On achieving scalability through relaxation
G. Nadiradze, On Achieving Scalability through Relaxation, Institute of Science and Technology Austria, 2021.

Asynchronous decentralized SGD with quantized and local updates
G. Nadiradze, A. Sabour, P. Davies, S. Li, D.-A. Alistarh, in:, 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021.

Graph sparsification for derandomizing massively parallel computation with low space
A. Czumaj, P. Davies, M. Parter, ACM Transactions on Algorithms 17 (2021).

Efficient load-balancing through distributed token dropping
S. Brandt, B. Keller, J. Rybicki, J. Suomela, J. Uitto, in:, Annual ACM Symposium on Parallelism in Algorithms and Architectures, 2021, pp. 129–139.

NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization
A. Ramezani-Kebrya, F. Faghri, I. Markov, V. Aksenov, D.-A. Alistarh, D.M. Roy, Journal of Machine Learning Research 22 (2021) 143.

Fast approximate shortest paths in the congested clique
K. Censor-Hillel, M. Dory, J. Korhonen, D. Leitersdorf, Distributed Computing 34 (2021) 463–487.

Simple, deterministic, constant-round coloring in congested clique and MPC
A. Czumaj, P. Davies, M. Parter, SIAM Journal on Computing 50 (2021) 1603–1626.

Exploiting spontaneous transmissions for broadcasting and leader election in radio networks
A. Czumaj, P. Davies, Journal of the ACM 68 (2021).

Momentum improves optimization on Riemannian manifolds
F. Alimisis, A. Orvieto, G. Becigneul, A. Lucchi, in:, Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2021, pp. 1351–1359.

Getting to the root of concurrent binary search tree performance
M. Arbel-Raviv, T.A. Brown, A. Morrison, in:, Proceedings of the 2018 USENIX Annual Technical Conference, USENIX Association, 2020, pp. 295–306.

In search of the fastest concurrent union-find algorithm
D.-A. Alistarh, A. Fedorov, N. Koval, in:, 23rd International Conference on Principles of Distributed Systems, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020, p. 15:1-15:16.

Simple, deterministic, constant-round coloring in the congested clique
A. Czumaj, P. Davies, M. Parter, in:, Proceedings of the 2020 ACM Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2020, pp. 309–318.

The splay-list: A distribution-adaptive concurrent skip-list
V. Aksenov, D.-A. Alistarh, A. Drozdova, A. Mohtashami, in:, 34th International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020, p. 3:1-3:18.

WoodFisher: Efficient second-order approximation for neural network compression
S.P. Singh, D.-A. Alistarh, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 18098–18109.

Scalable belief propagation via relaxed scheduling
V. Aksenov, D.-A. Alistarh, J. Korhonen, in:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 22361–22372.

Inducing and exploiting activation sparsity for fast neural network inference
M. Kurtz, J. Kopinsky, R. Gelashvili, A. Matveev, J. Carr, M. Goin, W. Leiserson, S. Moore, B. Nell, N. Shavit, D.-A. Alistarh, in:, 37th International Conference on Machine Learning, ICML 2020, 2020, pp. 5533–5543.

Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications
N.M. Gurel, K. Kara, A. Stojanov, T. Smith, T. Lemmin, D.-A. Alistarh, M. Puschel, C. Zhang, IEEE Transactions on Signal Processing 68 (2020) 4268–4282.

Taming unbalanced training workloads in deep learning with partial collective operations
S. Li, T.B.-N. Tal Ben-Nun, S.D. Girolamo, D.-A. Alistarh, T. Hoefler, in:, Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2020, pp. 45–61.

Habitat fragmentation and species diversity in competitive communities
J. Rybicki, N. Abrego, O. Ovaskainen, Ecology Letters 23 (2020) 506–517.

On the sample complexity of adversarial multi-source PAC learning
N.H. Konstantinov, E. Frantar, D.-A. Alistarh, C. Lampert, in:, Proceedings of the 37th International Conference on Machine Learning, ML Research Press, 2020, pp. 5416–5425.