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.

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.

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.

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.

Alistarh D-A, Ellen F, Rybicki J. 2023. Wait-free approximate agreement on graphs. Theoretical Computer Science. 948(2), 113733.

Aksenov V, Alistarh D-A, Drozdova A, Mohtashami A. 2023. The splay-list: A distribution-adaptive concurrent skip-list. Distributed Computing. 36, 395–418.

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.

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.

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.

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.

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.

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.

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 .

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.

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.

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.

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.

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.

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.

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 .

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Alistarh D-A, Nadiradze G, Sabour A. 2021. Dynamic averaging load balancing on cycles. Algorithmica.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Koval N, Alistarh D-A, Elizarov R. 2019. Lock-free channels for programming via communicating sequential processes, ACM Press,p.

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.

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, .

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.

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.

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.

Wendler C, Alistarh D-A, Püschel M. 2019. Powerset convolutional neural networks. NIPS: Conference on Neural Information Processing Systems vol. 32, 927–938.

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.

Alistarh D-A, Aspnes J, King V, Saia J. 2018. Communication-efficient randomized consensus. Distributed Computing. 31(6), 489–501.

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.

Alistarh D-A, Leiserson W, Matveev A, Shavit N. 2018. ThreadScan: Automatic and scalable memory reclamation. ACM Transactions on Parallel Computing. 4(4), 18.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Alistarh D-A, Censor Hillel K, Shavit N. 2016. Are lock free concurrent algorithms practically wait free . Journal of the ACM. 63(4).

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.

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.

Alistarh D-A, Iglesias J, Vojnovi M. 2015. Streaming min-max hypergraph partitioning. NIPS: Neural Information Processing Systems vol. 2015–January, 1900–1908.

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.

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.

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.

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.

Alistarh D-A, Sauerwald T, Vojnovi M. 2015. Lock-Free algorithms under stochastic schedulers. PODC: Principles of Distributed Computing vol. 2015–July, 251–260.

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.

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.

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.

Alistarh D-A, Aspnes J, Censor Hillel K, Gilbert S, Guerraoui R. 2014. Tight bounds for asynchronous renaming. Journal of the ACM. 61(3).

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.

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.

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.