Hybrid decentralized optimization: Leveraging both first- and zeroth-order optimizers for faster convergence
S. Talaei, M. Ansaripour, G. Nadiradze, D.-A. Alistarh, Proceedings of The39th AAAI Conference on Artificial Intelligence 39 (2025) 20778–20786.

Communication-efficient federated learning with data and client heterogeneity
H. Zakerinia, S. Talaei, G. Nadiradze, D.-A. Alistarh, in:, Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2024, pp. 3448–3456.

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.

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.

Multi-queues can be state-of-the-art priority schedulers
A. Postnikova, N. Koval, G. Nadiradze, D.-A. Alistarh, (2022).

Dynamic averaging load balancing on cycles
D.-A. Alistarh, G. Nadiradze, A. Sabour, Algorithmica 84 (2022) 1007–1029.

Asynchronous decentralized SGD with quantized and local updates
G. Nadiradze, A. Sabour, P. Davies, S. Li, D.-A. Alistarh, in:, 35th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2021.

Elastic consistency: A practical consistency model for distributed stochastic gradient descent
G. Nadiradze, I. Markov, B. Chatterjee, V. Kungurtsev, D.-A. Alistarh, in:, Proceedings of the AAAI Conference on Artificial Intelligence, 2021, pp. 9037–9045.

Lower bounds for shared-memory leader election under bounded write contention
D.-A. Alistarh, R. Gelashvili, G. Nadiradze, in:, 35th International Symposium on Distributed Computing, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021.

On achieving scalability through relaxation
G. Nadiradze, On Achieving Scalability through Relaxation, Institute of Science and Technology Austria, 2021.

Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging
S. Li, T.B.-N. Tal Ben-Nun, G. Nadiradze, S.D. Girolamo, N. Dryden, D.-A. Alistarh, T. Hoefler, IEEE Transactions on Parallel and Distributed Systems 32 (2021).

Dynamic averaging load balancing on cycles
D.-A. Alistarh, G. Nadiradze, A. Sabour, in:, 47th International Colloquium on Automata, Languages, and Programming, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020.

Efficiency guarantees for parallel incremental algorithms under relaxed schedulers
D.-A. Alistarh, G. Nadiradze, N. Koval, in:, 31st ACM Symposium on Parallelism in Algorithms and Architectures, ACM, 2019, pp. 145–154.

The power of choice in priority scheduling
D.-A. Alistarh, J. Kopinsky, J. Li, G. Nadiradze, in:, Proceedings of the ACM Symposium on Principles of Distributed Computing, ACM, 2017, pp. 283–292.