Alistarh, Dan-Adrian, Giorgi Nadiradze, and Amirmojtaba Sabour. “Dynamic Averaging Load Balancing on Cycles.”https://doi.org/10.1007/s00453-021-00905-9__Algorithmica. Springer Nature, 2022.https://doi.org/10.1007/s00453-021-00905-9 .
LibreCat: https://research-explorer.ista.ac.at/record/8286
DOI: 10.1007/s00453-021-00905-9
WoS: 000734004600001
arXiv: 2003.09297

Hoefler, Torsten, Dan-Adrian Alistarh, Tal Ben-Nun, Nikoli Dryden, and Elena-Alexandra Peste. “Sparsity in Deep Learning: Pruning and Growth for Efficient Inference and Training in Neural Networks.” Journal of Machine Learning Research. Journal of Machine Learning Research, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10180
arXiv: 2102.00554

Alistarh, Dan-Adrian, Rati Gelashvili, and Joel Rybicki. “Brief Announcement: Fast Graphical Population Protocols.”https://doi.org/10.4230/LIPIcs.DISC.2021.43__In 35th International Symposium on Distributed Computing, Vol. 209. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021.https://doi.org/10.4230/LIPIcs.DISC.2021.43 .
LibreCat: https://research-explorer.ista.ac.at/record/10218
DOI: 10.4230/LIPIcs.DISC.2021.43
arXiv: 2102.08808

Alistarh, Dan-Adrian, Rati Gelashvili, and Giorgi Nadiradze. “Lower Bounds for Shared-Memory Leader Election under Bounded Write Contention.”https://doi.org/10.4230/LIPIcs.DISC.2021.4__In 35th International Symposium on Distributed Computing, Vol. 209. Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021.https://doi.org/10.4230/LIPIcs.DISC.2021.4 .
LibreCat: https://research-explorer.ista.ac.at/record/10217
DOI: 10.4230/LIPIcs.DISC.2021.4

Chatterjee, Bapi, Sathya Peri, and Muktikanta Sa. “Brief Announcement: Non-Blocking Dynamic Unbounded Graphs with Worst-Case Amortized Bounds.”https://doi.org/10.4230/LIPIcs.DISC.2021.52__In 35th International Symposium on Distributed Computing, Vol. 209. Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021.https://doi.org/10.4230/LIPIcs.DISC.2021.52 .
LibreCat: https://research-explorer.ista.ac.at/record/10216
DOI: 10.4230/LIPIcs.DISC.2021.52
arXiv: 2003.01697

Korhonen, Janne, Ami Paz, Joel Rybicki, Stefan Schmid, and Jukka Suomela. “Brief Announcement: Sinkless Orientation Is Hard Also in the Supported LOCAL Model.”https://doi.org/10.4230/LIPIcs.DISC.2021.58__In 35th International Symposium on Distributed Computing, Vol. 209. Schloss Dagstuhl - Leibniz Zentrum für Informatik, 2021.https://doi.org/10.4230/LIPIcs.DISC.2021.58 .
LibreCat: https://research-explorer.ista.ac.at/record/10219
DOI: 10.4230/LIPIcs.DISC.2021.58
arXiv: 2108.02655

Fedorov, Alexander, Nikita Koval, and Dan-Adrian Alistarh. “A Scalable Concurrent Algorithm for Dynamic Connectivity.”https://doi.org/10.1145/3409964.3461810__In Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures, 208–20. Association for Computing Machinery, 2021.https://doi.org/10.1145/3409964.3461810 .
LibreCat: https://research-explorer.ista.ac.at/record/10853
DOI: 10.1145/3409964.3461810
arXiv: 2105.08098

Kungurtsev, Vyacheslav, Malcolm Egan, Bapi Chatterjee, and Dan-Adrian Alistarh. “Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees.” In 35th AAAI Conference on Artificial Intelligence, AAAI 2021, 35:8209–16. AAAI Press, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/11436
arXiv: 1905.11845

Alimisis, Foivos, Peter Davies, Bart Vandereycken, and Dan-Adrian Alistarh. “Distributed Principal Component Analysis with Limited Communication.” In Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, 4:2823–34. Neural Information Processing Systems Foundation, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/11452
arXiv: 2110.14391

Frantar, Elias, Eldar Kurtic, and Dan-Adrian Alistarh. “M-FAC: Efficient Matrix-Free Approximations of Second-Order Information.” In 35th Conference on Neural Information Processing Systems, 34:14873–86. Curran Associates, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/11463
arXiv: 2010.08222

Alistarh, Dan-Adrian, and Janne Korhonen. “Towards Tight Communication Lower Bounds for Distributed Optimisation.” In 35th Conference on Neural Information Processing Systems, 34:7254–66. Curran Associates, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/11464
arXiv: 2010.08222

Davies, Peter, Vijaykrishna Gurunanthan, Niusha  Moshrefi, Saleh Ashkboos, and Dan-Adrian Alistarh. “New Bounds for Distributed Mean Estimation and Variance Reduction.” In 9th International Conference on Learning Representations, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/9543
arXiv: 2002.09268

Alistarh, Dan-Adrian, and Peter Davies. “Collecting Coupons Is Faster with Friends.”https://doi.org/10.1007/978-3-030-79527-6_1__In Structural Information and Communication Complexity, 12810:3–12. Springer Nature, 2021.https://doi.org/10.1007/978-3-030-79527-6_1 .
LibreCat: https://research-explorer.ista.ac.at/record/9620
DOI: 10.1007/978-3-030-79527-6_1

Alistarh, Dan-Adrian, Faith Ellen, and Joel Rybicki. “Wait-Free Approximate Agreement on Graphs.”https://doi.org/10.1007/978-3-030-79527-6_6__In Structural Information and Communication Complexity, 12810:87–105. Springer Nature, 2021.https://doi.org/10.1007/978-3-030-79527-6_6 .
LibreCat: https://research-explorer.ista.ac.at/record/9823
DOI: 10.1007/978-3-030-79527-6_6
arXiv: 2103.08949

Peste, Elena-Alexandra, Eugenia B Iofinova, Adrian Vladu, and Dan-Adrian Alistarh. “AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks.” In 35th Conference on Neural Information Processing Systems, 34:8557–70. Curran Associates, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/11458
arXiv: 2106.12379

Alimisis, Foivos, Peter Davies, and Dan-Adrian Alistarh. “Communication-Efficient Distributed Optimization with Quantized Preconditioners.” In Proceedings of the 38th International Conference on Machine Learning, 139:196–206. ML Research Press, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/13147
arXiv: 2102.07214

Li, Shigang, Tal Ben-Nun Tal Ben-Nun, Giorgi Nadiradze, Salvatore Di Girolamo, Nikoli Dryden, Dan-Adrian Alistarh, and Torsten Hoefler. “Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging.”https://doi.org/10.1109/TPDS.2020.3040606__IEEE Transactions on Parallel and Distributed Systems. IEEE, 2021.https://doi.org/10.1109/TPDS.2020.3040606 .
LibreCat: https://research-explorer.ista.ac.at/record/8723
DOI: 10.1109/TPDS.2020.3040606
WoS: 000621405200019
arXiv: 2005.00124

Chatterjee, Bapi, Ivan Walulya, and Philippas Tsigas. “Concurrent Linearizable Nearest Neighbour Search in LockFree-KD-Tree.”https://doi.org/10.1016/j.tcs.2021.06.041__Theoretical Computer Science. Elsevier, 2021.https://doi.org/10.1016/j.tcs.2021.06.041 .
LibreCat: https://research-explorer.ista.ac.at/record/9827
DOI: 10.1016/j.tcs.2021.06.041
WoS: 000694718900004

Alistarh, Dan-Adrian, Martin Töpfer, and Przemysaw Uznaski. “Comparison Dynamics in Population Protocols.”https://doi.org/10.1145/3465084.3467915__In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, 55–65. Association for Computing Machinery, 2021.https://doi.org/10.1145/3465084.3467915 .
LibreCat: https://research-explorer.ista.ac.at/record/9951
DOI: 10.1145/3465084.3467915
WoS: 000744439800005

Czumaj, Artur, Peter Davies, and Merav Parter. “Improved Deterministic (+1) Coloring in Low-Space MPC.”https://doi.org/10.1145/3465084.3467937__In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, 469–479. Association for Computing Machinery, 2021.https://doi.org/10.1145/3465084.3467937 .
LibreCat: https://research-explorer.ista.ac.at/record/9935
DOI: 10.1145/3465084.3467937
WoS: 000744439800048

Czumaj, Artur, Peter Davies, and Merav Parter. “Component Stability in Low-Space Massively Parallel Computation.”https://doi.org/10.1145/3465084.3467903__In Proceedings of the 2021 ACM Symposium on Principles of Distributed Computing, 481–491. Association for Computing Machinery, 2021.https://doi.org/10.1145/3465084.3467903 .
LibreCat: https://research-explorer.ista.ac.at/record/9933
DOI: 10.1145/3465084.3467903
WoS: 000744439800049
arXiv: 2106.01880

Nadiradze, Giorgi, Ilia Markov, Bapi Chatterjee, Vyacheslav  Kungurtsev, and Dan-Adrian Alistarh. “Elastic Consistency: A Practical Consistency Model for Distributed Stochastic Gradient Descent.” In Proceedings of the AAAI Conference on Artificial Intelligence, 35:9037–45, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10432
arXiv: 2001.05918

Klein, Karen, Guillermo Pascual Perez, Michael Walter, Chethan Kamath Hosdurg, Margarita Capretto, Miguel Cueto Noval, Ilia Markov, Michelle X Yeo, Joel F Alwen, and Krzysztof Z Pietrzak. “Keep the Dirt: Tainted TreeKEM, Adaptively and Actively Secure Continuous Group Key Agreement.”https://doi.org/10.1109/sp40001.2021.00035__In 2021 IEEE Symposium on Security and Privacy , 268–84. IEEE, 2021.https://doi.org/10.1109/sp40001.2021.00035 .
LibreCat: https://research-explorer.ista.ac.at/record/10049
DOI: 10.1109/sp40001.2021.00035

Foerster, Klaus-Tycho, Janne Korhonen, Ami Paz, Joel Rybicki, and Stefan Schmid. “Input-Dynamic Distributed Algorithms for Communication Networks.”https://doi.org/10.1145/3410220.3453923__In Abstract Proceedings of the 2021 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, 71–72. Association for Computing Machinery, 2021.https://doi.org/10.1145/3410220.3453923 .
LibreCat: https://research-explorer.ista.ac.at/record/10854
DOI: 10.1145/3410220.3453923
arXiv: 2005.07637

Foerster, Klaus-Tycho, Janne Korhonen, Ami Paz, Joel Rybicki, and Stefan Schmid. “Input-Dynamic Distributed Algorithms for Communication Networks.”https://doi.org/10.1145/3447384__Proceedings of the ACM on Measurement and Analysis of Computing Systems. Association for Computing Machinery, 2021.https://doi.org/10.1145/3447384 .
LibreCat: https://research-explorer.ista.ac.at/record/10855
DOI: 10.1145/3447384
arXiv: 2005.07637

Nadiradze, Giorgi. “On Achieving Scalability through Relaxation.”https://doi.org/10.15479/at:ista:10429__Institute of Science and Technology Austria, 2021.https://doi.org/10.15479/at:ista:10429 .
LibreCat: https://research-explorer.ista.ac.at/record/10429
DOI: 10.15479/at:ista:10429

Nadiradze, Giorgi, Amirmojtaba Sabour, Peter Davies, Shigang Li, and Dan-Adrian Alistarh. “Asynchronous Decentralized SGD with Quantized and Local Updates.” In 35th Conference on Neural Information Processing Systems. Neural Information Processing Systems Foundation, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/10435
arXiv: 1910.12308

Czumaj, Artur, Peter Davies, and Merav Parter. “Graph Sparsification for Derandomizing Massively Parallel Computation with Low Space.”https://doi.org/10.1145/3451992__ACM Transactions on Algorithms. Association for Computing Machinery, 2021.https://doi.org/10.1145/3451992 .
LibreCat: https://research-explorer.ista.ac.at/record/9541
DOI: 10.1145/3451992
WoS: 000661311300006
arXiv: 1912.05390

Brandt, Sebastian, Barbara Keller, Joel Rybicki, Jukka Suomela, and Jara Uitto. “Efficient Load-Balancing through Distributed Token Dropping.”https://doi.org/10.1145/3409964.3461785__In Annual ACM Symposium on Parallelism in Algorithms and Architectures, 129–39, 2021.https://doi.org/10.1145/3409964.3461785 .
LibreCat: https://research-explorer.ista.ac.at/record/9678
DOI: 10.1145/3409964.3461785
arXiv: 2005.07761

Ramezani-Kebrya, Ali, Fartash Faghri, Ilya Markov, Vitalii Aksenov, Dan-Adrian Alistarh, and Daniel M. Roy. “NUQSGD: Provably Communication-Efficient Data-Parallel SGD via Nonuniform Quantization.” Journal of Machine Learning Research. Journal of Machine Learning Research, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/9571
arXiv: 1908.06077

Censor-Hillel, Keren, Michal Dory, Janne Korhonen, and Dean Leitersdorf. “Fast Approximate Shortest Paths in the Congested Clique.”https://doi.org/10.1007/s00446-020-00380-5__Distributed Computing. Springer Nature, 2021.https://doi.org/10.1007/s00446-020-00380-5 .
LibreCat: https://research-explorer.ista.ac.at/record/7939
DOI: 10.1007/s00446-020-00380-5
WoS: 000556444600001
arXiv: 1903.05956

Czumaj, Artur, Peter Davies, and Merav Parter. “Simple, Deterministic, Constant-Round Coloring in Congested Clique and MPC.”https://doi.org/10.1137/20m1366502__SIAM Journal on Computing. Society for Industrial &; Applied Mathematics, 2021.https://doi.org/10.1137/20m1366502 .
LibreCat: https://research-explorer.ista.ac.at/record/15271
DOI: 10.1137/20m1366502

Czumaj, Artur, and Peter Davies. “Exploiting Spontaneous Transmissions for Broadcasting and Leader Election in Radio Networks.”https://doi.org/10.1145/3446383__Journal of the ACM. Association for Computing Machinery, 2021.https://doi.org/10.1145/3446383 .
LibreCat: https://research-explorer.ista.ac.at/record/15267
DOI: 10.1145/3446383
arXiv: 1703.01859

Alimisis, Foivos, Antonio Orvieto, Gary Becigneul, and Aurelien Lucchi. “Momentum Improves Optimization on Riemannian Manifolds.” In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 130:1351–59. ML Research Press, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/15263
arXiv: 2002.04144

Arbel-Raviv, Maya, Trevor A Brown, and Adam Morrison. “Getting to the Root of Concurrent Binary Search Tree Performance.” In Proceedings of the 2018 USENIX Annual Technical Conference, 295–306. USENIX Association, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/7272

Alistarh, Dan-Adrian, Alexander Fedorov, and Nikita Koval. “In Search of the Fastest Concurrent Union-Find Algorithm.”https://doi.org/10.4230/LIPIcs.OPODIS.2019.15__In 23rd International Conference on Principles of Distributed Systems, 153:15:1-15:16. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.https://doi.org/10.4230/LIPIcs.OPODIS.2019.15 .
LibreCat: https://research-explorer.ista.ac.at/record/7605
DOI: 10.4230/LIPIcs.OPODIS.2019.15
arXiv: 1911.06347

Czumaj, Artur, Peter Davies, and Merav Parter. “Simple, Deterministic, Constant-Round Coloring in the Congested Clique.”https://doi.org/10.1145/3382734.3405751__In Proceedings of the 2020 ACM Symposium on Principles of Distributed Computing, 309–18. Association for Computing Machinery, 2020.https://doi.org/10.1145/3382734.3405751 .
LibreCat: https://research-explorer.ista.ac.at/record/7803
DOI: 10.1145/3382734.3405751
arXiv: 2009.06043

Aksenov, Vitaly, Dan-Adrian Alistarh, Alexandra Drozdova, and Amirkeivan Mohtashami. “The Splay-List: A Distribution-Adaptive Concurrent Skip-List.”https://doi.org/10.4230/LIPIcs.DISC.2020.3__In 34th International Symposium on Distributed Computing, 179:3:1-3:18. LIPIcs. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.https://doi.org/10.4230/LIPIcs.DISC.2020.3 .
LibreCat: https://research-explorer.ista.ac.at/record/8725
DOI: 10.4230/LIPIcs.DISC.2020.3
arXiv: 2008.01009

Singh, Sidak Pal, and Dan-Adrian Alistarh. “WoodFisher: Efficient Second-Order Approximation for Neural Network Compression.” In Advances in Neural Information Processing Systems, 33:18098–109. Curran Associates, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/9632
arXiv: 2004.14340

Aksenov, Vitaly, Dan-Adrian Alistarh, and Janne Korhonen. “Scalable Belief Propagation via Relaxed Scheduling.” In Advances in Neural Information Processing Systems, 33:22361–72. Curran Associates, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/9631
arXiv: 2002.11505

Kurtz, Mark, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William Leiserson, et al. “Inducing and Exploiting Activation Sparsity for Fast Neural Network Inference.” In 37th International Conference on Machine Learning, ICML 2020, 119:5533–43, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/9415

Gurel, Nezihe Merve, Kaan Kara, Alen Stojanov, Tyler Smith, Thomas Lemmin, Dan-Adrian Alistarh, Markus Puschel, and Ce Zhang. “Compressive Sensing Using Iterative Hard Thresholding with Low Precision Data Representation: Theory and Applications.”https://doi.org/10.1109/TSP.2020.3010355__IEEE Transactions on Signal Processing. IEEE, 2020.https://doi.org/10.1109/TSP.2020.3010355 .
LibreCat: https://research-explorer.ista.ac.at/record/8268
DOI: 10.1109/TSP.2020.3010355
WoS: 000562044500001
arXiv: 1802.04907

Li, Shigang, Tal Ben-Nun Tal Ben-Nun, Salvatore Di Girolamo, Dan-Adrian Alistarh, and Torsten Hoefler. “Taming Unbalanced Training Workloads in Deep Learning with Partial Collective Operations.”https://doi.org/10.1145/3332466.3374528__In Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 45–61. Association for Computing Machinery, 2020.https://doi.org/10.1145/3332466.3374528 .
LibreCat: https://research-explorer.ista.ac.at/record/8722
DOI: 10.1145/3332466.3374528
WoS: 000564476500004
arXiv: 1908.04207

Rybicki, Joel, Nerea Abrego, and Otso Ovaskainen. “Habitat Fragmentation and Species Diversity in Competitive Communities.”https://doi.org/10.1111/ele.13450__Ecology Letters. Wiley, 2020.https://doi.org/10.1111/ele.13450 .
LibreCat: https://research-explorer.ista.ac.at/record/7224
DOI: 10.1111/ele.13450
WoS: 000503625200001

Konstantinov, Nikola H, Elias Frantar, Dan-Adrian Alistarh, and Christoph Lampert. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” In Proceedings of the 37th International Conference on Machine Learning, 119:5416–25. ML Research Press, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/8724
arXiv: 2002.10384

Bhatia, Sumit, Bapi Chatterjee, Deepak Nathani, and Manohar Kaul. “A Persistent Homology Perspective to the Link Prediction Problem.”https://doi.org/10.1007/978-3-030-36687-2_3__In Complex Networks and Their Applications VIII, 881:27–39. Springer Nature, 2020.https://doi.org/10.1007/978-3-030-36687-2_3 .
LibreCat: https://research-explorer.ista.ac.at/record/7213
DOI: 10.1007/978-3-030-36687-2_3
WoS: 000843927300003

Czumaj, Artur, Peter Davies, and Merav Parter. “Graph Sparsification for Derandomizing Massively Parallel Computation with Low Space.”https://doi.org/10.1145/3350755.3400282__In Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2020), 175–85. Association for Computing Machinery, 2020.https://doi.org/10.1145/3350755.3400282 .
LibreCat: https://research-explorer.ista.ac.at/record/7802
DOI: 10.1145/3350755.3400282
WoS: 000744436200015
arXiv: 1912.05390

Brown, Trevor A, Aleksandar Prokopec, and Dan-Adrian Alistarh. “Non-Blocking Interpolation Search Trees with Doubly-Logarithmic Running Time.”https://doi.org/10.1145/3332466.3374542__In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 276–91. Association for Computing Machinery, 2020.https://doi.org/10.1145/3332466.3374542 .
LibreCat: https://research-explorer.ista.ac.at/record/7636
DOI: 10.1145/3332466.3374542
WoS: 000564476500020

Alistarh, Dan-Adrian, Trevor A Brown, and Nandini Singhal. “Memory Tagging: Minimalist Synchronization for Scalable Concurrent Data Structures.”https://doi.org/10.1145/3350755.3400213__In Annual ACM Symposium on Parallelism in Algorithms and Architectures, 37–49. Association for Computing Machinery, 2020.https://doi.org/10.1145/3350755.3400213 .
LibreCat: https://research-explorer.ista.ac.at/record/8191
DOI: 10.1145/3350755.3400213
WoS: 000744436200004

Koval, Nikita, Mariia Sokolova, Alexander Fedorov, Dan-Adrian Alistarh, and Dmitry Tsitelov. “Testing Concurrency on the JVM with Lincheck.”https://doi.org/10.1145/3332466.3374503__In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 423–24. Association for Computing Machinery, 2020.https://doi.org/10.1145/3332466.3374503 .
LibreCat: https://research-explorer.ista.ac.at/record/7635
DOI: 10.1145/3332466.3374503

Alistarh, Dan-Adrian, James Aspnes, Faith Ellen, Rati Gelashvili, and Leqi Zhu. “Brief Announcement: Why Extension-Based Proofs Fail.”https://doi.org/10.1145/3382734.3405743__In Proceedings of the 39th Symposium on Principles of Distributed Computing, 54–56. Association for Computing Machinery, 2020.https://doi.org/10.1145/3382734.3405743 .
LibreCat: https://research-explorer.ista.ac.at/record/8383
DOI: 10.1145/3382734.3405743

Brandt, Sebastian, Barbara Keller, Joel Rybicki, Jukka Suomela, and Jara Uitto. “Brief Announcement: Efficient Load-Balancing through Distributed Token Dropping.”https://doi.org/10.4230/LIPIcs.DISC.2020.40__In 34th International Symposium on Distributed Computing, Vol. 179. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.https://doi.org/10.4230/LIPIcs.DISC.2020.40 .
LibreCat: https://research-explorer.ista.ac.at/record/15074
DOI: 10.4230/LIPIcs.DISC.2020.40
arXiv: 2005.07761

Faghri, Fartash , Iman  Tabrizian, Ilia Markov, Dan-Adrian Alistarh, Daniel  Roy, and Ali  Ramezani-Kebrya. “Adaptive Gradient Quantization for Data-Parallel SGD.” In Advances in Neural Information Processing Systems, Vol. 33. Neural Information Processing Systems Foundation, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/15086
arXiv: 2010.12460

Alistarh, Dan-Adrian, Giorgi Nadiradze, and Amirmojtaba Sabour. “Dynamic Averaging Load Balancing on Cycles.”https://doi.org/10.4230/LIPIcs.ICALP.2020.7__In 47th International Colloquium on Automata, Languages, and Programming, Vol. 168. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.https://doi.org/10.4230/LIPIcs.ICALP.2020.7 .
LibreCat: https://research-explorer.ista.ac.at/record/15077
DOI: 10.4230/LIPIcs.ICALP.2020.7
arXiv: 2003.09297

Jelínek, Vít, and Martin Töpfer. “On Grounded L-Graphs and Their Relatives.”https://doi.org/10.37236/8096__Electronic Journal of Combinatorics. Electronic Journal of Combinatorics, 2019.https://doi.org/10.37236/8096 .
LibreCat: https://research-explorer.ista.ac.at/record/6759
DOI: 10.37236/8096
arXiv: 1808.04148

Nowak, Thomas, and Joel Rybicki. “Byzantine Approximate Agreement on Graphs.”https://doi.org/10.4230/LIPICS.DISC.2019.29__In 33rd International Symposium on Distributed Computing, 146:29:1--29:17. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019.https://doi.org/10.4230/LIPICS.DISC.2019.29 .
LibreCat: https://research-explorer.ista.ac.at/record/6931
DOI: 10.4230/LIPICS.DISC.2019.29
arXiv: 1908.02743

Chatterjee, Bapi, Sathya Peri, Muktikanta Sa, and Nandini Singhal. “A Simple and Practical Concurrent Non-Blocking Unbounded Graph with Linearizable Reachability Queries.”https://doi.org/10.1145/3288599.3288617__In ACM International Conference Proceeding Series, 168–77. ACM, 2019.https://doi.org/10.1145/3288599.3288617 .
LibreCat: https://research-explorer.ista.ac.at/record/5947
DOI: 10.1145/3288599.3288617
WoS: 000484491600019
arXiv: 1809.00896

Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. Lock-Free Channels for Programming via Communicating Sequential Processes. Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming. ACM Press, 2019.https://doi.org/10.1145/3293883.3297000 .
LibreCat: https://research-explorer.ista.ac.at/record/6485
DOI: 10.1145/3293883.3297000
WoS: 000587604600044

Ovaskainen, Otso, Joel Rybicki, and Nerea Abrego. “What Can Observational Data Reveal about Metacommunity Processes?”https://doi.org/10.1111/ecog.04444__Ecography. Wiley, 2019.https://doi.org/10.1111/ecog.04444 .
LibreCat: https://research-explorer.ista.ac.at/record/6936
DOI: 10.1111/ecog.04444
WoS: 000486348700001

Lenzen, Christoph, and Joel Rybicki. “Self-Stabilising Byzantine Clock Synchronisation Is Almost as Easy as Consensus.”https://doi.org/10.1145/3339471__Journal of the ACM. ACM, 2019.https://doi.org/10.1145/3339471 .
LibreCat: https://research-explorer.ista.ac.at/record/6972
DOI: 10.1145/3339471
WoS: 000496514100001
arXiv: 1705.06173

Khirirat, Sarit, Mikael Johansson, and Dan-Adrian Alistarh. “Gradient Compression for Communication-Limited Convex Optimization.”https://doi.org/10.1109/cdc.2018.8619625__In 2018 IEEE Conference on Decision and Control. IEEE, 2019.https://doi.org/10.1109/cdc.2018.8619625 .
LibreCat: https://research-explorer.ista.ac.at/record/7122
DOI: 10.1109/cdc.2018.8619625
WoS: 000458114800023

Renggli, Cedric, Saleh Ashkboos, Mehdi Aghagolzadeh, Dan-Adrian Alistarh, and Torsten Hoefler. “SparCML: High-Performance Sparse Communication for Machine Learning.”https://doi.org/10.1145/3295500.3356222__In International Conference for High Performance Computing, Networking, Storage and Analysis, SC. ACM, 2019.https://doi.org/10.1145/3295500.3356222 .
LibreCat: https://research-explorer.ista.ac.at/record/7201
DOI: 10.1145/3295500.3356222
WoS: 000545976800011
arXiv: 1802.08021

Aganezov, Sergey, Ilya Zban, Vitalii Aksenov, Nikita Alexeev, and Michael C. Schatz. “Recovering Rearranged Cancer Chromosomes from Karyotype Graphs.”https://doi.org/10.1186/s12859-019-3208-4__BMC Bioinformatics. BMC, 2019.https://doi.org/10.1186/s12859-019-3208-4 .
LibreCat: https://research-explorer.ista.ac.at/record/7214
DOI: 10.1186/s12859-019-3208-4
WoS: 000511618800007

Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. “Scalable FIFO Channels for Programming via Communicating Sequential Processes.”https://doi.org/10.1007/978-3-030-29400-7_23__In 25th Anniversary of Euro-Par, 11725:317–33. Springer Nature, 2019.https://doi.org/10.1007/978-3-030-29400-7_23 .
LibreCat: https://research-explorer.ista.ac.at/record/7228
DOI: 10.1007/978-3-030-29400-7_23
WoS: 000851061400023

Yu, Chen, Hanlin Tang, Cedric Renggli, Simon Kassing, Ankit Singla, Dan-Adrian Alistarh, Ce Zhang, and Ji Liu. “Distributed Learning over Unreliable Networks.” In 36th International Conference on Machine Learning, ICML 2019, 2019–June:12481–512. IMLS, 2019.
LibreCat: https://research-explorer.ista.ac.at/record/7437
WoS: 000684034307036
arXiv: 1810.07766

Alistarh, Dan-Adrian, Giorgi Nadiradze, and Nikita Koval. “Efficiency Guarantees for Parallel Incremental Algorithms under Relaxed Schedulers.”https://doi.org/10.1145/3323165.3323201__In 31st ACM Symposium on Parallelism in Algorithms and Architectures, 145–54. ACM Press, 2019.https://doi.org/10.1145/3323165.3323201 .
LibreCat: https://research-explorer.ista.ac.at/record/6673
DOI: 10.1145/3323165.3323201
WoS: 000507618500018
arXiv: 2003.09363

Wendler, Chris, Dan-Adrian Alistarh, and Markus Püschel. “Powerset Convolutional Neural Networks,” 32:927–38. Neural Information Processing Systems Foundation, 2019.
LibreCat: https://research-explorer.ista.ac.at/record/7542
WoS: 000534424300084
arXiv: 1909.02253

Foerster, Klaus-Tycho, Janne Korhonen, Joel Rybicki, and Stefan Schmid. “Does Preprocessing Help under Congestion?”https://doi.org/10.1145/3293611.3331581__In Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing, 259–61. ACM, 2019.https://doi.org/10.1145/3293611.3331581 .
LibreCat: https://research-explorer.ista.ac.at/record/6935
DOI: 10.1145/3293611.3331581
WoS: 000570442000037
arXiv: 1905.03012

Alistarh, Dan-Adrian, James Aspnes, Faith Ellen, Rati Gelashvili, and Leqi Zhu. “Why Extension-Based Proofs Fail.”https://doi.org/10.1145/3313276.3316407__In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, 986–96. ACM Press, 2019.https://doi.org/10.1145/3313276.3316407 .
LibreCat: https://research-explorer.ista.ac.at/record/6676
DOI: 10.1145/3313276.3316407
WoS: 000523199100089
arXiv: 1811.01421

Censor-Hillel, Keren, Michal Dory, Janne Korhonen, and Dean Leitersdorf. “Fast Approximate Shortest Paths in the Congested Clique.”https://doi.org/10.1145/3293611.3331633__In Proceedings of the 2019 ACM Symposium on Principles of Distributed Computin, 74–83. ACM, 2019.https://doi.org/10.1145/3293611.3331633 .
LibreCat: https://research-explorer.ista.ac.at/record/6933
DOI: 10.1145/3293611.3331633
WoS: 000570442000011
arXiv: 1903.05956

Alistarh, Dan-Adrian, James Aspnes, Valerie King, and Jared Saia. “Communication-Efficient Randomized Consensus.”https://doi.org/10.1007/s00446-017-0315-1__Distributed Computing. Springer, 2018.https://doi.org/10.1007/s00446-017-0315-1 .
LibreCat: https://research-explorer.ista.ac.at/record/536
DOI: 10.1007/s00446-017-0315-1

Grubic, Demjan, Leo Tam, Dan-Adrian Alistarh, and Ce Zhang. “Synchronous Multi-GPU Training for Deep Learning with Low-Precision Communications: An Empirical Study.”https://doi.org/10.5441/002/EDBT.2018.14__In Proceedings of the 21st International Conference on Extending Database Technology, 145–56. OpenProceedings, 2018.https://doi.org/10.5441/002/EDBT.2018.14 .
LibreCat: https://research-explorer.ista.ac.at/record/7116
DOI: 10.5441/002/EDBT.2018.14

Alistarh, Dan-Adrian, William Leiserson, Alexander Matveev, and Nir Shavit. “ThreadScan: Automatic and Scalable Memory Reclamation.”https://doi.org/10.1145/3201897__ACM Transactions on Parallel Computing. Association for Computing Machinery, 2018.https://doi.org/10.1145/3201897 .
LibreCat: https://research-explorer.ista.ac.at/record/6001
DOI: 10.1145/3201897

Polino, Antonio, Razvan Pascanu, and Dan-Adrian Alistarh. “Model Compression via Distillation and Quantization.” In 6th International Conference on Learning Representations, 2018.
LibreCat: https://research-explorer.ista.ac.at/record/7812
arXiv: 1802.05668

Arbel Raviv, Maya, and Trevor A Brown. “Harnessing Epoch-Based Reclamation for Efficient Range Queries,”https://doi.org/10.1145/3178487.3178489__53:14–27. ACM, 2018.https://doi.org/10.1145/3178487.3178489 .
LibreCat: https://research-explorer.ista.ac.at/record/397
DOI: 10.1145/3178487.3178489
WoS: 000446161100002

Rybicki, Joel, Eva Kisdi, and Jani Anttila. “Model of Bacterial Toxin-Dependent Pathogenesis Explains Infective Dose.”https://doi.org/10.1073/pnas.1721061115__PNAS. National Academy of Sciences, 2018.https://doi.org/10.1073/pnas.1721061115 .
LibreCat: https://research-explorer.ista.ac.at/record/43
DOI: 10.1073/pnas.1721061115
WoS: 000447491300057

Lenzen, Christoph, and Joel Rybicki. “Near-Optimal Self-Stabilising Counting and Firing Squads.”https://doi.org/10.1007/s00446-018-0342-6__Distributed Computing. Springer, 2018.https://doi.org/10.1007/s00446-018-0342-6 .
LibreCat: https://research-explorer.ista.ac.at/record/76
DOI: 10.1007/s00446-018-0342-6
WoS: 000475627800005

Gilad, Eran, Trevor A Brown, Mark Oskin, and Yoav Etsion. “Snapshot Based Synchronization: A Fast Replacement for Hand-over-Hand Locking,”https://doi.org/10.1007/978-3-319-96983-1_33__11014:465–79. Springer, 2018.https://doi.org/10.1007/978-3-319-96983-1_33 .
LibreCat: https://research-explorer.ista.ac.at/record/85
DOI: 10.1007/978-3-319-96983-1_33
WoS: 000851042300031

Alistarh, Dan-Adrian, Christopher De Sa, and Nikola H Konstantinov. “The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory.”https://doi.org/10.1145/3212734.3212763__In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 169–78. ACM Press, 2018.https://doi.org/10.1145/3212734.3212763 .
LibreCat: https://research-explorer.ista.ac.at/record/5962
DOI: 10.1145/3212734.3212763
WoS: 000458186900022
arXiv: 1803.08841

Alistarh, Dan-Adrian. “A Brief Tutorial on Distributed and Concurrent Machine Learning.”https://doi.org/10.1145/3212734.3212798__In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 487–88. ACM Press, 2018.https://doi.org/10.1145/3212734.3212798 .
LibreCat: https://research-explorer.ista.ac.at/record/5961
DOI: 10.1145/3212734.3212798
WoS: 000458186900063

Alistarh, Dan-Adrian, Trevor A Brown, Justin Kopinsky, and Giorgi Nadiradze. “Relaxed Schedulers Can Efficiently Parallelize Iterative Algorithms.”https://doi.org/10.1145/3212734.3212756__In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 377–86. ACM Press, 2018.https://doi.org/10.1145/3212734.3212756 .
LibreCat: https://research-explorer.ista.ac.at/record/5963
DOI: 10.1145/3212734.3212756
WoS: 000458186900048
arXiv: 1808.04155

Alistarh, Dan-Adrian, Trevor A Brown, Justin Kopinsky, Jerry Z. Li, and Giorgi Nadiradze. “Distributionally Linearizable Data Structures.”https://doi.org/10.1145/3210377.3210411__In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, 133–42. ACM Press, 2018.https://doi.org/10.1145/3210377.3210411 .
LibreCat: https://research-explorer.ista.ac.at/record/5965
DOI: 10.1145/3210377.3210411
WoS: 000545269600016
arXiv: 1804.01018

Alistarh, Dan-Adrian, Syed Kamran Haider, Raphael Kübler, and Giorgi Nadiradze. “The Transactional Conflict Problem.”https://doi.org/10.1145/3210377.3210406__In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, 383–92. ACM Press, 2018.https://doi.org/10.1145/3210377.3210406 .
LibreCat: https://research-explorer.ista.ac.at/record/5966
DOI: 10.1145/3210377.3210406
WoS: 000545269600046
arXiv: 1804.00947

Aksenov, Vitaly, Dan-Adrian Alistarh, and Petr Kuznetsov. “Brief Announcement: Performance Prediction for Coarse-Grained Locking.”https://doi.org/10.1145/3212734.3212785__In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 411–13. ACM Press, 2018.https://doi.org/10.1145/3212734.3212785 .
LibreCat: https://research-explorer.ista.ac.at/record/5964
DOI: 10.1145/3212734.3212785
WoS: 000458186900052

Stojanov, Alen, Tyler Michael Smith, Dan-Adrian Alistarh, and Markus Puschel. “Fast Quantized Arithmetic on : Trading Compute for Data Movement.”https://doi.org/10.1109/SiPS.2018.8598402__In 2018 IEEE International Workshop on Signal Processing Systems, Vol. 2018–October. IEEE, 2018.https://doi.org/10.1109/SiPS.2018.8598402 .
LibreCat: https://research-explorer.ista.ac.at/record/6031
DOI: 10.1109/SiPS.2018.8598402
WoS: 000465106800060

Alistarh, Dan-Adrian, James Aspnes, and Rati Gelashvili. “Space-Optimal Majority in Population Protocols.”https://doi.org/10.1137/1.9781611975031.144__In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, 2221–39. ACM, 2018.https://doi.org/10.1137/1.9781611975031.144 .
LibreCat: https://research-explorer.ista.ac.at/record/7123
DOI: 10.1137/1.9781611975031.144
WoS: 000483921200145
arXiv: 1704.04947

Alistarh, Dan-Adrian, Zeyuan Allen-Zhu, and Jerry Li. “Byzantine Stochastic Gradient Descent.” In Advances in Neural Information Processing Systems, 2018:4613–23. Neural Information Processing Systems Foundation, 2018.
LibreCat: https://research-explorer.ista.ac.at/record/6558
WoS: 000461823304061
arXiv: 1803.08917

Alistarh, Dan-Adrian, Torsten Hoefler, Mikael Johansson, Nikola H Konstantinov, Sarit Khirirat, and Cedric Renggli. “The Convergence of Sparsified Gradient Methods.” In Advances in Neural Information Processing Systems 31, Volume 2018:5973–83. Neural Information Processing Systems Foundation, 2018.
LibreCat: https://research-explorer.ista.ac.at/record/6589
WoS: 000461852000047
arXiv: 1809.10505

Baig, Ghufran, Bozidar Radunovic, Dan-Adrian Alistarh, Matthew Balkwill, Thomas Karagiannis, and Lili Qiu. “Towards Unlicensed Cellular Networks in TV White Spaces.”https://doi.org/10.1145/3143361.3143367__In Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies, 2–14. ACM, 2017.https://doi.org/10.1145/3143361.3143367 .
LibreCat: https://research-explorer.ista.ac.at/record/487
DOI: 10.1145/3143361.3143367

Alistarh, Dan-Adrian, Justin Kopinsky, Jerry Li, and Giorgi Nadiradze. “The Power of Choice in Priority Scheduling.”https://doi.org/10.1145/3087801.3087810__In Proceedings of the ACM Symposium on Principles of Distributed Computing, Part 9314:283–92. ACM, 2017.https://doi.org/10.1145/3087801.3087810 .
LibreCat: https://research-explorer.ista.ac.at/record/791
DOI: 10.1145/3087801.3087810
WoS: 000462995000035

Alistarh, Dan-Adrian, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovi. “QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding,” 2017:1710–21. Neural Information Processing Systems Foundation, 2017.
LibreCat: https://research-explorer.ista.ac.at/record/431
arXiv: 1610.02132

Zhang, Hantian, Jerry Li, Kaan Kara, Dan-Adrian Alistarh, Ji Liu, and Ce Zhang. “ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning.” In Proceedings of Machine Learning Research, 70:4035–43. ML Research Press, 2017.
LibreCat: https://research-explorer.ista.ac.at/record/432