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 
LibreCat: https://research-explorer.ista.ac.at/record/5962
DOI: 10.1145/3212734.3212763
WoS: 000458186900022
arXiv: 1803.08841

Corominas-Murtra, B., Seoane, L. F., &; Solé, R. (2018). Zipf’s Law, unbounded complexity and open-ended evolution. Journal of the Royal Society Interface. Royal Society Publishing.https://doi.org/10.1098/rsif.2018.0395 
LibreCat: https://research-explorer.ista.ac.at/record/5860
DOI: 10.1098/rsif.2018.0395
WoS: 000456783800002
arXiv: 1612.01605

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 
LibreCat: https://research-explorer.ista.ac.at/record/5961
DOI: 10.1145/3212734.3212798
WoS: 000458186900063

Rohou, S., Franek, P., Aubry, C., &; Jaulin, L. (2018). Proving the existence of loops in robot trajectories. The International Journal of Robotics Research. SAGE Publications.https://doi.org/10.1177/0278364918808367 
LibreCat: https://research-explorer.ista.ac.at/record/5960
DOI: 10.1177/0278364918808367
WoS: 000456881100004
arXiv: 1712.01341

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 
LibreCat: https://research-explorer.ista.ac.at/record/5963
DOI: 10.1145/3212734.3212756
WoS: 000458186900048
arXiv: 1808.04155