Kurtic E, Hoefler T, Alistarh D-A. 2024. How to prune your language model: Recovering accuracy on the ‘Sparsity May Cry’ benchmark. Proceedings of Machine Learning Research. CPAL: Conference on Parsimony and Learning, PMLR, vol. 234, 542–553.
LibreCat: https://research-explorer.ista.ac.at/record/15011
arXiv: 2312.13547

Koval N, Alistarh D-A, Elizarov R. 2023. Fast and scalable channels in Kotlin Coroutines. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. PPoPP: Sympopsium on Principles and Practice of Parallel Programming, 107–118.
LibreCat: https://research-explorer.ista.ac.at/record/12735
DOI: 10.1145/3572848.3577481
arXiv: 2211.04986

Aksenov V, Brown TA, Fedorov A, Kokorin I. 2023. Unexpected scaling in path copying trees, Association for Computing Machinery,p.
LibreCat: https://research-explorer.ista.ac.at/record/12736
DOI: 10.1145/3572848.3577512

Peste E-A, Vladu A, Kurtic E, Lampert C, Alistarh D-A. CrAM: A Compression-Aware Minimizer. 11th International Conference on Learning Representations . ICLR: International Conference on Learning Representations.
LibreCat: https://research-explorer.ista.ac.at/record/13053
arXiv: 2207.14200

Koval N, Khalanskiy D, Alistarh D-A. 2023. CQS: A formally-verified framework for fair and abortable synchronization. Proceedings of the ACM on Programming Languages. 7, 116.
LibreCat: https://research-explorer.ista.ac.at/record/13179
DOI: 10.1145/3591230

Alistarh D-A, Ellen F, Rybicki J. 2023. Wait-free approximate agreement on graphs. Theoretical Computer Science. 948(2), 113733.
LibreCat: https://research-explorer.ista.ac.at/record/12566
DOI: 10.1016/j.tcs.2023.113733
WoS: 000934262700001

Peste E-A. 2023. Efficiency and generalization of sparse neural networks. Institute of Science and Technology Austria.
LibreCat: https://research-explorer.ista.ac.at/record/13074
DOI: 10.15479/at:ista:13074

Aksenov V, Alistarh D-A, Drozdova A, Mohtashami A. 2023. The splay-list: A distribution-adaptive concurrent skip-list. Distributed Computing. 36, 395–418.
LibreCat: https://research-explorer.ista.ac.at/record/12330
DOI: 10.1007/s00446-022-00441-x
WoS: 000913424000001
arXiv: 2008.01009

Markov I, Vladu A, Guo Q, Alistarh D-A. 2023. Quantized distributed training of large models with convergence guarantees. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 24020–24044.
LibreCat: https://research-explorer.ista.ac.at/record/14461
arXiv: 2302.02390

Shevchenko A, Kögler K, Hassani H, Mondelli M. 2023. Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 31151–31209.
LibreCat: https://research-explorer.ista.ac.at/record/14459
arXiv: 2212.13468

Nikdan M, Pegolotti T, Iofinova EB, Kurtic E, Alistarh D-A. 2023. SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 26215–26227.
LibreCat: https://research-explorer.ista.ac.at/record/14460
arXiv: 2302.04852

Frantar E, Alistarh D-A. 2023. SparseGPT: Massive language models can be accurately pruned in one-shot. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 10323–10337.
LibreCat: https://research-explorer.ista.ac.at/record/14458
arXiv: 2301.00774

Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. 2023. Why extension-based proofs fail. SIAM Journal on Computing. 52(4), 913–944.
LibreCat: https://research-explorer.ista.ac.at/record/14364
DOI: 10.1137/20M1375851
WoS: 001082972300004
arXiv: 1811.01421

Iofinova EB, Peste E-A, Alistarh D-A. 2023. Bias in pruned vision models: In-depth analysis and countermeasures. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 24364–24373.
LibreCat: https://research-explorer.ista.ac.at/record/14771
DOI: 10.1109/cvpr52729.2023.02334
WoS: 001062531308068
arXiv: 2304.12622

Beznosikov A, Horvath S, Richtarik P, Safaryan M. 2023. On biased compression for distributed learning. Journal of Machine Learning Research. 24, 1–50.
LibreCat: https://research-explorer.ista.ac.at/record/14815
WoS: 001111578500001
arXiv: 2002.12410

Koval N, Fedorov A, Sokolova M, Tsitelov D, Alistarh D-A. 2023. Lincheck: A practical framework for testing concurrent data structures on JVM. 35th International Conference on Computer Aided Verification . CAV: Computer Aided Verification, LNCS, vol. 13964, 156–169.
LibreCat: https://research-explorer.ista.ac.at/record/14260
DOI: 10.1007/978-3-031-37706-8_8

Koval N, Fedorov A, Sokolova M, Tsitelov D, Alistarh D-A. 2023. Lincheck: A practical framework for testing concurrent data structures on JVM, Zenodo,10.5281/ZENODO.7877757 .
LibreCat: https://research-explorer.ista.ac.at/record/14995
DOI: 10.5281/ZENODO.7877757

Fedorov A, Hashemi D, Nadiradze G, Alistarh D-A. 2023. Provably-efficient and internally-deterministic parallel Union-Find. Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures. SPAA: Symposium on Parallelism in Algorithms and Architectures, 261–271.
LibreCat: https://research-explorer.ista.ac.at/record/13262
DOI: 10.1145/3558481.3591082
arXiv: 2304.09331

Alistarh D-A, Gelashvili R, Rybicki J. 2022. Fast graphical population protocols. 25th International Conference on Principles of Distributed Systems. OPODIS, LIPIcs, vol. 217, 14.
LibreCat: https://research-explorer.ista.ac.at/record/11184
DOI: 10.4230/LIPIcs.OPODIS.2021.14
arXiv: 2102.08808

Nikabadi A, Korhonen J. 2022. Beyond distributed subgraph detection: Induced subgraphs, multicolored problems and graph parameters. 25th International Conference on Principles of Distributed Systems. OPODIS, LIPIcs, vol. 217, 15.
LibreCat: https://research-explorer.ista.ac.at/record/11183
DOI: 10.4230/LIPIcs.OPODIS.2021.15

Shevchenko A, Kungurtsev V, Mondelli M. 2022. Mean-field analysis of piecewise linear solutions for wide ReLU networks. Journal of Machine Learning Research. 23(130), 1–55.
LibreCat: https://research-explorer.ista.ac.at/record/11420
arXiv: 2111.02278

Pacut M, Parham M, Rybicki J, Schmid S, Suomela J, Tereshchenko A. 2022. Brief announcement: Temporal locality in online algorithms. 36th International Symposium on Distributed Computing. DISC: Symposium on Distributed Computing vol. 246, 52.
LibreCat: https://research-explorer.ista.ac.at/record/12182
DOI: 10.4230/LIPIcs.DISC.2022.52

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

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

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

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

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

Balliu A, Hirvonen J, Melnyk D, Olivetti D, Rybicki J, Suomela J. 2022. Local mending. International Colloquium on Structural Information and Communication Complexity. SIROCCO: Structural Information and Communication ComplexityLNCS vol. 13298, 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

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

Hoefler T, Alistarh D-A, Ben-Nun T, Dryden N, Peste E-A. 2021. Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks. Journal of Machine Learning Research. 22(241), 1–124.
LibreCat: https://research-explorer.ista.ac.at/record/10180
arXiv: 2102.00554

Alistarh D-A, Gelashvili R, Rybicki J. 2021. Brief announcement: Fast graphical population protocols. 35th International Symposium on Distributed Computing. DISC: Distributed Computing , LIPIcs, vol. 209, 43.
LibreCat: https://research-explorer.ista.ac.at/record/10218
DOI: 10.4230/LIPIcs.DISC.2021.43
arXiv: 2102.08808

Alistarh D-A, Gelashvili R, Nadiradze G. 2021. Lower bounds for shared-memory leader election under bounded write contention. 35th International Symposium on Distributed Computing. DISC: Distributed Computing, LIPIcs, vol. 209, 4.
LibreCat: https://research-explorer.ista.ac.at/record/10217
DOI: 10.4230/LIPIcs.DISC.2021.4

Chatterjee B, Peri S, Sa M. 2021. Brief announcement: Non-blocking dynamic unbounded graphs with worst-case amortized bounds. 35th International Symposium on Distributed Computing. DISC: Distributed Computing, LIPIcs, vol. 209, 52.
LibreCat: https://research-explorer.ista.ac.at/record/10216
DOI: 10.4230/LIPIcs.DISC.2021.52
arXiv: 2003.01697

Korhonen J, Paz A, Rybicki J, Schmid S, Suomela J. 2021. Brief announcement: Sinkless orientation is hard also in the supported LOCAL model. 35th International Symposium on Distributed Computing. DISC: Distributed Computing , LIPIcs, vol. 209, 58.
LibreCat: https://research-explorer.ista.ac.at/record/10219
DOI: 10.4230/LIPIcs.DISC.2021.58
arXiv: 2108.02655

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

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

Alimisis F, Davies P, Vandereycken B, Alistarh D-A. 2021. Distributed principal component analysis with limited communication. Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 4, 2823–2834.
LibreCat: https://research-explorer.ista.ac.at/record/11452
arXiv: 2110.14391

Frantar E, Kurtic E, Alistarh D-A. 2021. M-FAC: Efficient matrix-free approximations of second-order information. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 14873–14886.
LibreCat: https://research-explorer.ista.ac.at/record/11463
arXiv: 2010.08222

Alistarh D-A, Korhonen J. 2021. Towards tight communication lower bounds for distributed optimisation. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 7254–7266.
LibreCat: https://research-explorer.ista.ac.at/record/11464
arXiv: 2010.08222

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

Alistarh D-A, Davies P. 2021. Collecting coupons is faster with friends. Structural Information and Communication Complexity.  SIROCCO: International Colloquium on Structural Information and Communication Complexity, LNCS, vol. 12810, 3–12.
LibreCat: https://research-explorer.ista.ac.at/record/9620
DOI: 10.1007/978-3-030-79527-6_1

Alistarh D-A, Ellen F, Rybicki J. 2021. Wait-free approximate agreement on graphs. Structural Information and Communication Complexity. SIROCCO: Structural Information and Communication Complexity, LNCS, vol. 12810, 87–105.
LibreCat: https://research-explorer.ista.ac.at/record/9823
DOI: 10.1007/978-3-030-79527-6_6
arXiv: 2103.08949

Peste E-A, Iofinova EB, Vladu A, Alistarh D-A. 2021. AC/DC: Alternating Compressed/DeCompressed training of deep neural networks. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems vol. 34, 8557–8570.
LibreCat: https://research-explorer.ista.ac.at/record/11458
arXiv: 2106.12379

Alimisis F, Davies P, Alistarh D-A. 2021. Communication-efficient distributed optimization with quantized preconditioners. Proceedings of the 38th International Conference on Machine Learning. International Conference on Machine Learning vol. 139, 196–206.
LibreCat: https://research-explorer.ista.ac.at/record/13147
arXiv: 2102.07214

Li S, Tal Ben-Nun TB-N, Nadiradze G, Girolamo SD, Dryden N, Alistarh D-A, Hoefler T. 2021. Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging. IEEE Transactions on Parallel and Distributed Systems. 32(7), 9271898.
LibreCat: https://research-explorer.ista.ac.at/record/8723
DOI: 10.1109/TPDS.2020.3040606
WoS: 000621405200019
arXiv: 2005.00124

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

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

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

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

Nadiradze G, Markov I, Chatterjee B, Kungurtsev V, Alistarh D-A. 2021. Elastic consistency: A practical consistency model for distributed stochastic gradient descent. Proceedings of the AAAI Conference on Artificial Intelligence. AAAI: Association for the Advancement of Artificial Intelligence vol. 35, 9037–9045.
LibreCat: https://research-explorer.ista.ac.at/record/10432
arXiv: 2001.05918

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

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

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

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

Nadiradze G, Sabour A, Davies P, Li S, Alistarh D-A. 2021. Asynchronous decentralized SGD with quantized and local updates. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems.
LibreCat: https://research-explorer.ista.ac.at/record/10435
arXiv: 1910.12308

Czumaj A, Davies P, Parter M. 2021. Graph sparsification for derandomizing massively parallel computation with low space. ACM Transactions on Algorithms. 17(2), 16.
LibreCat: https://research-explorer.ista.ac.at/record/9541
DOI: 10.1145/3451992
WoS: 000661311300006
arXiv: 1912.05390

Brandt S, Keller B, Rybicki J, Suomela J, Uitto J. 2021. Efficient load-balancing through distributed token dropping. Annual ACM Symposium on Parallelism in Algorithms and Architectures. SPAA: Symposium on Parallelism in Algorithms and Architectures , 129–139.
LibreCat: https://research-explorer.ista.ac.at/record/9678
DOI: 10.1145/3409964.3461785
arXiv: 2005.07761

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

Ramezani-Kebrya A, Faghri F, Markov I, Aksenov V, Alistarh D-A, Roy DM. 2021. NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization. Journal of Machine Learning Research. 22(114), 143.
LibreCat: https://research-explorer.ista.ac.at/record/9571
arXiv: 1908.06077

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

Czumaj A, Davies P, Parter M. 2021. Simple, deterministic, constant-round coloring in congested clique and MPC. SIAM Journal on Computing. 50(5), 1603–1626.
LibreCat: https://research-explorer.ista.ac.at/record/15271
DOI: 10.1137/20m1366502

Czumaj A, Davies P. 2021. Exploiting spontaneous transmissions for broadcasting and leader election in radio networks. Journal of the ACM. 68(2), 13.
LibreCat: https://research-explorer.ista.ac.at/record/15267
DOI: 10.1145/3446383
arXiv: 1703.01859

Alimisis F, Orvieto A, Becigneul G, Lucchi A. 2021. Momentum improves optimization on Riemannian manifolds. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and Statistics, PMLR, vol. 130, 1351–1359.
LibreCat: https://research-explorer.ista.ac.at/record/15263
arXiv: 2002.04144

Arbel-Raviv M, Brown TA, Morrison A. 2020. Getting to the root of concurrent binary search tree performance. Proceedings of the 2018 USENIX Annual Technical Conference. USENIX: Annual Technical Conference, 295–306.
LibreCat: https://research-explorer.ista.ac.at/record/7272

Alistarh D-A, Fedorov A, Koval N. 2020. In search of the fastest concurrent union-find algorithm. 23rd International Conference on Principles of Distributed Systems. OPODIS: International Conference on Principles of Distributed Systems, LIPIcs, vol. 153, 15:1-15:16.
LibreCat: https://research-explorer.ista.ac.at/record/7605
DOI: 10.4230/LIPIcs.OPODIS.2019.15
arXiv: 1911.06347

Czumaj A, Davies P, Parter M. 2020. Simple, deterministic, constant-round coloring in the congested clique. Proceedings of the 2020 ACM Symposium on Principles of Distributed Computing. PODC: Symposium on Principles of Distributed Computing, 309–318.
LibreCat: https://research-explorer.ista.ac.at/record/7803
DOI: 10.1145/3382734.3405751
arXiv: 2009.06043

Aksenov V, Alistarh D-A, Drozdova A, Mohtashami A. 2020. The splay-list: A distribution-adaptive concurrent skip-list. 34th International Symposium on Distributed Computing. DISC: Symposium on Distributed ComputingLIPIcs vol. 179, 3:1-3:18.
LibreCat: https://research-explorer.ista.ac.at/record/8725
DOI: 10.4230/LIPIcs.DISC.2020.3
arXiv: 2008.01009

Singh SP, Alistarh D-A. 2020. WoodFisher: Efficient second-order approximation for neural network compression. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 18098–18109.
LibreCat: https://research-explorer.ista.ac.at/record/9632
arXiv: 2004.14340

Aksenov V, Alistarh D-A, Korhonen J. 2020. Scalable belief propagation via relaxed scheduling. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 33, 22361–22372.
LibreCat: https://research-explorer.ista.ac.at/record/9631
arXiv: 2002.11505

Kurtz M, Kopinsky J, Gelashvili R, Matveev A, Carr J, Goin M, Leiserson W, Moore S, Nell B, Shavit N, Alistarh D-A. 2020. Inducing and exploiting activation sparsity for fast neural network inference. 37th International Conference on Machine Learning, ICML 2020. ICML: International Conference on Machine Learning vol. 119, 5533–5543.
LibreCat: https://research-explorer.ista.ac.at/record/9415

Gurel NM, Kara K, Stojanov A, Smith T, Lemmin T, Alistarh D-A, Puschel M, Zhang C. 2020. Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications. IEEE Transactions on Signal Processing. 68, 4268–4282.
LibreCat: https://research-explorer.ista.ac.at/record/8268
DOI: 10.1109/TSP.2020.3010355
WoS: 000562044500001
arXiv: 1802.04907

Li S, Tal Ben-Nun TB-N, Girolamo SD, Alistarh D-A, Hoefler T. 2020. Taming unbalanced training workloads in deep learning with partial collective operations. Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. PPoPP: Sympopsium on Principles and Practice of Parallel Programming, 45–61.
LibreCat: https://research-explorer.ista.ac.at/record/8722
DOI: 10.1145/3332466.3374528
WoS: 000564476500004
arXiv: 1908.04207

Rybicki J, Abrego N, Ovaskainen O. 2020. Habitat fragmentation and species diversity in competitive communities. Ecology Letters. 23(3), 506–517.
LibreCat: https://research-explorer.ista.ac.at/record/7224
DOI: 10.1111/ele.13450
WoS: 000503625200001

Konstantinov NH, Frantar E, Alistarh D-A, Lampert C. 2020. On the sample complexity of adversarial multi-source PAC learning. Proceedings of the 37th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 119, 5416–5425.
LibreCat: https://research-explorer.ista.ac.at/record/8724
arXiv: 2002.10384

Bhatia S, Chatterjee B, Nathani D, Kaul M. 2020. A persistent homology perspective to the link prediction problem. Complex Networks and their applications VIII. COMPLEX: International Conference on Complex Networks and their Applications, SCI, vol. 881, 27–39.
LibreCat: https://research-explorer.ista.ac.at/record/7213
DOI: 10.1007/978-3-030-36687-2_3
WoS: 000843927300003

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

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

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

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

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

Brandt S, Keller B, Rybicki J, Suomela J, Uitto J. 2020. Brief announcement: Efficient load-balancing through distributed token dropping. 34th International Symposium on Distributed Computing. DISC: Symposium on Distributed Computing, LIPIcs, vol. 179, 40.
LibreCat: https://research-explorer.ista.ac.at/record/15074
DOI: 10.4230/LIPIcs.DISC.2020.40
arXiv: 2005.07761

Alistarh D-A, Nadiradze G, Sabour A. 2020. Dynamic averaging load balancing on cycles. 47th International Colloquium on Automata, Languages, and Programming. ICALP: International Colloquium on Automata, Languages, and Programming, LIPIcs, vol. 168, 7.
LibreCat: https://research-explorer.ista.ac.at/record/15077
DOI: 10.4230/LIPIcs.ICALP.2020.7
arXiv: 2003.09297

Faghri F, Tabrizian I, Markov I, Alistarh D-A, Roy D, Ramezani-Kebrya A. 2020. Adaptive gradient quantization for data-parallel SGD. Advances in Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, NeurIPS, vol. 33.
LibreCat: https://research-explorer.ista.ac.at/record/15086
arXiv: 2010.12460

Jelínek V, Töpfer M. 2019. On grounded L-graphs and their relatives. Electronic Journal of Combinatorics. 26(3), .17.
LibreCat: https://research-explorer.ista.ac.at/record/6759
DOI: 10.37236/8096
arXiv: 1808.04148

Nowak T, Rybicki J. 2019. Byzantine approximate agreement on graphs. 33rd International Symposium on Distributed Computing. DISC: International Symposium on Distributed Computing, LIPIcs, vol. 146, 29:1--29:17.
LibreCat: https://research-explorer.ista.ac.at/record/6931
DOI: 10.4230/LIPICS.DISC.2019.29
arXiv: 1908.02743

Chatterjee B, Peri S, Sa M, Singhal N. 2019. A simple and practical concurrent non-blocking unbounded graph with linearizable reachability queries. ACM International Conference Proceeding Series. ICDCN: Conference on Distributed Computing and Networking, 168–177.
LibreCat: https://research-explorer.ista.ac.at/record/5947
DOI: 10.1145/3288599.3288617
WoS: 000484491600019
arXiv: 1809.00896

Koval N, Alistarh D-A, Elizarov R. 2019. Lock-free channels for programming via communicating sequential processes, ACM Press,p.
LibreCat: https://research-explorer.ista.ac.at/record/6485
DOI: 10.1145/3293883.3297000
WoS: 000587604600044

Ovaskainen O, Rybicki J, Abrego N. 2019. What can observational data reveal about metacommunity processes? Ecography. 42(11), 1877–1886.
LibreCat: https://research-explorer.ista.ac.at/record/6936
DOI: 10.1111/ecog.04444
WoS: 000486348700001

Lenzen C, Rybicki J. 2019. Self-stabilising Byzantine clock synchronisation is almost as easy as consensus. Journal of the ACM. 66(5), 32.
LibreCat: https://research-explorer.ista.ac.at/record/6972
DOI: 10.1145/3339471
WoS: 000496514100001
arXiv: 1705.06173

Khirirat S, Johansson M, Alistarh D-A. 2019. Gradient compression for communication-limited convex optimization. 2018 IEEE Conference on Decision and Control. CDC: Conference on Decision and Control, 8619625.
LibreCat: https://research-explorer.ista.ac.at/record/7122
DOI: 10.1109/cdc.2018.8619625
WoS: 000458114800023

Renggli C, Ashkboos S, Aghagolzadeh M, Alistarh D-A, Hoefler T. 2019. SparCML: High-performance sparse communication for machine learning. International Conference for High Performance Computing, Networking, Storage and Analysis, SC. SC: Conference for High Performance Computing, Networking, Storage and Analysis, .
LibreCat: https://research-explorer.ista.ac.at/record/7201
DOI: 10.1145/3295500.3356222
WoS: 000545976800011
arXiv: 1802.08021

Aganezov S, Zban I, Aksenov V, Alexeev N, Schatz MC. 2019. Recovering rearranged cancer chromosomes from karyotype graphs. BMC Bioinformatics. 20, 641.
LibreCat: https://research-explorer.ista.ac.at/record/7214
DOI: 10.1186/s12859-019-3208-4
WoS: 000511618800007

Koval N, Alistarh D-A, Elizarov R. 2019. Scalable FIFO channels for programming via communicating sequential processes. 25th Anniversary of Euro-Par. Euro-Par: European Conference on Parallel Processing, LNCS, vol. 11725, 317–333.
LibreCat: https://research-explorer.ista.ac.at/record/7228
DOI: 10.1007/978-3-030-29400-7_23
WoS: 000851061400023

Yu C, Tang H, Renggli C, Kassing S, Singla A, Alistarh D-A, Zhang C, Liu J. 2019. Distributed learning over unreliable networks. 36th International Conference on Machine Learning, ICML 2019. ICML: International Conference on Machine Learning vol. 2019–June, 12481–12512.
LibreCat: https://research-explorer.ista.ac.at/record/7437
WoS: 000684034307036
arXiv: 1810.07766

Alistarh D-A, Nadiradze G, Koval N. 2019. Efficiency guarantees for parallel incremental algorithms under relaxed schedulers. 31st ACM Symposium on Parallelism in Algorithms and Architectures. SPAA: Symposium on Parallelism in Algorithms and Architectures, 145–154.
LibreCat: https://research-explorer.ista.ac.at/record/6673
DOI: 10.1145/3323165.3323201
WoS: 000507618500018
arXiv: 2003.09363

Wendler C, Alistarh D-A, Püschel M. 2019. Powerset convolutional neural networks. NIPS: Conference on Neural Information Processing Systems vol. 32, 927–938.
LibreCat: https://research-explorer.ista.ac.at/record/7542
WoS: 000534424300084
arXiv: 1909.02253

Foerster K-T, Korhonen J, Rybicki J, Schmid S. 2019. Does preprocessing help under congestion? Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing. PODC: Symposium on Principles of Distributed Computing, 259–261.
LibreCat: https://research-explorer.ista.ac.at/record/6935
DOI: 10.1145/3293611.3331581
WoS: 000570442000037
arXiv: 1905.03012

Alistarh D-A, Aspnes J, Ellen F, Gelashvili R, Zhu L. 2019. Why extension-based proofs fail. Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. STOC: Symposium on Theory of Computing, 986–996.
LibreCat: https://research-explorer.ista.ac.at/record/6676
DOI: 10.1145/3313276.3316407
WoS: 000523199100089
arXiv: 1811.01421

Censor-Hillel K, Dory M, Korhonen J, Leitersdorf D. 2019. Fast approximate shortest paths in the congested clique. Proceedings of the 2019 ACM Symposium on Principles of Distributed Computin. PODC: Symposium on Principles of Distributed Computing, 74–83.
LibreCat: https://research-explorer.ista.ac.at/record/6933
DOI: 10.1145/3293611.3331633
WoS: 000570442000011
arXiv: 1903.05956

Alistarh D-A, Aspnes J, King V, Saia J. 2018. Communication-efficient randomized consensus. Distributed Computing. 31(6), 489–501.
LibreCat: https://research-explorer.ista.ac.at/record/536
DOI: 10.1007/s00446-017-0315-1