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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Grubic D, Tam L, Alistarh D-A, Zhang C. 2018. Synchronous multi-GPU training for deep learning with low-precision communications: An empirical study. Proceedings of the 21st International Conference on Extending Database Technology. EDBT: Conference on Extending Database Technology, 145–156.
LibreCat: https://research-explorer.ista.ac.at/record/7116
DOI: 10.5441/002/EDBT.2018.14

Alistarh D-A, Leiserson W, Matveev A, Shavit N. 2018. ThreadScan: Automatic and scalable memory reclamation. ACM Transactions on Parallel Computing. 4(4), 18.
LibreCat: https://research-explorer.ista.ac.at/record/6001
DOI: 10.1145/3201897

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

Alistarh D-A, De Sa C, Konstantinov NH. 2018. The convergence of stochastic gradient descent in asynchronous shared memory. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. PODC: Principles of Distributed Computing, 169–178.
LibreCat: https://research-explorer.ista.ac.at/record/5962
DOI: 10.1145/3212734.3212763
WoS: 000458186900022
arXiv: 1803.08841

Alistarh D-A. 2018. A brief tutorial on distributed and concurrent machine learning. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. PODC: Principles of Distributed Computing, 487–488.
LibreCat: https://research-explorer.ista.ac.at/record/5961
DOI: 10.1145/3212734.3212798
WoS: 000458186900063

Alistarh D-A, Brown TA, Kopinsky J, Nadiradze G. 2018. Relaxed schedulers can efficiently parallelize iterative algorithms. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. PODC: Principles of Distributed Computing, 377–386.
LibreCat: https://research-explorer.ista.ac.at/record/5963
DOI: 10.1145/3212734.3212756
WoS: 000458186900048
arXiv: 1808.04155

Alistarh D-A, Brown TA, Kopinsky J, Li JZ, Nadiradze G. 2018. Distributionally linearizable data structures. Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18. SPAA: Symposium on Parallelism in Algorithms and Architectures, 133–142.
LibreCat: https://research-explorer.ista.ac.at/record/5965
DOI: 10.1145/3210377.3210411
WoS: 000545269600016
arXiv: 1804.01018

Alistarh D-A, Haider SK, Kübler R, Nadiradze G. 2018. The transactional conflict problem. Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18. SPAA: Symposium on Parallelism in Algorithms and Architectures, 383–392.
LibreCat: https://research-explorer.ista.ac.at/record/5966
DOI: 10.1145/3210377.3210406
WoS: 000545269600046
arXiv: 1804.00947

Aksenov V, Alistarh D-A, Kuznetsov P. 2018. Brief Announcement: Performance prediction for coarse-grained locking. Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18. PODC: Principles of Distributed Computing, 411–413.
LibreCat: https://research-explorer.ista.ac.at/record/5964
DOI: 10.1145/3212734.3212785
WoS: 000458186900052

Stojanov A, Smith TM, Alistarh D-A, Puschel M. 2018. Fast quantized arithmetic on : Trading compute for data movement. 2018 IEEE International Workshop on Signal Processing Systems. SiPS: Workshop on Signal Processing Systems vol. 2018–October, 8598402.
LibreCat: https://research-explorer.ista.ac.at/record/6031
DOI: 10.1109/SiPS.2018.8598402
WoS: 000465106800060

Alistarh D-A, Aspnes J, Gelashvili R. 2018. Space-optimal majority in population protocols. Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms. SODA: Symposium on Discrete Algorithms, 2221–2239.
LibreCat: https://research-explorer.ista.ac.at/record/7123
DOI: 10.1137/1.9781611975031.144
WoS: 000483921200145
arXiv: 1704.04947

Alistarh D-A, Allen-Zhu Z, Li J. 2018. Byzantine stochastic gradient descent. Advances in Neural Information Processing Systems. NeurIPS: Conference on Neural Information Processing Systems vol. 2018, 4613–4623.
LibreCat: https://research-explorer.ista.ac.at/record/6558
WoS: 000461823304061
arXiv: 1803.08917

Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli C. 2018. The convergence of sparsified gradient methods. Advances in Neural Information Processing Systems 31. NeurIPS: Conference on Neural Information Processing Systems vol. Volume 2018, 5973–5983.
LibreCat: https://research-explorer.ista.ac.at/record/6589
WoS: 000461852000047
arXiv: 1809.10505

Baig G, Radunovic B, Alistarh D-A, Balkwill M, Karagiannis T, Qiu L. 2017. Towards unlicensed cellular networks in TV white spaces. Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies. CoNEXT: Conference on emerging Networking EXperiments and Technologies, 2–14.
LibreCat: https://research-explorer.ista.ac.at/record/487
DOI: 10.1145/3143361.3143367

Alistarh D-A, Dudek B, Kosowski A, Soloveichik D, Uznaski P. 2017. Robust detection in leak-prone population protocols. DNA Computing and Molecular Programming, LNCS, vol. 10467 LNCS, 155–171.
LibreCat: https://research-explorer.ista.ac.at/record/788
DOI: 10.1007/978-3-319-66799-7_11
arXiv: 1706.09937

Alistarh D-A, Aspnes J, Eisenstat D, Rivest R, Gelashvili R. 2017. Time-space trade-offs in population protocols. SODA: Symposium on Discrete Algorithms, 2560–2579.
LibreCat: https://research-explorer.ista.ac.at/record/787
DOI: doi.org/10.1137/1.9781611974782.169

Alistarh D-A, Leiserson W, Matveev A, Shavit N. 2017. Forkscan: Conservative memory reclamation for modern operating systems. EuroSys: European Conference on Computer Systems, 483–498.
LibreCat: https://research-explorer.ista.ac.at/record/789
DOI: 10.1145/3064176.3064214

Kara K, Alistarh D-A, Alonso G, Mutlu O, Zhang C. 2017. FPGA-accelerated dense linear machine learning: A precision-convergence trade-off. FCCM: Field-Programmable Custom Computing Machines, 160–167.
LibreCat: https://research-explorer.ista.ac.at/record/790
DOI: 10.1109/FCCM.2017.39

Alistarh D-A, Kopinsky J, Li J, Nadiradze G. 2017. The power of choice in priority scheduling. Proceedings of the ACM Symposium on Principles of Distributed Computing. PODC: Principles of Distributed Computing vol. Part 9314, 283–292.
LibreCat: https://research-explorer.ista.ac.at/record/791
DOI: 10.1145/3087801.3087810
WoS: 000462995000035

Alistarh D-A, Grubic D, Li J, Tomioka R, Vojnovi M. 2017. QSGD: Communication-efficient SGD via gradient quantization and encoding. NIPS: Neural Information Processing System, Advances in Neural Information Processing Systems, vol. 2017, 1710–1721.
LibreCat: https://research-explorer.ista.ac.at/record/431
arXiv: 1610.02132

Zhang H, Li J, Kara K, Alistarh D-A, Liu J, Zhang C. 2017. ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning. Proceedings of Machine Learning Research. ICML: International  Conference  on  Machine Learning, PMLR Press, vol. 70, 4035–4043.
LibreCat: https://research-explorer.ista.ac.at/record/432

Alistarh D-A, Censor Hillel K, Shavit N. 2016. Are lock free concurrent algorithms practically wait free . Journal of the ACM. 63(4).
LibreCat: https://research-explorer.ista.ac.at/record/786
DOI: 10.1145/2903136
arXiv: 1311.3200

Haider S, Hasenplaugh W, Alistarh D-A. 2016. Lease/Release: Architectural support for scaling contended data structures. PPoPP: Principles and Practice of Parallel Pogramming vol. 12-16-March-2016.
LibreCat: https://research-explorer.ista.ac.at/record/785
DOI: 10.1145/2851141.2851155

Alistarh D-A, Kopinsky J, Li J, Shavit N. 2015. The SprayList: A scalable relaxed priority queue. PPoPP: Principles and Practice of Parallel Pogramming vol. 2015–January, 11–20.
LibreCat: https://research-explorer.ista.ac.at/record/776
DOI: 10.1145/2688500.2688523

Alistarh D-A, Iglesias J, Vojnovi M. 2015. Streaming min-max hypergraph partitioning. NIPS: Neural Information Processing Systems vol. 2015–January, 1900–1908.
LibreCat: https://research-explorer.ista.ac.at/record/777

Alistarh D-A, Kopinsky J, Kuznetsov P, Ravi S, Shavit N. 2015. Inherent limitations of hybrid transactional memory. DISC: Distributed Computing, LNCS, vol. 9363, 185–199.
LibreCat: https://research-explorer.ista.ac.at/record/778
DOI: 10.1007/978-3-662-48653-5_13
arXiv: 1405.5689

Alistarh D-A, Matveev A, Leiserson W, Shavit N. 2015. ThreadScan: Automatic and scalable memory reclamation. SPAA: Symposium on Parallelism in Algorithms and Architectures vol. 2015–June, 123–132.
LibreCat: https://research-explorer.ista.ac.at/record/779
DOI: 10.1145/2755573.2755600

Alistarh D-A, Gelashvili R. 2015. Polylogarithmic-time leader election in population protocols. ICALP: International Colloquium on Automota, Languages and Programming vol. 9135, 479–491.
LibreCat: https://research-explorer.ista.ac.at/record/780
DOI: 10.1007/978-3-662-47666-6_38
arXiv: 1502.05745

Alistarh D-A, Gelashvili R, Vojnovi M. 2015. Fast and exact majority in population protocols. PODC: Principles of Distributed Computing vol. 2015–July, 47–56.
LibreCat: https://research-explorer.ista.ac.at/record/781
DOI: 10.1145/2767386.2767429

Alistarh D-A, Sauerwald T, Vojnovi M. 2015. Lock-Free algorithms under stochastic schedulers. PODC: Principles of Distributed Computing vol. 2015–July, 251–260.
LibreCat: https://research-explorer.ista.ac.at/record/782
DOI: 10.1145/2767386.2767430

Alistarh D-A, Gelashvili R, Vladu A. 2015. How to elect a leader faster than a tournament. PODC: Principles of Distributed Computing vol. 2015–July, 365–374.
LibreCat: https://research-explorer.ista.ac.at/record/783
DOI: 10.1145/2767386.2767420

Alistarh D-A, Ballani H, Costa P, Funnell A, Benjamin J, Watts P, Thomsen B. 2015. A high-radix, low-latency optical switch for data centers. SIGCOMM: Special Interest Group on Data Communication, 367–368.
LibreCat: https://research-explorer.ista.ac.at/record/784
DOI: 10.1145/2785956.2790035

Alistarh D-A, Aspnes J, Bender M, Gelashvili R, Gilbert S. 2014. Dynamic task allocation in asynchronous shared memory. SODA: Symposium on Discrete Algorithms, 416–435.
LibreCat: https://research-explorer.ista.ac.at/record/768
DOI: 10.1137/1.9781611973402.31

Alistarh D-A, Aspnes J, Censor Hillel K, Gilbert S, Guerraoui R. 2014. Tight bounds for asynchronous renaming. Journal of the ACM. 61(3).
LibreCat: https://research-explorer.ista.ac.at/record/769
DOI: 10.1145/2597630

Alistarh D-A, Eugster P, Herlihy M, Matveev A, Shavit N. 2014. StackTrack: An automated transactional approach to concurrent memory reclamation. EuroSys: European Conference on Computer Systems.
LibreCat: https://research-explorer.ista.ac.at/record/770
DOI: 10.1145/2592798.2592808

Alistarh D-A, Denysyuk O, Rodrígues L, Shavit N. 2014. Balls-into-Leaves: Sub-logarithmic renaming in synchronous message-passing systems. PODC: Principles of Distributed Computing, 232–241.
LibreCat: https://research-explorer.ista.ac.at/record/771
DOI: 10.1145/2611462.2611499

Alistarh D-A, Censor Hillel K, Shavit N. 2014. Are lock-free concurrent algorithms practically wait-free? STOC: Symposium on Theory of Computing, 714–723.
LibreCat: https://research-explorer.ista.ac.at/record/772
DOI: 10.1145/2591796.2591836
arXiv: 1311.3200