:10.1109/SiPS.2018.8598402.
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Alistarh, Dan-Adrian, et al. “Space-Optimal Majority in Population Protocols.”10.1137/1.9781611975031.144__Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, ACM, 2018, pp. 2221–39, doi:10.1137/1.9781611975031.144.
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Alistarh, Dan-Adrian, et al. “Byzantine Stochastic Gradient Descent.” Advances in Neural Information Processing Systems, vol. 2018, Neural Information Processing Systems Foundation, 2018, pp. 4613–23.
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Alistarh, Dan-Adrian, et al. “The Convergence of Sparsified Gradient Methods.” Advances in Neural Information Processing Systems 31, vol. Volume 2018, Neural Information Processing Systems Foundation, 2018, pp. 5973–83.
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Baig, Ghufran, et al. “Towards Unlicensed Cellular Networks in TV White Spaces.”10.1145/3143361.3143367__Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies, ACM, 2017, pp. 2–14, doi:10.1145/3143361.3143367.
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