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

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

V. Aksenov, T. A. Brown, A. Fedorov, and I. Kokorin, Unexpected scaling in path copying trees. Association for Computing Machinery, 2023, pp. 438–440.
LibreCat: https://research-explorer.ista.ac.at/record/12736
DOI: 10.1145/3572848.3577512

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

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

D.-A. Alistarh, F. Ellen, and J. Rybicki, “Wait-free approximate agreement on graphs,” Theoretical Computer Science, vol. 948, no. 2. Elsevier, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/12566
DOI: 10.1016/j.tcs.2023.113733
WoS: 000934262700001

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

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

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

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

M. Nikdan, T. Pegolotti, E. B. Iofinova, E. Kurtic, and D.-A. Alistarh, “SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 26215–26227.
LibreCat: https://research-explorer.ista.ac.at/record/14460
arXiv: 2302.04852

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

D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, and L. Zhu, “Why extension-based proofs fail,” SIAM Journal on Computing, vol. 52, no. 4. Society for Industrial and Applied Mathematics, pp. 913–944, 2023.
LibreCat: https://research-explorer.ista.ac.at/record/14364
DOI: 10.1137/20M1375851
WoS: 001082972300004
arXiv: 1811.01421

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

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

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

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

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

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

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

A. Shevchenko, V. Kungurtsev, and M. Mondelli, “Mean-field analysis of piecewise linear solutions for wide ReLU networks,” Journal of Machine Learning Research, vol. 23, no. 130. Journal of Machine Learning Research, pp. 1–55, 2022.
LibreCat: https://research-explorer.ista.ac.at/record/11420
arXiv: 2111.02278

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

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

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

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

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

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

A. Balliu, J. Hirvonen, D. Melnyk, D. Olivetti, J. Rybicki, and J. Suomela, “Local mending,” in International Colloquium on Structural Information and Communication Complexity, Paderborn, Germany, 2022, vol. 13298, 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

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

T. Hoefler, D.-A. Alistarh, T. Ben-Nun, N. Dryden, and E.-A. Peste, “Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks,” Journal of Machine Learning Research, vol. 22, no. 241. Journal of Machine Learning Research, pp. 1–124, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10180
arXiv: 2102.00554

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

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

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

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

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

V. Kungurtsev, M. Egan, B. Chatterjee, and D.-A. Alistarh, “Asynchronous optimization methods for efficient training of deep neural networks with guarantees,” in 35th AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual, Online, 2021, vol. 35, no. 9B, pp. 8209–8216.
LibreCat: https://research-explorer.ista.ac.at/record/11436
arXiv: 1905.11845

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

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

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

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

D.-A. Alistarh and P. Davies, “Collecting coupons is faster with friends,” in Structural Information and Communication Complexity, Wrocaw, Poland, 2021, vol. 12810, pp. 3–12.
LibreCat: https://research-explorer.ista.ac.at/record/9620
DOI: 10.1007/978-3-030-79527-6_1

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

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

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

S. Li et al., “Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging,” IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 7. IEEE, 2021.
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DOI: 10.1109/TPDS.2020.3040606
WoS: 000621405200019
arXiv: 2005.00124

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

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

A. Czumaj, P. Davies, and M. Parter, “Improved deterministic (+1) coloring in low-space MPC,” in Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, Virtual, Italy, 2021, pp. 469–479.
LibreCat: https://research-explorer.ista.ac.at/record/9935
DOI: 10.1145/3465084.3467937
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A. Czumaj, P. Davies, and M. Parter, “Component stability in low-space massively parallel computation,” in Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, Virtual, Italy, 2021, pp. 481–491.
LibreCat: https://research-explorer.ista.ac.at/record/9933
DOI: 10.1145/3465084.3467903
WoS: 000744439800049
arXiv: 2106.01880

G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, and D.-A. Alistarh, “Elastic consistency: A practical consistency model for distributed stochastic gradient descent,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 10, pp. 9037–9045.
LibreCat: https://research-explorer.ista.ac.at/record/10432
arXiv: 2001.05918

K. Klein et al., “Keep the dirt: tainted TreeKEM, adaptively and actively secure continuous group key agreement,” in 2021 IEEE Symposium on Security and Privacy , San Francisco, CA, United States, 2021, pp. 268–284.
LibreCat: https://research-explorer.ista.ac.at/record/10049
DOI: 10.1109/sp40001.2021.00035

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

K.-T. Foerster, J. Korhonen, A. Paz, J. Rybicki, and S. Schmid, “Input-dynamic distributed algorithms for communication networks,” Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 5, no. 1. Association for Computing Machinery, pp. 1–33, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10855
DOI: 10.1145/3447384
arXiv: 2005.07637

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

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

A. Czumaj, P. Davies, and M. Parter, “Graph sparsification for derandomizing massively parallel computation with low space,” ACM Transactions on Algorithms, vol. 17, no. 2. Association for Computing Machinery, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/9541
DOI: 10.1145/3451992
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arXiv: 1912.05390

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

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

A. Ramezani-Kebrya, F. Faghri, I. Markov, V. Aksenov, D.-A. Alistarh, and D. M. Roy, “NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization,” Journal of Machine Learning Research, vol. 22, no. 114. Journal of Machine Learning Research, p. 143, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/9571
arXiv: 1908.06077

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

A. Czumaj, P. Davies, and M. Parter, “Simple, deterministic, constant-round coloring in congested clique and MPC,” SIAM Journal on Computing, vol. 50, no. 5. Society for Industrial &; Applied Mathematics, pp. 1603–1626, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/15271
DOI: 10.1137/20m1366502

A. Czumaj and P. Davies, “Exploiting spontaneous transmissions for broadcasting and leader election in radio networks,” Journal of the ACM, vol. 68, no. 2. Association for Computing Machinery, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/15267
DOI: 10.1145/3446383
arXiv: 1703.01859

F. Alimisis, A. Orvieto, G. Becigneul, and A. Lucchi, “Momentum improves optimization on Riemannian manifolds,” in Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, San Diego, CA, United States; Virtual, 2021, vol. 130, pp. 1351–1359.
LibreCat: https://research-explorer.ista.ac.at/record/15263
arXiv: 2002.04144

M. Arbel-Raviv, T. A. Brown, and A. Morrison, “Getting to the root of concurrent binary search tree performance,” in Proceedings of the 2018 USENIX Annual Technical Conference, Boston, MA, United States, 2020, pp. 295–306.
LibreCat: https://research-explorer.ista.ac.at/record/7272

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

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

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

S. P. Singh and D.-A. Alistarh, “WoodFisher: Efficient second-order approximation for neural network compression,” in Advances in Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 18098–18109.
LibreCat: https://research-explorer.ista.ac.at/record/9632
arXiv: 2004.14340

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

M. Kurtz et al., “Inducing and exploiting activation sparsity for fast neural network inference,” in 37th International Conference on Machine Learning, ICML 2020, Online, 2020, vol. 119, pp. 5533–5543.
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N. M. Gurel et al., “Compressive sensing using iterative hard thresholding with low precision data representation: Theory and applications,” IEEE Transactions on Signal Processing, vol. 68. IEEE, pp. 4268–4282, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/8268
DOI: 10.1109/TSP.2020.3010355
WoS: 000562044500001
arXiv: 1802.04907

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

J. Rybicki, N. Abrego, and O. Ovaskainen, “Habitat fragmentation and species diversity in competitive communities,” Ecology Letters, vol. 23, no. 3. Wiley, pp. 506–517, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/7224
DOI: 10.1111/ele.13450
WoS: 000503625200001

N. H. Konstantinov, E. Frantar, D.-A. Alistarh, and C. Lampert, “On the sample complexity of adversarial multi-source PAC learning,” in Proceedings of the 37th International Conference on Machine Learning, Online, 2020, vol. 119, pp. 5416–5425.
LibreCat: https://research-explorer.ista.ac.at/record/8724
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S. Bhatia, B. Chatterjee, D. Nathani, and M. Kaul, “A persistent homology perspective to the link prediction problem,” in Complex Networks and their applications VIII, Lisbon, Portugal, 2020, vol. 881, pp. 27–39.
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D.-A. Alistarh, G. Nadiradze, and A. Sabour, “Dynamic averaging load balancing on cycles,” in 47th International Colloquium on Automata, Languages, and Programming, Saarbrücken, Germany, Virtual, 2020, vol. 168.
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