Brief announcement: Efficient load-balancing through distributed token dropping
S. Brandt, B. Keller, J. Rybicki, J. Suomela, J. Uitto, in:, 34th International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.

Dynamic averaging load balancing on cycles
D.-A. Alistarh, G. Nadiradze, A. Sabour, in:, 47th International Colloquium on Automata, Languages, and Programming, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.

Adaptive gradient quantization for data-parallel SGD
F. Faghri, I. Tabrizian, I. Markov, D.-A. Alistarh, D. Roy, A. Ramezani-Kebrya, in:, Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2020.

On grounded L-graphs and their relatives
V. Jelínek, M. Töpfer, Electronic Journal of Combinatorics 26 (2019).

Byzantine approximate agreement on graphs
T. Nowak, J. Rybicki, in:, 33rd International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019, p. 29:1--29:17.

A simple and practical concurrent non-blocking unbounded graph with linearizable reachability queries
B. Chatterjee, S. Peri, M. Sa, N. Singhal, in:, ACM International Conference Proceeding Series, ACM, 2019, pp. 168–177.

Lock-free channels for programming via communicating sequential processes
N. Koval, D.-A. Alistarh, R. Elizarov, Lock-Free Channels for Programming via Communicating Sequential Processes, ACM Press, 2019.

What can observational data reveal about metacommunity processes?
O. Ovaskainen, J. Rybicki, N. Abrego, Ecography 42 (2019) 1877–1886.

Self-stabilising Byzantine clock synchronisation is almost as easy as consensus
C. Lenzen, J. Rybicki, Journal of the ACM 66 (2019).

Gradient compression for communication-limited convex optimization
S. Khirirat, M. Johansson, D.-A. Alistarh, in:, 2018 IEEE Conference on Decision and Control, IEEE, 2019.

SparCML: High-performance sparse communication for machine learning
C. Renggli, S. Ashkboos, M. Aghagolzadeh, D.-A. Alistarh, T. Hoefler, in:, International Conference for High Performance Computing, Networking, Storage and Analysis, SC, ACM, 2019.

Recovering rearranged cancer chromosomes from karyotype graphs
S. Aganezov, I. Zban, V. Aksenov, N. Alexeev, M.C. Schatz, BMC Bioinformatics 20 (2019).

Scalable FIFO channels for programming via communicating sequential processes
N. Koval, D.-A. Alistarh, R. Elizarov, in:, 25th Anniversary of Euro-Par, Springer Nature, 2019, pp. 317–333.

Distributed learning over unreliable networks
C. Yu, H. Tang, C. Renggli, S. Kassing, A. Singla, D.-A. Alistarh, C. Zhang, J. Liu, in:, 36th International Conference on Machine Learning, ICML 2019, IMLS, 2019, pp. 12481–12512.

Efficiency guarantees for parallel incremental algorithms under relaxed schedulers
D.-A. Alistarh, G. Nadiradze, N. Koval, in:, 31st ACM Symposium on Parallelism in Algorithms and Architectures, ACM Press, 2019, pp. 145–154.

Powerset convolutional neural networks
C. Wendler, D.-A. Alistarh, M. Püschel, in:, Neural Information Processing Systems Foundation, 2019, pp. 927–938.

Does preprocessing help under congestion?
K.-T. Foerster, J. Korhonen, J. Rybicki, S. Schmid, in:, Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing, ACM, 2019, pp. 259–261.

Why extension-based proofs fail
D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, L. Zhu, in:, Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, ACM Press, 2019, pp. 986–996.

Fast approximate shortest paths in the congested clique
K. Censor-Hillel, M. Dory, J. Korhonen, D. Leitersdorf, in:, Proceedings of the 2019 ACM Symposium on Principles of Distributed Computin, ACM, 2019, pp. 74–83.

Communication-efficient randomized consensus
D.-A. Alistarh, J. Aspnes, V. King, J. Saia, Distributed Computing 31 (2018) 489–501.

Synchronous multi-GPU training for deep learning with low-precision communications: An empirical study
D. Grubic, L. Tam, D.-A. Alistarh, C. Zhang, in:, Proceedings of the 21st International Conference on Extending Database Technology, OpenProceedings, 2018, pp. 145–156.

ThreadScan: Automatic and scalable memory reclamation
D.-A. Alistarh, W. Leiserson, A. Matveev, N. Shavit, ACM Transactions on Parallel Computing 4 (2018).

Model compression via distillation and quantization
A. Polino, R. Pascanu, D.-A. Alistarh, in:, 6th International Conference on Learning Representations, 2018.

Harnessing epoch-based reclamation for efficient range queries
M. Arbel Raviv, T.A. Brown, in:, ACM, 2018, pp. 14–27.

Model of bacterial toxin-dependent pathogenesis explains infective dose
J. Rybicki, E. Kisdi, J. Anttila, PNAS 115 (2018) 10690–10695.

Near-optimal self-stabilising counting and firing squads
C. Lenzen, J. Rybicki, Distributed Computing (2018).

Snapshot based synchronization: A fast replacement for Hand-over-Hand locking
E. Gilad, T.A. Brown, M. Oskin, Y. Etsion, in:, Springer, 2018, pp. 465–479.

The convergence of stochastic gradient descent in asynchronous shared memory
D.-A. Alistarh, C. De Sa, N.H. Konstantinov, in:, Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, ACM Press, 2018, pp. 169–178.

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.