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168 Publications
2026 |
Published |
Thesis | PhD |
IST-REx-ID: 21854 |
Iofinova, E. B. (2026). On the utility and effects of efficiency in artificial neural networks. Institute of Science and Technology Austria. https://doi.org/10.15479/AT-ISTA-21854
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2026 |
Published |
Conference Poster |
IST-REx-ID: 21857 |
Nicolicioiu, A., Iofinova, E. B., Jovanovic, A., Kurtic, E., Nikdan, M., Panferov, A., … Alistarh, D.-A. (2026). Panza: Investigating the feasibility of fully-local personalized text generation. Third Conference on Parsimony and Learning (Proceedings Track). Tübíngen, Germany: OpenReview.
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2026 |
Draft |
Preprint |
IST-REx-ID: 21859 |
Iofinova, E. B., & Alistarh, D.-A. (n.d.). Behemoth: Benchmarking unlearning in LLMs using fully synthetic data. arXiv. https://doi.org/10.48550/arXiv.2601.23153
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 19877 |
Frantar, E., Castro, R. L., Chen, J., Hoefler, T., & Alistarh, D.-A. (2025). MARLIN: Mixed-precision auto-regressive parallel inference on Large Language Models. In Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (pp. 239–251). Las Vegas, NV, United States: Association for Computing Machinery. https://doi.org/10.1145/3710848.3710871
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| arXiv
2025 |
Published |
Journal Article |
IST-REx-ID: 19969 |
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Alistarh, D.-A., Rybicki, J., & Voitovych, S. (2025). Near-optimal leader election in population protocols on graphs. Distributed Computing. Springer Nature. https://doi.org/10.1007/s00446-025-00487-7
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 20032 |
Chen, J., Yao, D., Pervez, A. A., Alistarh, D.-A., & Locatello, F. (2025). Scalable mechanistic neural networks. In 13th International Conference on Learning Representations (pp. 63716–63737). Singapore, Singapore: ICLR.
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 20034 |
Robert, T., Safaryan, M., Modoranu, I.-V., & Alistarh, D.-A. (2025). LDAdam: Adaptive optimization from low-dimensional gradient statistics. In 13th International Conference on Learning Representations (pp. 101877–101913). Singapore, Singapore: ICLR.
[Published Version]
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 20037 |
Sawmya, S., Kong, L., Markov, I., Alistarh, D.-A., & Shavit, N. (2025). Wasserstein distances, neuronal entanglement, and sparsity. In 13th International Conference on Learning Representations (pp. 26244–26274). Singapore, Singapore: ICLR.
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 20038 |
Jin, T., Humayun, A. I., Evci, U., Subramanian, S., Yazdanbakhsh, A., Alistarh, D.-A., & Dziugaite, G. K. (2025). The journey matters: Average parameter count over pre-training unifies sparse and dense scaling laws. In 13th International Conference on Learning Representations (pp. 85165–85181). Singapore, Singapore: ICLR.
[Published Version]
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 20224 |
Martynov, P., Buzdalov, M., Pankratov, S., Aksenov, V., & Schmid, S. (2025). In the search of optimal tree networks: Hardness and heuristics. In Proceedings of the 2025 Genetic and Evolutionary Computation Conference (pp. 249–257). Malaga, Spain: Association for Computing Machinery. https://doi.org/10.1145/3712256.3726425
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| WoS
2025 |
Published |
Conference Paper |
IST-REx-ID: 20684 |
Kurtic, E., Marques, A., Pandit, S., Kurtz, M., & Alistarh, D.-A. (2025). “Give me BF16 or give me death”? Accuracy-performance trade-offs in LLM quantization. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (pp. 26872–26886). Vienna, Austria: Association for Computational Linguistics.
[Published Version]
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| arXiv
2025 |
Published |
Journal Article |
IST-REx-ID: 20704
Tuo, P., Zeng, Z., Chen, J., & Cheng, B. (2025). Scalable multitemperature free energy sampling of classical Ising spin states. Journal of Chemical Theory and Computation. American Chemical Society. https://doi.org/10.1021/acs.jctc.5c01248
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| PubMed | Europe PMC
2025 |
Published |
Journal Article |
IST-REx-ID: 19713 |
Talaei, S., Ansaripour, M., Nadiradze, G., & Alistarh, D.-A. (2025). Hybrid decentralized optimization: Leveraging both first- and zeroth-order optimizers for faster convergence. Proceedings of the 39th AAAI Conference on Artificial Intelligence. Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v39i19.34290
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 20820 |
Sieberling, O., Kuznedelev, D., Kurtic, E., & Alistarh, D.-A. (2025). EvoPress: Accurate dynamic model compression via evolutionary search. In 42nd International Conference on Machine Learning (Vol. 267, pp. 55556–55590). Vancouver, Canada: ML Research Press.
[Published Version]
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 20821 |
Nguyen, A. D., Markov, I., Wu, F. Z., Ramezani-Kebrya, A., Antonakopoulos, K., Alistarh, D.-A., & Cevher, V. (2025). Layer-wise quantization for quantized optimistic dual averaging. In 42nd International Conference on Machine Learning (Vol. 267, pp. 46026–46072). Vancouver, Canada: ML Research Press.
[Published Version]
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 21250 |
Alistarh, D.-A., Ellen, F., & Fedorov, A. (2025). An almost-logarithmic lower bound for leader election with bounded value contention. In 39th International Symposium on Distributed Computing (Vol. 356, p. 3:1-3:16). Berlin, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.DISC.2025.3
[Published Version]
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2025 |
Published |
Book Chapter |
IST-REx-ID: 21257 |
Kurtic, E., Kuznedelev, D., Frantar, E., Goinv, M., Pandit, S., Agarwalla, A., … Alistarh, D.-A. (2025). Sparse Fine-Tuning for Inference Acceleration of Large Language Models. In P. Passban, A. Way, & M. Rezagholizadeh (Eds.), Enhancing LLM Performance. Efficacy, Fine-Tuning, and Inference Techniques (pp. 83–97). Springer Nature. https://doi.org/10.1007/978-3-031-85747-8_6
[Preprint]
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| arXiv
2025 |
Draft |
Preprint |
IST-REx-ID: 21858 |
Iofinova, E. B., Jovanovic, A., & Alistarh, D.-A. (n.d.). Position: It’s time to act on the risk of efficient personalized text generation. arXiv. https://doi.org/10.48550/arXiv.2502.06560
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| arXiv
2024 |
Published |
Conference Paper |
IST-REx-ID: 18070
Chatterjee, B., Kungurtsev, V., & Alistarh, D.-A. (2024). Federated SGD with local asynchrony. In Proceedings of the 44th International Conference on Distributed Computing Systems (pp. 857–868). Jersey City, NJ, United States: IEEE. https://doi.org/10.1109/ICDCS60910.2024.00084
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| WoS
2024 |
Published |
Conference Paper |
IST-REx-ID: 18113 |
Egiazarian, V., Panferov, A., Kuznedelev, D., Frantar, E., Babenko, A., & Alistarh, D.-A. (2024). Extreme compression of large language models via additive quantization. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 12284–12303). Vienna, Austria: ML Research Press.
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| arXiv