Near-optimal leader election in population protocols on graphs
D.-A. Alistarh, J. Rybicki, S. Voitovych, Distributed Computing 38 (2025) 207–245.

Hybrid decentralized optimization: Leveraging both first- and zeroth-order optimizers for faster convergence
S. Talaei, M. Ansaripour, G. Nadiradze, D.-A. Alistarh, Proceedings of the 39th AAAI Conference on Artificial Intelligence 39 (2025) 20778–20786.

Compressing large neural networks : Algorithms, systems and scaling laws
E. Frantar, Compressing Large Neural Networksf: Algorithms, Systems and Scaling Laws, Institute of Science and Technology Austria, 2024.

Communication-efficient distributed training of deep neural networks : An algorithms and systems perspective
I. Markov, Communication-Efficient Distributed Training of Deep Neural Networksf: An Algorithms and Systems Perspective, Institute of Science and Technology Austria, 2024.

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.

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.

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

CrAM: A Compression-Aware Minimizer
A. Krumes, A. Vladu, E. Kurtic, C. Lampert, D.-A. Alistarh, in:, 11th International Conference on Learning Representations , OpenReview, 2023.

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

Sinkless Orientation Made Simple
A. Balliu, J. Korhonen, F. Kuhn, H. Lievonen, D. Olivetti, S. Pai, A. Paz, J. Rybicki, S. Schmid, J. Studený, J. Suomela, J. Uitto, in:, Symposium on Simplicity in Algorithms, 2023 Society for Industrial and Applied Mathematics, 2023, pp. 175–191.

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

OPTQ: Accurate post-training quantization for generative pre-trained transformers
E. Frantar, S. Ashkboos, T. Hoefler, D.-A. Alistarh, in:, 11th International Conference on Learning Representations , International Conference on Learning Representations, 2023.

Near-optimal leader election in population protocols on graphs
D.-A. Alistarh, J. Rybicki, S. Voitovych, in:, Proceedings of the Annual ACM Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2022, pp. 246–256.

How well do sparse ImageNet models transfer?
E.B. Iofinova, A. Krumes, 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.

Multi-queues can be state-of-the-art priority schedulers
A. Postnikova, N. Koval, G. Nadiradze, D.-A. Alistarh, in:, Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2022, pp. 353–367.

Beyond distributed subgraph detection: Induced subgraphs, multicolored problems and graph parameters
A. Nikabadi, J. Korhonen, in:, Q. Bramas, V. Gramoli, A. Milani (Eds.), 25th International Conference on Principles of Distributed Systems, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022.

Fast graphical population protocols
D.-A. Alistarh, R. Gelashvili, J. Rybicki, in:, Q. Bramas, V. Gramoli, A. Milani (Eds.), 25th International Conference on Principles of Distributed Systems, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022.

SPDY: Accurate pruning with speedup guarantees
E. Frantar, D.-A. Alistarh, in:, 39th International Conference on Machine Learning, ML Research Press, 2022, pp. 6726–6743.