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39 Publications
2025 | Published | Conference Paper | IST-REx-ID: 20256 | 
Henzinger, T. A., Kresse, F., Mallik, K., Yu, E., & Zikelic, D. (2025). Predictive monitoring of black-box dynamical systems. In 7th Annual Learning for Dynamics & Control Conference (Vol. 283, pp. 804–816). Ann Arbor, MI, United States: ML Research Press.
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2025 | Published | Conference Paper | IST-REx-ID: 20299 |
Asadi, A., Chatterjee, K., & De Raaij, J. (2025). Lower bound on Howard policy iteration for deterministic Markov Decision Processes. In The 41st Conference on Uncertainty in Artificial Intelligence (Vol. 286, pp. 223–232). Rio de Janeiro, Brazil: ML Research Press.
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2025 | Published | Conference Paper | IST-REx-ID: 20301 |
Henzinger, M., Sricharan, A. R., & Steiner, T. A. (2025). Differentially private continual release of histograms and related queries. In The 28th International Conference on Artificial Intelligence and Statistics (Vol. 258, pp. 1990–1998). Mai Khao, Thailand: ML Research Press.
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2025 | Published | Conference Paper | IST-REx-ID: 20302 |
Luo, R., Stich, S. U., Horváth, S., & Takáč, M. (2025). Revisiting LocalSGD and SCAFFOLD: Improved rates and missing analysis. In The 28th International Conference on Artificial Intelligence and Statistics (Vol. 258, pp. 2539–2547). Mai Khao, Thailand: ML Research Press.
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2025 | Published | Conference Paper | IST-REx-ID: 20303 |
Huang, S., Pfister, N., & Bowden, J. (2025). Sparse causal effect estimation using two-sample summary statistics in the presence of unmeasured confounding. In The 28th International Conference on Artificial Intelligence and Statistics (Vol. 258, pp. 3394–3402). Mai Khao, Thailand: ML Research Press.
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2025 | Published | Conference Paper | IST-REx-ID: 20300 |
Wegel, T., Kovačević, F., Ţifrea, A., & Yang, F. (2025). Learning Pareto manifolds in high dimensions: How can regularization help? In The 28th International Conference on Artificial Intelligence and Statistics (Vol. 258, pp. 4591–4599). Mai Khao, Thailand: ML Research Press.
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20297 |
Asadi, A., Chatterjee, K., Saona Urmeneta, R. J., & Shafiee, A. (2025). Limit-sure reachability for small memory policies in POMDPs is NP-complete. In The 41st Conference on Uncertainty in Artificial Intelligence (Vol. 286, pp. 238–247). Rio de Janeiro, Brazil: ML Research Press.
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20296 |
Kresse, F., Yu, E., Lampert, C., & Henzinger, T. A. (2025). Logic gate neural networks are good for verification. In 2nd International Conferenceon Neuro-Symbolic Systems (Vol. 288). Philadephia, PA, United States: ML Research Press.
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20298 |
Kalinin, N., & Steinberger, L. (2025). Efficient estimation of a Gaussian mean with local differential privacy. In Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (Vol. 258, pp. 118–126). Mai Khao, Thailand: ML Research Press.
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20592 |
Yao, D., Tronarp, F., & Bosch, N. (2025). Propagating model uncertainty through filtering-based probabilistic numerical ODE solvers. In Proceedings of the 1st International Conference on Probabilistic Numerics (Vol. 271). Sophia Antipolis, France: ML Research Press.
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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.
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20819 |
Scott, J. A., Lampert, C., & Saulpic, D. (2025). Differentially private federated k-means clustering with server-side data. In 42nd International Conference on Machine Learning (Vol. 267, pp. 53757–53790). Vancouver, Canada: ML Research Press.
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20817 |
Pervez, A. A., Gavves, E., & Locatello, F. (2025). Mechanistic PDE networks for discovery of governing equations. In 42nd International Conference on Machine Learning (Vol. 267, pp. 48962–48973). Vancouver, Canada: ML Research Press.
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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.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17093 |
Zakerinia, H., Talaei, S., Nadiradze, G., & Alistarh, D.-A. (2024). Communication-efficient federated learning with data and client heterogeneity. In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 3448–3456). Valencia, Spain: ML Research Press.
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2024 | Published | Conference Paper | IST-REx-ID: 15011 |
Kurtic, E., Hoefler, T., & Alistarh, D.-A. (2024). How to prune your language model: Recovering accuracy on the “Sparsity May Cry” benchmark. In Proceedings of Machine Learning Research (Vol. 234, pp. 542–553). Hongkong, China: ML Research Press.
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| arXiv
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
2024 | Published | Conference Paper | IST-REx-ID: 18114 |
Pervez, A. A., Locatello, F., & Gavves, E. (2024). Mechanistic neural networks for scientific machine learning. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 40484–40501). Vienna, Austria: ML Research Press.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18117 |
Nikdan, M., Tabesh, S., Crncevic, E., & Alistarh, D.-A. (2024). RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 38187–38206). Vienna, Austria: ML Research Press.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18118 |
Zakerinia, H., Behjati, A., & Lampert, C. (2024). More flexible PAC-Bayesian meta-learning by learning learning algorithms. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 58122–58139). Vienna, Austria: ML Research Press.
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