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34 Publications
2025 | Published | Conference Paper | IST-REx-ID: 20256 | 
T. A. Henzinger, F. Kresse, K. Mallik, E. Yu, and D. Zikelic, “Predictive monitoring of black-box dynamical systems,” in 7th Annual Learning for Dynamics & Control Conference, Ann Arbor, MI, United States, 2025, vol. 283, pp. 804–816.
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20299 |
A. Asadi, K. Chatterjee, and J. De Raaij, “Lower bound on Howard policy iteration for deterministic Markov Decision Processes,” in The 41st Conference on Uncertainty in Artificial Intelligence, Rio de Janeiro, Brazil, 2025, vol. 286, pp. 223–232.
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20301 |
M. Henzinger, A. R. Sricharan, and T. A. Steiner, “Differentially private continual release of histograms and related queries,” in The 28th International Conference on Artificial Intelligence and Statistics, Mai Khao, Thailand, 2025, vol. 258, pp. 1990–1998.
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2025 | Published | Conference Paper | IST-REx-ID: 20302 |
R. Luo, S. U. Stich, S. Horváth, and M. Takáč, “Revisiting LocalSGD and SCAFFOLD: Improved rates and missing analysis,” in The 28th International Conference on Artificial Intelligence and Statistics, Mai Khao, Thailand, 2025, vol. 258, pp. 2539–2547.
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20303 |
S. Huang, N. Pfister, and J. Bowden, “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, Mai Khao, Thailand, 2025, vol. 258, pp. 3394–3402.
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20300 |
T. Wegel, F. Kovačević, A. Ţifrea, and F. Yang, “Learning Pareto manifolds in high dimensions: How can regularization help?,” in The 28th International Conference on Artificial Intelligence and Statistics, Mai Khao, Thailand, 2025, vol. 258, pp. 4591–4599.
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20297 |
A. Asadi, K. Chatterjee, R. J. Saona Urmeneta, and A. Shafiee, “Limit-sure reachability for small memory policies in POMDPs is NP-complete,” in The 41st Conference on Uncertainty in Artificial Intelligence, Rio de Janeiro, Brazil, 2025, vol. 286, pp. 238–247.
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20296 |
F. Kresse, E. Yu, C. Lampert, and T. A. Henzinger, “Logic gate neural networks are good for verification,” in 2nd International Conferenceon Neuro-Symbolic Systems, Philadephia, PA, United States, 2025, vol. 288.
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| arXiv
2025 | Published | Conference Paper | IST-REx-ID: 20298 |
N. Kalinin and L. Steinberger, “Efficient estimation of a Gaussian mean with local differential privacy,” in Proceedings of the 28th International Conference on Artificial Intelligence and Statistics, Mai Khao, Thailand, 2025, vol. 258, pp. 118–126.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 17093 |
H. Zakerinia, S. Talaei, G. Nadiradze, and D.-A. Alistarh, “Communication-efficient federated learning with data and client heterogeneity,” in Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 2024, vol. 238, pp. 3448–3456.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 15011 |
E. Kurtic, T. Hoefler, and D.-A. Alistarh, “How to prune your language model: Recovering accuracy on the ‘Sparsity May Cry’ benchmark,” in Proceedings of Machine Learning Research, Hongkong, China, 2024, vol. 234, pp. 542–553.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18113 |
V. Egiazarian, A. Panferov, D. Kuznedelev, E. Frantar, A. Babenko, and D.-A. Alistarh, “Extreme compression of large language models via additive quantization,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 12284–12303.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18114 |
A. A. Pervez, F. Locatello, and E. Gavves, “Mechanistic neural networks for scientific machine learning,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 40484–40501.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18117 |
M. Nikdan, S. Tabesh, E. Crncevic, and D.-A. Alistarh, “RoSA: Accurate parameter-efficient fine-tuning via robust adaptation,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 38187–38206.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18118 |
H. Zakerinia, A. Behjati, and C. Lampert, “More flexible PAC-Bayesian meta-learning by learning learning algorithms,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 58122–58139.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18120 |
J. A. Scott and Á. Cahill, “Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 44012–44037.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18975 |
I.-V. Modoranu, A. Kalinov, E. Kurtic, E. Frantar, and D.-A. Alistarh, “Error feedback can accurately compress preconditioners,” in 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 35910–35933.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18971 |
R. Arefin et al., “Unsupervised concept discovery mitigates spurious correlations,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 1672–1688.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18976 |
R. Islamov, M. Safaryan, and D.-A. Alistarh, “AsGrad: A sharp unified analysis of asynchronous-SGD algorithms,” in Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 2024, vol. 238, pp. 649–657.
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| arXiv
2024 | Published | Conference Paper | IST-REx-ID: 18973 |
S. Bombari and M. Mondelli, “Towards understanding the word sensitivity of attention layers: A study via random features,” in 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 4300–4328.
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| arXiv