Mean-field analysis of piecewise linear solutions for wide ReLU networks
A. Shevchenko, V. Kungurtsev, M. Mondelli, Journal of Machine Learning Research 23 (2022) 1–55.
LibreCat: https://research-explorer.ista.ac.at/record/11420
arXiv: 2111.02278

Brief announcement: Temporal locality in online algorithms
M. Pacut, M. Parham, J. Rybicki, S. Schmid, J. Suomela, A. Tereshchenko, in:, 36th International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/12182
DOI: 10.4230/LIPIcs.DISC.2022.52

CGX: Adaptive system support for communication-efficient deep learning
I. Markov, H. Ramezanikebrya, D.-A. Alistarh, in:, Proceedings of the 23rd ACM/IFIP International Middleware Conference, Association for Computing Machinery, 2022, pp. 241–254.
LibreCat: https://research-explorer.ista.ac.at/record/12780
DOI: 10.1145/3528535.3565248
arXiv: 2111.08617

Near-optimal leader election in population protocols on graphs
D.-A. Alistarh, J. Rybicki, S. Voitovych, in:, Proceedings of the Annual ACM Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2022, pp. 246–256.
LibreCat: https://research-explorer.ista.ac.at/record/11844
DOI: 10.1145/3519270.3538435
arXiv: 2205.12597

PathCAS: An efficient middle ground for concurrent search data structures
T.A. Brown, W. Sigouin, D.-A. Alistarh, in:, Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2022, pp. 385–399.
LibreCat: https://research-explorer.ista.ac.at/record/11181
DOI: 10.1145/3503221.3508410
WoS: 000883318200027

Multi-queues can be state-of-the-art priority schedulers
A. Postnikova, N. Koval, G. Nadiradze, D.-A. Alistarh, in:, Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2022, pp. 353–367.
LibreCat: https://research-explorer.ista.ac.at/record/11180
DOI: 10.1145/3503221.3508432
WoS: 000883318200025
arXiv: 2109.00657

Multi-queues can be state-of-the-art priority schedulers
A. Postnikova, N. Koval, G. Nadiradze, D.-A. Alistarh, (2022).
LibreCat: https://research-explorer.ista.ac.at/record/13076
DOI: 10.5281/ZENODO.5733408

Local mending
A. Balliu, J. Hirvonen, D. Melnyk, D. Olivetti, J. Rybicki, J. Suomela, in:, M. Parter (Ed.), International Colloquium on Structural Information and Communication Complexity, Springer Nature, 2022, pp. 1–20.
LibreCat: https://research-explorer.ista.ac.at/record/11707
DOI: 10.1007/978-3-031-09993-9_1
WoS: 000876977400001
arXiv: 2102.08703

How well do sparse ImageNet models transfer?
E.B. Iofinova, E.-A. Peste, M. Kurtz, D.-A. Alistarh, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 12256–12266.
LibreCat: https://research-explorer.ista.ac.at/record/12299
DOI: 10.1109/cvpr52688.2022.01195
WoS: 000870759105034
arXiv: 2111.13445

Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
T. Hoefler, D.-A. Alistarh, T. Ben-Nun, N. Dryden, E.-A. Peste, Journal of Machine Learning Research 22 (2021) 1–124.
LibreCat: https://research-explorer.ista.ac.at/record/10180
arXiv: 2102.00554

Brief announcement: Fast graphical population protocols
D.-A. Alistarh, R. Gelashvili, J. Rybicki, in:, 35th International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10218
DOI: 10.4230/LIPIcs.DISC.2021.43
arXiv: 2102.08808

Lower bounds for shared-memory leader election under bounded write contention
D.-A. Alistarh, R. Gelashvili, G. Nadiradze, in:, 35th International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10217
DOI: 10.4230/LIPIcs.DISC.2021.4

Brief announcement: Non-blocking dynamic unbounded graphs with worst-case amortized bounds
B. Chatterjee, S. Peri, M. Sa, in:, 35th International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10216
DOI: 10.4230/LIPIcs.DISC.2021.52
arXiv: 2003.01697

Brief announcement: Sinkless orientation is hard also in the supported LOCAL model
J. Korhonen, A. Paz, J. Rybicki, S. Schmid, J. Suomela, in:, 35th International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10219
DOI: 10.4230/LIPIcs.DISC.2021.58
arXiv: 2108.02655

A scalable concurrent algorithm for dynamic connectivity
A. Fedorov, N. Koval, D.-A. Alistarh, in:, Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures, Association for Computing Machinery, 2021, pp. 208–220.
LibreCat: https://research-explorer.ista.ac.at/record/10853
DOI: 10.1145/3409964.3461810
arXiv: 2105.08098

Asynchronous optimization methods for efficient training of deep neural networks with guarantees
V. Kungurtsev, M. Egan, B. Chatterjee, D.-A. Alistarh, in:, 35th AAAI Conference on Artificial Intelligence, AAAI 2021, AAAI Press, 2021, pp. 8209–8216.
LibreCat: https://research-explorer.ista.ac.at/record/11436
arXiv: 1905.11845

Distributed principal component analysis with limited communication
F. Alimisis, P. Davies, B. Vandereycken, D.-A. Alistarh, in:, Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021, pp. 2823–2834.
LibreCat: https://research-explorer.ista.ac.at/record/11452
arXiv: 2110.14391

M-FAC: Efficient matrix-free approximations of second-order information
E. Frantar, E. Kurtic, D.-A. Alistarh, in:, 35th Conference on Neural Information Processing Systems, Curran Associates, 2021, pp. 14873–14886.
LibreCat: https://research-explorer.ista.ac.at/record/11463
arXiv: 2010.08222

Towards tight communication lower bounds for distributed optimisation
D.-A. Alistarh, J. Korhonen, in:, 35th Conference on Neural Information Processing Systems, Curran Associates, 2021, pp. 7254–7266.
LibreCat: https://research-explorer.ista.ac.at/record/11464
arXiv: 2010.08222

New bounds for distributed mean estimation and variance reduction
P. Davies, V. Gurunanthan, N. Moshrefi, S. Ashkboos, D.-A. Alistarh, in:, 9th International Conference on Learning Representations, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/9543
arXiv: 2002.09268

Collecting coupons is faster with friends
D.-A. Alistarh, P. Davies, in:, Structural Information and Communication Complexity, Springer Nature, 2021, pp. 3–12.
LibreCat: https://research-explorer.ista.ac.at/record/9620
DOI: 10.1007/978-3-030-79527-6_1

Wait-free approximate agreement on graphs
D.-A. Alistarh, F. Ellen, J. Rybicki, in:, Structural Information and Communication Complexity, Springer Nature, 2021, pp. 87–105.
LibreCat: https://research-explorer.ista.ac.at/record/9823
DOI: 10.1007/978-3-030-79527-6_6
arXiv: 2103.08949

AC/DC: Alternating Compressed/DeCompressed training of deep neural networks
E.-A. Peste, E.B. Iofinova, A. Vladu, D.-A. Alistarh, in:, 35th Conference on Neural Information Processing Systems, Curran Associates, 2021, pp. 8557–8570.
LibreCat: https://research-explorer.ista.ac.at/record/11458
arXiv: 2106.12379

Communication-efficient distributed optimization with quantized preconditioners
F. Alimisis, P. Davies, D.-A. Alistarh, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 196–206.
LibreCat: https://research-explorer.ista.ac.at/record/13147
arXiv: 2102.07214

Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging
S. Li, T.B.-N. Tal Ben-Nun, G. Nadiradze, S.D. Girolamo, N. Dryden, D.-A. Alistarh, T. Hoefler, IEEE Transactions on Parallel and Distributed Systems 32 (2021).
LibreCat: https://research-explorer.ista.ac.at/record/8723
DOI: 10.1109/TPDS.2020.3040606
WoS: 000621405200019
arXiv: 2005.00124

Concurrent linearizable nearest neighbour search in LockFree-kD-tree
B. Chatterjee, I. Walulya, P. Tsigas, Theoretical Computer Science 886 (2021) 27–48.
LibreCat: https://research-explorer.ista.ac.at/record/9827
DOI: 10.1016/j.tcs.2021.06.041
WoS: 000694718900004

Comparison dynamics in population protocols
D.-A. Alistarh, M. Töpfer, P. Uznaski, in:, Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2021, pp. 55–65.
LibreCat: https://research-explorer.ista.ac.at/record/9951
DOI: 10.1145/3465084.3467915
WoS: 000744439800005

Improved deterministic (+1) coloring in low-space MPC
A. Czumaj, P. Davies, M. Parter, in:, Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2021, pp. 469–479.
LibreCat: https://research-explorer.ista.ac.at/record/9935
DOI: 10.1145/3465084.3467937
WoS: 000744439800048

Component stability in low-space massively parallel computation
A. Czumaj, P. Davies, M. Parter, in:, Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2021, pp. 481–491.
LibreCat: https://research-explorer.ista.ac.at/record/9933
DOI: 10.1145/3465084.3467903
WoS: 000744439800049
arXiv: 2106.01880

Elastic consistency: A practical consistency model for distributed stochastic gradient descent
G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, D.-A. Alistarh, in:, Proceedings of the AAAI Conference on Artificial Intelligence, 2021, pp. 9037–9045.
LibreCat: https://research-explorer.ista.ac.at/record/10432
arXiv: 2001.05918

Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement
K. Klein, G. Pascual Perez, M. Walter, C. Kamath Hosdurg, M. Capretto, M. Cueto Noval, I. Markov, M.X. Yeo, J.F. Alwen, K.Z. Pietrzak, in:, 2021 IEEE Symposium on Security and Privacy , IEEE, 2021, pp. 268–284.
LibreCat: https://research-explorer.ista.ac.at/record/10049
DOI: 10.1109/sp40001.2021.00035

Input-dynamic distributed algorithms for communication networks
K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, S. Schmid, in:, Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, Association for Computing Machinery, 2021, pp. 71–72.
LibreCat: https://research-explorer.ista.ac.at/record/10854
DOI: 10.1145/3410220.3453923
arXiv: 2005.07637

Input-dynamic distributed algorithms for communication networks
K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, S. Schmid, Proceedings of the ACM on Measurement and Analysis of Computing Systems 5 (2021) 1–33.
LibreCat: https://research-explorer.ista.ac.at/record/10855
DOI: 10.1145/3447384
arXiv: 2005.07637

On achieving scalability through relaxation
G. Nadiradze, On Achieving Scalability through Relaxation, Institute of Science and Technology Austria, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10429
DOI: 10.15479/at:ista:10429

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.
LibreCat: https://research-explorer.ista.ac.at/record/10435
arXiv: 1910.12308

Graph sparsification for derandomizing massively parallel computation with low space
A. Czumaj, P. Davies, M. Parter, ACM Transactions on Algorithms 17 (2021).
LibreCat: https://research-explorer.ista.ac.at/record/9541
DOI: 10.1145/3451992
WoS: 000661311300006
arXiv: 1912.05390

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.
LibreCat: https://research-explorer.ista.ac.at/record/9678
DOI: 10.1145/3409964.3461785
arXiv: 2005.07761

Dynamic averaging load balancing on cycles
D.-A. Alistarh, G. Nadiradze, A. Sabour, Algorithmica (2021).
LibreCat: https://research-explorer.ista.ac.at/record/8286
DOI: 10.1007/s00453-021-00905-9
WoS: 000734004600001
arXiv: 2003.09297

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.
LibreCat: https://research-explorer.ista.ac.at/record/9571
arXiv: 1908.06077

Fast approximate shortest paths in the congested clique
K. Censor-Hillel, M. Dory, J. Korhonen, D. Leitersdorf, Distributed Computing 34 (2021) 463–487.
LibreCat: https://research-explorer.ista.ac.at/record/7939
DOI: 10.1007/s00446-020-00380-5
WoS: 000556444600001
arXiv: 1903.05956

Simple, deterministic, constant-round coloring in congested clique and MPC
A. Czumaj, P. Davies, M. Parter, SIAM Journal on Computing 50 (2021) 1603–1626.
LibreCat: https://research-explorer.ista.ac.at/record/15271
DOI: 10.1137/20m1366502

Exploiting spontaneous transmissions for broadcasting and leader election in radio networks
A. Czumaj, P. Davies, Journal of the ACM 68 (2021).
LibreCat: https://research-explorer.ista.ac.at/record/15267
DOI: 10.1145/3446383
arXiv: 1703.01859

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.
LibreCat: https://research-explorer.ista.ac.at/record/15263
arXiv: 2002.04144

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.
LibreCat: https://research-explorer.ista.ac.at/record/7272

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.
LibreCat: https://research-explorer.ista.ac.at/record/7605
DOI: 10.4230/LIPIcs.OPODIS.2019.15
arXiv: 1911.06347

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.
LibreCat: https://research-explorer.ista.ac.at/record/7803
DOI: 10.1145/3382734.3405751
arXiv: 2009.06043

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.
LibreCat: https://research-explorer.ista.ac.at/record/8725
DOI: 10.4230/LIPIcs.DISC.2020.3
arXiv: 2008.01009

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.
LibreCat: https://research-explorer.ista.ac.at/record/9632
arXiv: 2004.14340

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.
LibreCat: https://research-explorer.ista.ac.at/record/9631
arXiv: 2002.11505

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.
LibreCat: https://research-explorer.ista.ac.at/record/9415

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.
LibreCat: https://research-explorer.ista.ac.at/record/8268
DOI: 10.1109/TSP.2020.3010355
WoS: 000562044500001
arXiv: 1802.04907

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.
LibreCat: https://research-explorer.ista.ac.at/record/8722
DOI: 10.1145/3332466.3374528
WoS: 000564476500004
arXiv: 1908.04207

Habitat fragmentation and species diversity in competitive communities
J. Rybicki, N. Abrego, O. Ovaskainen, Ecology Letters 23 (2020) 506–517.
LibreCat: https://research-explorer.ista.ac.at/record/7224
DOI: 10.1111/ele.13450
WoS: 000503625200001

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.
LibreCat: https://research-explorer.ista.ac.at/record/8724
arXiv: 2002.10384

A persistent homology perspective to the link prediction problem
S. Bhatia, B. Chatterjee, D. Nathani, M. Kaul, in:, Complex Networks and Their Applications VIII, Springer Nature, 2020, pp. 27–39.
LibreCat: https://research-explorer.ista.ac.at/record/7213
DOI: 10.1007/978-3-030-36687-2_3
WoS: 000843927300003

Graph sparsification for derandomizing massively parallel computation with low space
A. Czumaj, P. Davies, M. Parter, in:, Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2020), Association for Computing Machinery, 2020, pp. 175–185.
LibreCat: https://research-explorer.ista.ac.at/record/7802
DOI: 10.1145/3350755.3400282
WoS: 000744436200015
arXiv: 1912.05390

Non-blocking interpolation search trees with doubly-logarithmic running time
T.A. Brown, A. Prokopec, D.-A. Alistarh, in:, Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2020, pp. 276–291.
LibreCat: https://research-explorer.ista.ac.at/record/7636
DOI: 10.1145/3332466.3374542
WoS: 000564476500020

Memory tagging: Minimalist synchronization for scalable concurrent data structures
D.-A. Alistarh, T.A. Brown, N. Singhal, in:, Annual ACM Symposium on Parallelism in Algorithms and Architectures, Association for Computing Machinery, 2020, pp. 37–49.
LibreCat: https://research-explorer.ista.ac.at/record/8191
DOI: 10.1145/3350755.3400213
WoS: 000744436200004

Testing concurrency on the JVM with Lincheck
N. Koval, M. Sokolova, A. Fedorov, D.-A. Alistarh, D. Tsitelov, in:, Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, Association for Computing Machinery, 2020, pp. 423–424.
LibreCat: https://research-explorer.ista.ac.at/record/7635
DOI: 10.1145/3332466.3374503

Brief Announcement: Why Extension-Based Proofs Fail
D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, L. Zhu, in:, Proceedings of the 39th Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2020, pp. 54–56.
LibreCat: https://research-explorer.ista.ac.at/record/8383
DOI: 10.1145/3382734.3405743

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.
LibreCat: https://research-explorer.ista.ac.at/record/15074
DOI: 10.4230/LIPIcs.DISC.2020.40
arXiv: 2005.07761

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.
LibreCat: https://research-explorer.ista.ac.at/record/15077
DOI: 10.4230/LIPIcs.ICALP.2020.7
arXiv: 2003.09297

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.
LibreCat: https://research-explorer.ista.ac.at/record/15086
arXiv: 2010.12460

On grounded L-graphs and their relatives
V. Jelínek, M. Töpfer, Electronic Journal of Combinatorics 26 (2019).
LibreCat: https://research-explorer.ista.ac.at/record/6759
DOI: 10.37236/8096
arXiv: 1808.04148

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.
LibreCat: https://research-explorer.ista.ac.at/record/6931
DOI: 10.4230/LIPICS.DISC.2019.29
arXiv: 1908.02743

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.
LibreCat: https://research-explorer.ista.ac.at/record/5947
DOI: 10.1145/3288599.3288617
WoS: 000484491600019
arXiv: 1809.00896

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.
LibreCat: https://research-explorer.ista.ac.at/record/6485
DOI: 10.1145/3293883.3297000
WoS: 000587604600044

What can observational data reveal about metacommunity processes?
O. Ovaskainen, J. Rybicki, N. Abrego, Ecography 42 (2019) 1877–1886.
LibreCat: https://research-explorer.ista.ac.at/record/6936
DOI: 10.1111/ecog.04444
WoS: 000486348700001

Self-stabilising Byzantine clock synchronisation is almost as easy as consensus
C. Lenzen, J. Rybicki, Journal of the ACM 66 (2019).
LibreCat: https://research-explorer.ista.ac.at/record/6972
DOI: 10.1145/3339471
WoS: 000496514100001
arXiv: 1705.06173

Gradient compression for communication-limited convex optimization
S. Khirirat, M. Johansson, D.-A. Alistarh, in:, 2018 IEEE Conference on Decision and Control, IEEE, 2019.
LibreCat: https://research-explorer.ista.ac.at/record/7122
DOI: 10.1109/cdc.2018.8619625
WoS: 000458114800023

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.
LibreCat: https://research-explorer.ista.ac.at/record/7201
DOI: 10.1145/3295500.3356222
WoS: 000545976800011
arXiv: 1802.08021

Recovering rearranged cancer chromosomes from karyotype graphs
S. Aganezov, I. Zban, V. Aksenov, N. Alexeev, M.C. Schatz, BMC Bioinformatics 20 (2019).
LibreCat: https://research-explorer.ista.ac.at/record/7214
DOI: 10.1186/s12859-019-3208-4
WoS: 000511618800007

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.
LibreCat: https://research-explorer.ista.ac.at/record/7228
DOI: 10.1007/978-3-030-29400-7_23
WoS: 000851061400023

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.
LibreCat: https://research-explorer.ista.ac.at/record/7437
WoS: 000684034307036
arXiv: 1810.07766

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.
LibreCat: https://research-explorer.ista.ac.at/record/6673
DOI: 10.1145/3323165.3323201
WoS: 000507618500018
arXiv: 2003.09363

Powerset convolutional neural networks
C. Wendler, D.-A. Alistarh, M. Püschel, in:, Neural Information Processing Systems Foundation, 2019, pp. 927–938.
LibreCat: https://research-explorer.ista.ac.at/record/7542
WoS: 000534424300084
arXiv: 1909.02253

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.
LibreCat: https://research-explorer.ista.ac.at/record/6935
DOI: 10.1145/3293611.3331581
WoS: 000570442000037
arXiv: 1905.03012

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.
LibreCat: https://research-explorer.ista.ac.at/record/6676
DOI: 10.1145/3313276.3316407
WoS: 000523199100089
arXiv: 1811.01421

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.
LibreCat: https://research-explorer.ista.ac.at/record/6933
DOI: 10.1145/3293611.3331633
WoS: 000570442000011
arXiv: 1903.05956

Communication-efficient randomized consensus
D.-A. Alistarh, J. Aspnes, V. King, J. Saia, Distributed Computing 31 (2018) 489–501.
LibreCat: https://research-explorer.ista.ac.at/record/536
DOI: 10.1007/s00446-017-0315-1

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.
LibreCat: https://research-explorer.ista.ac.at/record/7116
DOI: 10.5441/002/EDBT.2018.14

ThreadScan: Automatic and scalable memory reclamation
D.-A. Alistarh, W. Leiserson, A. Matveev, N. Shavit, ACM Transactions on Parallel Computing 4 (2018).
LibreCat: https://research-explorer.ista.ac.at/record/6001
DOI: 10.1145/3201897

Model compression via distillation and quantization
A. Polino, R. Pascanu, D.-A. Alistarh, in:, 6th International Conference on Learning Representations, 2018.
LibreCat: https://research-explorer.ista.ac.at/record/7812
arXiv: 1802.05668

Harnessing epoch-based reclamation for efficient range queries
M. Arbel Raviv, T.A. Brown, in:, ACM, 2018, pp. 14–27.
LibreCat: https://research-explorer.ista.ac.at/record/397
DOI: 10.1145/3178487.3178489
WoS: 000446161100002

Model of bacterial toxin-dependent pathogenesis explains infective dose
J. Rybicki, E. Kisdi, J. Anttila, PNAS 115 (2018) 10690–10695.
LibreCat: https://research-explorer.ista.ac.at/record/43
DOI: 10.1073/pnas.1721061115
WoS: 000447491300057

Near-optimal self-stabilising counting and firing squads
C. Lenzen, J. Rybicki, Distributed Computing (2018).
LibreCat: https://research-explorer.ista.ac.at/record/76
DOI: 10.1007/s00446-018-0342-6
WoS: 000475627800005

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.
LibreCat: https://research-explorer.ista.ac.at/record/85
DOI: 10.1007/978-3-319-96983-1_33
WoS: 000851042300031

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

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

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

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.
LibreCat: https://research-explorer.ista.ac.at/record/5965
DOI: 10.1145/3210377.3210411
WoS: 000545269600016
arXiv: 1804.01018

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.
LibreCat: https://research-explorer.ista.ac.at/record/5966
DOI: 10.1145/3210377.3210406
WoS: 000545269600046
arXiv: 1804.00947

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.
LibreCat: https://research-explorer.ista.ac.at/record/5964
DOI: 10.1145/3212734.3212785
WoS: 000458186900052

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.
LibreCat: https://research-explorer.ista.ac.at/record/6031
DOI: 10.1109/SiPS.2018.8598402
WoS: 000465106800060

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.
LibreCat: https://research-explorer.ista.ac.at/record/7123
DOI: 10.1137/1.9781611975031.144
WoS: 000483921200145
arXiv: 1704.04947

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.
LibreCat: https://research-explorer.ista.ac.at/record/6558
WoS: 000461823304061
arXiv: 1803.08917

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.
LibreCat: https://research-explorer.ista.ac.at/record/6589
WoS: 000461852000047
arXiv: 1809.10505

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.
LibreCat: https://research-explorer.ista.ac.at/record/487
DOI: 10.1145/3143361.3143367

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.
LibreCat: https://research-explorer.ista.ac.at/record/791
DOI: 10.1145/3087801.3087810
WoS: 000462995000035

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.
LibreCat: https://research-explorer.ista.ac.at/record/431
arXiv: 1610.02132