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.

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.

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 .

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 .

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 .

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.

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 .

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 .

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 .

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 .

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 .

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 .

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 .

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.

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 .

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 .

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 .

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 .

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 .

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 .