Alistarh, Dan-Adrian. “A Brief Tutorial on Distributed and Concurrent Machine Learning.”https://doi.org/10.1145/3212734.3212798__In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 487–88. ACM Press, 2018.https://doi.org/10.1145/3212734.3212798 .

Alistarh, Dan-Adrian, Trevor A Brown, Justin Kopinsky, and Giorgi Nadiradze. “Relaxed Schedulers Can Efficiently Parallelize Iterative Algorithms.”https://doi.org/10.1145/3212734.3212756__In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 377–86. ACM Press, 2018.https://doi.org/10.1145/3212734.3212756 .

Alistarh, Dan-Adrian, Trevor A Brown, Justin Kopinsky, Jerry Z. Li, and Giorgi Nadiradze. “Distributionally Linearizable Data Structures.”https://doi.org/10.1145/3210377.3210411__In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, 133–42. ACM Press, 2018.https://doi.org/10.1145/3210377.3210411 .

Alistarh, Dan-Adrian, Syed Kamran Haider, Raphael Kübler, and Giorgi Nadiradze. “The Transactional Conflict Problem.”https://doi.org/10.1145/3210377.3210406__In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, 383–92. ACM Press, 2018.https://doi.org/10.1145/3210377.3210406 .

Aksenov, Vitaly, Dan-Adrian Alistarh, and Petr Kuznetsov. “Brief Announcement: Performance Prediction for Coarse-Grained Locking.”https://doi.org/10.1145/3212734.3212785__In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 411–13. ACM Press, 2018.https://doi.org/10.1145/3212734.3212785 .

Stojanov, Alen, Tyler Michael Smith, Dan-Adrian Alistarh, and Markus Puschel. “Fast Quantized Arithmetic on : Trading Compute for Data Movement.”https://doi.org/10.1109/SiPS.2018.8598402__In 2018 IEEE International Workshop on Signal Processing Systems, Vol. 2018–October. IEEE, 2018.https://doi.org/10.1109/SiPS.2018.8598402 .

Alistarh, Dan-Adrian, James Aspnes, and Rati Gelashvili. “Space-Optimal Majority in Population Protocols.”https://doi.org/10.1137/1.9781611975031.144__In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, 2221–39. ACM, 2018.https://doi.org/10.1137/1.9781611975031.144 .

Alistarh, Dan-Adrian, Zeyuan Allen-Zhu, and Jerry Li. “Byzantine Stochastic Gradient Descent.” In Advances in Neural Information Processing Systems, 2018:4613–23. Neural Information Processing Systems Foundation, 2018.

Alistarh, Dan-Adrian, Torsten Hoefler, Mikael Johansson, Nikola H Konstantinov, Sarit Khirirat, and Cedric Renggli. “The Convergence of Sparsified Gradient Methods.” In Advances in Neural Information Processing Systems 31, Volume 2018:5973–83. Neural Information Processing Systems Foundation, 2018.

Baig, Ghufran, Bozidar Radunovic, Dan-Adrian Alistarh, Matthew Balkwill, Thomas Karagiannis, and Lili Qiu. “Towards Unlicensed Cellular Networks in TV White Spaces.”https://doi.org/10.1145/3143361.3143367__In Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies, 2–14. ACM, 2017.https://doi.org/10.1145/3143361.3143367 .

Alistarh, Dan-Adrian, Justin Kopinsky, Jerry Li, and Giorgi Nadiradze. “The Power of Choice in Priority Scheduling.”https://doi.org/10.1145/3087801.3087810__In Proceedings of the ACM Symposium on Principles of Distributed Computing, Part 9314:283–92. ACM, 2017.https://doi.org/10.1145/3087801.3087810 .

Alistarh, Dan-Adrian, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovi. “QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding,” 2017:1710–21. Neural Information Processing Systems Foundation, 2017.

Zhang, Hantian, Jerry Li, Kaan Kara, Dan-Adrian Alistarh, Ji Liu, and Ce Zhang. “ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning.” In Proceedings of Machine Learning Research, 70:4035–43. ML Research Press, 2017.