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 Press.https://doi.org/10.1145/3212734.3212763 

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 Press.https://doi.org/10.1145/3212734.3212798 

Alistarh, D.-A., Brown, T. A., Kopinsky, J., &; Nadiradze, G. (2018). Relaxed schedulers can efficiently parallelize iterative algorithms. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 377–386). Egham, United Kingdom: ACM Press.https://doi.org/10.1145/3212734.3212756 

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 Press.https://doi.org/10.1145/3212734.3212785