How to prune your language model: Recovering accuracy on the "Sparsity May Cry" benchmark
E. Kurtic, T. Hoefler, D.-A. Alistarh, in:, Proceedings of Machine Learning Research, ML Research Press, 2024, pp. 542–553.

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

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

Fast and scalable channels in Kotlin Coroutines
N. Koval, D.-A. Alistarh, R. Elizarov, in:, Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2023, pp. 107–118.

Unexpected scaling in path copying trees
V. Aksenov, T.A. Brown, A. Fedorov, I. Kokorin, Unexpected Scaling in Path Copying Trees, Association for Computing Machinery, 2023.

CrAM: A Compression-Aware Minimizer
E.-A. Peste, A. Vladu, E. Kurtic, C. Lampert, D.-A. Alistarh, in:, 11th International Conference on Learning Representations , n.d.

Efficiency and generalization of sparse neural networks
E.-A. Peste, Efficiency and Generalization of Sparse Neural Networks, Institute of Science and Technology Austria, 2023.

CQS: A formally-verified framework for fair and abortable synchronization
N. Koval, D. Khalanskiy, D.-A. Alistarh, Proceedings of the ACM on Programming Languages 7 (2023).

Provably-efficient and internally-deterministic parallel Union-Find
A. Fedorov, D. Hashemi, G. Nadiradze, D.-A. Alistarh, in:, Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures, Association for Computing Machinery, 2023, pp. 261–271.

On biased compression for distributed learning
A. Beznosikov, S. Horvath, P. Richtarik, M. Safaryan, Journal of Machine Learning Research 24 (2023) 1–50.

Lincheck: A practical framework for testing concurrent data structures on JVM
N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, D.-A. Alistarh, (2023).

Lincheck: A practical framework for testing concurrent data structures on JVM
N. Koval, A. Fedorov, M. Sokolova, D. Tsitelov, D.-A. Alistarh, in:, 35th International Conference on Computer Aided Verification , Springer Nature, 2023, pp. 156–169.

Why extension-based proofs fail
D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, L. Zhu, SIAM Journal on Computing 52 (2023) 913–944.

SparseGPT: Massive language models can be accurately pruned in one-shot
E. Frantar, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 10323–10337.

Fundamental limits of two-layer autoencoders, and achieving them with gradient methods
A. Shevchenko, K. Kögler, H. Hassani, M. Mondelli, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 31151–31209.

SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge
M. Nikdan, T. Pegolotti, E.B. Iofinova, E. Kurtic, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 26215–26227.

Quantized distributed training of large models with convergence guarantees
I. Markov, A. Vladu, Q. Guo, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 24020–24044.

Bias in pruned vision models: In-depth analysis and countermeasures
E.B. Iofinova, E.-A. Peste, D.-A. Alistarh, in:, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2023, pp. 24364–24373.

Brief announcement: Temporal locality in online algorithms
M. Pacut, M. Parham, J. Rybicki, S. Schmid, J. Suomela, A. Tereshchenko, in:, 36th International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022.

How well do sparse ImageNet models transfer?
E.B. Iofinova, E.-A. Peste, M. Kurtz, D.-A. Alistarh, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 12256–12266.