A brief tutorial on distributed and concurrent machine learning
D.-A. Alistarh, in:, Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, ACM Press, 2018, pp. 487–488.

Relaxed schedulers can efficiently parallelize iterative algorithms
D.-A. Alistarh, T.A. Brown, J. Kopinsky, G. Nadiradze, in:, Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, ACM Press, 2018, pp. 377–386.

Distributionally linearizable data structures
D.-A. Alistarh, T.A. Brown, J. Kopinsky, J.Z. Li, G. Nadiradze, in:, Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, ACM Press, 2018, pp. 133–142.

The transactional conflict problem
D.-A. Alistarh, S.K. Haider, R. Kübler, G. Nadiradze, in:, Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, ACM Press, 2018, pp. 383–392.

Brief Announcement: Performance prediction for coarse-grained locking
V. Aksenov, D.-A. Alistarh, P. Kuznetsov, in:, Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, ACM Press, 2018, pp. 411–413.

Fast quantized arithmetic on : Trading compute for data movement
A. Stojanov, T.M. Smith, D.-A. Alistarh, M. Puschel, in:, 2018 IEEE International Workshop on Signal Processing Systems, IEEE, 2018.

Space-optimal majority in population protocols
D.-A. Alistarh, J. Aspnes, R. Gelashvili, in:, Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, ACM, 2018, pp. 2221–2239.

Byzantine stochastic gradient descent
D.-A. Alistarh, Z. Allen-Zhu, J. Li, in:, Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2018, pp. 4613–4623.

The convergence of sparsified gradient methods
D.-A. Alistarh, T. Hoefler, M. Johansson, N.H. Konstantinov, S. Khirirat, C. Renggli, in:, Advances in Neural Information Processing Systems 31, Neural Information Processing Systems Foundation, 2018, pp. 5973–5983.

Towards unlicensed cellular networks in TV white spaces
G. Baig, B. Radunovic, D.-A. Alistarh, M. Balkwill, T. Karagiannis, L. Qiu, in:, Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies, ACM, 2017, pp. 2–14.

The power of choice in priority scheduling
D.-A. Alistarh, J. Kopinsky, J. Li, G. Nadiradze, in:, Proceedings of the ACM Symposium on Principles of Distributed Computing, ACM, 2017, pp. 283–292.

QSGD: Communication-efficient SGD via gradient quantization and encoding
D.-A. Alistarh, D. Grubic, J. Li, R. Tomioka, M. Vojnovi, in:, Neural Information Processing Systems Foundation, 2017, pp. 1710–1721.

ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning
H. Zhang, J. Li, K. Kara, D.-A. Alistarh, J. Liu, C. Zhang, in:, Proceedings of Machine Learning Research, ML Research Press, 2017, pp. 4035–4043.