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173 Publications


2026 | Published | Conference Paper | IST-REx-ID: 21932 | OA
Schultheis, Erik, and Dan-Adrian Alistarh. “LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs.” 2nd Conference on Parsimony and Learning, vol. 328, ML Research Press, 2026, pp. 265–84.
[Published Version] View | Files available
 

2026 | Published | Conference Paper | IST-REx-ID: 22302
Pankratov, Sergei, and Dan-Adrian Alistarh. “Speculative Decoding Speed-of-Light: Optimal Lower Bounds via Branching Random Walks.” Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics, Association for Computational Linguistics, 2026, pp. 6404–6418, doi:10.18653/v1/2026.eacl-long.301.
[Preprint] View | DOI | arXiv
 

2026 | Published | Thesis | PhD | IST-REx-ID: 21854 | OA
Iofinova, Eugenia B. On the Utility and Effects of Efficiency in Artificial Neural Networks. Institute of Science and Technology Austria, 2026, doi:10.15479/AT-ISTA-21854.
[Published Version] View | Files available | DOI
 

2026 | Published | Conference Poster | IST-REx-ID: 21857 | OA
Nicolicioiu, Armand, et al. “Panza: Investigating the Feasibility of Fully-Local Personalized Text Generation.” Third Conference on Parsimony and Learning (Proceedings Track), 81, OpenReview, 2026.
[Accepted Version] View | Files available | Download Accepted Version (ext.)
 

2026 | Draft | Preprint | IST-REx-ID: 21859 | OA
Iofinova, Eugenia B., and Dan-Adrian Alistarh. “Behemoth: Benchmarking Unlearning in LLMs Using Fully Synthetic Data.” ArXiv, doi:10.48550/arXiv.2601.23153.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 

2026 | Published | Journal Article | IST-REx-ID: 22771 | OA | PlanS
Tuo, Ping, et al. “Flow Matching for Reaction Pathway Generation.” Nature Communications, vol. 17, 8769, Springer Nature, 2026, doi:10.1038/s41467-026-75654-w.
[Published Version] View | Files available | DOI | PubMed | Europe PMC | arXiv
 

2025 | Published | Conference Paper | IST-REx-ID: 20820 | OA
Sieberling, Oliver, et al. “EvoPress: Accurate Dynamic Model Compression via Evolutionary Search.” 42nd International Conference on Machine Learning, vol. 267, ML Research Press, 2025, pp. 55556–90.
[Published Version] View | Files available | arXiv
 

2025 | Published | Conference Paper | IST-REx-ID: 20821 | OA
Nguyen, Anh Duc, et al. “Layer-Wise Quantization for Quantized Optimistic Dual Averaging.” 42nd International Conference on Machine Learning, vol. 267, ML Research Press, 2025, pp. 46026–72.
[Published Version] View | Files available | arXiv
 

2025 | Published | Conference Paper | IST-REx-ID: 21250 | OA
Alistarh, Dan-Adrian, et al. “An Almost-Logarithmic Lower Bound for Leader Election with Bounded Value Contention.” 39th International Symposium on Distributed Computing, vol. 356, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2025, p. 3:1-3:16, doi:10.4230/LIPIcs.DISC.2025.3.
[Published Version] View | Files available | DOI
 

2025 | Published | Book Chapter | IST-REx-ID: 21257 | OA
Kurtic, Eldar, et al. “Sparse Fine-Tuning for Inference Acceleration of Large Language Models.” Enhancing LLM Performance. Efficacy, Fine-Tuning, and Inference Techniques, edited by Peyman Passban et al., Springer Nature, 2025, pp. 83–97, doi:10.1007/978-3-031-85747-8_6.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 

2025 | Published | Journal Article | IST-REx-ID: 19713 | OA
Talaei, Shayan, et al. “Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence.” Proceedings of the 39th AAAI Conference on Artificial Intelligence, vol. 39, no. 19, Association for the Advancement of Artificial Intelligence, 2025, pp. 20778–86, doi:10.1609/aaai.v39i19.34290.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 

2025 | Published | Conference Paper | IST-REx-ID: 19877 | OA
Frantar, Elias, et al. “MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models.” Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, Association for Computing Machinery, 2025, pp. 239–51, doi:10.1145/3710848.3710871.
[Published Version] View | Files available | DOI | WoS | arXiv
 

2025 | Published | Journal Article | IST-REx-ID: 19969 | OA | PlanS
Alistarh, Dan-Adrian, et al. “Near-Optimal Leader Election in Population Protocols on Graphs.” Distributed Computing, vol. 38, Springer Nature, 2025, pp. 207–45, doi:10.1007/s00446-025-00487-7.
[Published Version] View | Files available | DOI | WoS | arXiv
 

2025 | Published | Conference Paper | IST-REx-ID: 20032 | OA
Chen, Jiale, et al. “Scalable Mechanistic Neural Networks.” 13th International Conference on Learning Representations, ICLR, 2025, pp. 63716–37.
[Published Version] View | Files available | arXiv
 

2025 | Published | Conference Paper | IST-REx-ID: 20034 | OA
Robert, Thomas, et al. “LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics.” 13th International Conference on Learning Representations, ICLR, 2025, pp. 101877–913.
[Published Version] View | Files available | arXiv
 

2025 | Published | Conference Paper | IST-REx-ID: 20037 | OA
Sawmya, Shashata, et al. “Wasserstein Distances, Neuronal Entanglement, and Sparsity.” 13th International Conference on Learning Representations, ICLR, 2025, pp. 26244–74.
[Published Version] View | Files available | arXiv
 

2025 | Published | Conference Paper | IST-REx-ID: 20038 | OA
Jin, Tian, et al. “The Journey Matters: Average Parameter Count over Pre-Training Unifies Sparse and Dense Scaling Laws.” 13th International Conference on Learning Representations, ICLR, 2025, pp. 85165–81.
[Published Version] View | Files available | arXiv
 

2025 | Published | Conference Paper | IST-REx-ID: 20224 | OA
Martynov, Pavel, et al. “In the Search of Optimal Tree Networks: Hardness and Heuristics.” Proceedings of the 2025 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, 2025, pp. 249–57, doi:10.1145/3712256.3726425.
[Published Version] View | Files available | DOI | WoS
 

2025 | Published | Conference Paper | IST-REx-ID: 20684 | OA
Kurtic, Eldar, et al. “‘Give Me BF16 or Give Me Death’? Accuracy-Performance Trade-Offs in LLM Quantization.” Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, 2025, pp. 26872–86.
[Published Version] View | Files available | arXiv
 

2025 | Draft | Preprint | IST-REx-ID: 21858 | OA
Iofinova, Eugenia B., et al. “Position: It’s Time to Act on the Risk of Efficient Personalized Text Generation.” ArXiv, doi:10.48550/arXiv.2502.06560.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 

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