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174 Publications
2026 |
Published |
Conference Paper |
IST-REx-ID: 21932 |
Schultheis, Erik, and Dan-Adrian Alistarh. “LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs.” In 2nd Conference on Parsimony and Learning, 328:265–84. ML Research Press, 2026.
[Published Version]
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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.” In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics, 6404–6418. Association for Computational Linguistics, 2026. https://doi.org/10.18653/v1/2026.eacl-long.301.
[Preprint]
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| DOI
| arXiv
2026 |
Published |
Conference Poster |
IST-REx-ID: 21857 |
Nicolicioiu, Armand, Eugenia B Iofinova, Andrej Jovanovic, Eldar Kurtic, Mahdi Nikdan, Andrei Panferov, Ilia Markov, Nir Shavit, and Dan-Adrian Alistarh. Panza: Investigating the Feasibility of Fully-Local Personalized Text Generation. Third Conference on Parsimony and Learning (Proceedings Track). OpenReview, 2026.
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2026 |
Draft |
Preprint |
IST-REx-ID: 21859 |
Iofinova, Eugenia B, and Dan-Adrian Alistarh. “Behemoth: Benchmarking Unlearning in LLMs Using Fully Synthetic Data.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2601.23153.
[Preprint]
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| arXiv
2026 |
Published |
Journal Article |
IST-REx-ID: 22771 |
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Tuo, Ping, Jiale Chen, and Ju Li. “Flow Matching for Reaction Pathway Generation.” Nature Communications. Springer Nature, 2026. https://doi.org/10.1038/s41467-026-75654-w.
[Published Version]
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| PubMed | Europe PMC
| arXiv
2026 |
Published |
Thesis | PhD |
IST-REx-ID: 21854 |
Iofinova, Eugenia B. “On the Utility and Effects of Efficiency in Artificial Neural Networks.” Institute of Science and Technology Austria, 2026. https://doi.org/10.15479/AT-ISTA-21854.
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2025 |
Published |
Conference Paper |
IST-REx-ID: 20820 |
Sieberling, Oliver, Denis Kuznedelev, Eldar Kurtic, and Dan-Adrian Alistarh. “EvoPress: Accurate Dynamic Model Compression via Evolutionary Search.” In 42nd International Conference on Machine Learning, 267:55556–90. ML Research Press, 2025.
[Published Version]
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 20821 |
Nguyen, Anh Duc, Ilia Markov, Frank Zhengqing Wu, Ali Ramezani-Kebrya, Kimon Antonakopoulos, Dan-Adrian Alistarh, and Volkan Cevher. “Layer-Wise Quantization for Quantized Optimistic Dual Averaging.” In 42nd International Conference on Machine Learning, 267:46026–72. ML Research Press, 2025.
[Published Version]
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 21250 |
Alistarh, Dan-Adrian, Faith Ellen, and Alexander Fedorov. “An Almost-Logarithmic Lower Bound for Leader Election with Bounded Value Contention.” In 39th International Symposium on Distributed Computing, 356:3:1-3:16. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2025. https://doi.org/10.4230/LIPIcs.DISC.2025.3.
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2025 |
Published |
Book Chapter |
IST-REx-ID: 21257 |
Kurtic, Eldar, Denis Kuznedelev, Elias Frantar, Michael Goinv, Shubhra Pandit, Abhinav Agarwalla, Tuan Nguyen, Alexandre Marques, Mark Kurtz, and Dan-Adrian Alistarh. “Sparse Fine-Tuning for Inference Acceleration of Large Language Models.” In Enhancing LLM Performance. Efficacy, Fine-Tuning, and Inference Techniques, edited by Peyman Passban, Andy Way, and Mehdi Rezagholizadeh, 83–97. Springer Nature, 2025. https://doi.org/10.1007/978-3-031-85747-8_6.
[Preprint]
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| arXiv
2025 |
Published |
Journal Article |
IST-REx-ID: 19713 |
Talaei, Shayan, Matin Ansaripour, Giorgi Nadiradze, and Dan-Adrian Alistarh. “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, 2025. https://doi.org/10.1609/aaai.v39i19.34290.
[Preprint]
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 19877 |
Frantar, Elias, Roberto L. Castro, Jiale Chen, Torsten Hoefler, and Dan-Adrian Alistarh. “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, 239–51. Association for Computing Machinery, 2025. https://doi.org/10.1145/3710848.3710871.
[Published Version]
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| DOI
| WoS
| arXiv
2025 |
Published |
Journal Article |
IST-REx-ID: 19969 |
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Alistarh, Dan-Adrian, Joel Rybicki, and Sasha Voitovych. “Near-Optimal Leader Election in Population Protocols on Graphs.” Distributed Computing. Springer Nature, 2025. https://doi.org/10.1007/s00446-025-00487-7.
[Published Version]
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| WoS
| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 20032 |
Chen, Jiale, Dingling Yao, Adeel A Pervez, Dan-Adrian Alistarh, and Francesco Locatello. “Scalable Mechanistic Neural Networks.” In 13th International Conference on Learning Representations, 63716–37. ICLR, 2025.
[Published Version]
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 20034 |
Robert, Thomas, Mher Safaryan, Ionut-Vlad Modoranu, and Dan-Adrian Alistarh. “LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics.” In 13th International Conference on Learning Representations, 101877–913. ICLR, 2025.
[Published Version]
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 20037 |
Sawmya, Shashata, Linghao Kong, Ilia Markov, Dan-Adrian Alistarh, and Nir Shavit. “Wasserstein Distances, Neuronal Entanglement, and Sparsity.” In 13th International Conference on Learning Representations, 26244–74. ICLR, 2025.
[Published Version]
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 20038 |
Jin, Tian, Ahmed Imtiaz Humayun, Utku Evci, Suvinay Subramanian, Amir Yazdanbakhsh, Dan-Adrian Alistarh, and Gintare Karolina Dziugaite. “The Journey Matters: Average Parameter Count over Pre-Training Unifies Sparse and Dense Scaling Laws.” In 13th International Conference on Learning Representations, 85165–81. ICLR, 2025.
[Published Version]
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| arXiv
2025 |
Published |
Conference Paper |
IST-REx-ID: 20224 |
Martynov, Pavel, Maxim Buzdalov, Sergei Pankratov, Vitaliy Aksenov, and Stefan Schmid. “In the Search of Optimal Tree Networks: Hardness and Heuristics.” In Proceedings of the 2025 Genetic and Evolutionary Computation Conference, 249–57. Association for Computing Machinery, 2025. https://doi.org/10.1145/3712256.3726425.
[Published Version]
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| WoS
2025 |
Published |
Conference Paper |
IST-REx-ID: 20684 |
Kurtic, Eldar, Alexandre Marques, Shubhra Pandit, Mark Kurtz, and Dan-Adrian Alistarh. “‘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, 26872–86. Association for Computational Linguistics, 2025.
[Published Version]
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| arXiv
2025 |
Draft |
Preprint |
IST-REx-ID: 21858 |
Iofinova, Eugenia B, Andrej Jovanovic, and Dan-Adrian Alistarh. “Position: It’s Time to Act on the Risk of Efficient Personalized Text Generation.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2502.06560.
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| arXiv