Predictive monitoring of black-box dynamical systems
T.A. Henzinger, F. Kresse, K. Mallik, E. Yu, D. Zikelic, in:, 7th Annual Learning for Dynamics &; Control Conference, ML Research Press, 2025, pp. 804–816.

Lower bound on Howard policy iteration for deterministic Markov Decision Processes
A. Asadi, K. Chatterjee, J. De Raaij, in:, The 41st Conference on Uncertainty in Artificial Intelligence, ML Research Press, 2025, pp. 223–232.

Differentially private continual release of histograms and related queries
M. Henzinger, A.R. Sricharan, T.A. Steiner, in:, The 28th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2025, pp. 1990–1998.

Revisiting LocalSGD and SCAFFOLD: Improved rates and missing analysis
R. Luo, S.U. Stich, S. Horváth, M. Takád, in:, The 28th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2025, pp. 2539–2547.

Sparse causal effect estimation using two-sample summary statistics in the presence of unmeasured confounding
S. Huang, N. Pfister, J. Bowden, in:, The 28th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2025, pp. 3394–3402.

Learning Pareto manifolds in high dimensions: How can regularization help?
T. Wegel, F. Kovadevi, A. ifrea, F. Yang, in:, The 28th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2025, pp. 4591–4599.

Limit-sure reachability for small memory policies in POMDPs is NP-complete
A. Asadi, K. Chatterjee, R.J. Saona Urmeneta, A. Shafiee, in:, The 41st Conference on Uncertainty in Artificial Intelligence, ML Research Press, 2025, pp. 238–247.

Logic gate neural networks are good for verification
F. Kresse, E. Yu, C. Lampert, T.A. Henzinger, in:, 2nd International Conferenceon Neuro-Symbolic Systems, ML Research Press, 2025.

Efficient estimation of a Gaussian mean with local differential privacy
N. Kalinin, L. Steinberger, in:, Proceedings of the 28th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2025, pp. 118–126.

Propagating model uncertainty through filtering-based probabilistic numerical ODE solvers
D. Yao, F. Tronarp, N. Bosch, in:, Proceedings of the 1st International Conference on Probabilistic Numerics, ML Research Press, 2025.

Layer-wise quantization for quantized optimistic dual averaging
A.D. Nguyen, I. Markov, F.Z. Wu, A. Ramezani-Kebrya, K. Antonakopoulos, D.-A. Alistarh, V. Cevher, in:, 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 46026–46072.

Differentially private federated k-means clustering with server-side data
J.A. Scott, C. Lampert, D. Saulpic, in:, 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 53757–53790.

Mechanistic PDE networks for discovery of governing equations
A.A. Pervez, E. Gavves, F. Locatello, in:, 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 48962–48973.

EvoPress: Accurate dynamic model compression via evolutionary search
O. Sieberling, D. Kuznedelev, E. Kurtic, D.-A. Alistarh, in:, 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 55556–55590.

Communication-efficient federated learning with data and client heterogeneity
H. Zakerinia, S. Talaei, G. Nadiradze, D.-A. Alistarh, in:, Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2024, pp. 3448–3456.

How to prune your language model: Recovering accuracy on the "Sparsity May Cry" benchmark
E. Kurtic, T. Hoefler, D.-A. Alistarh, in:, Proceedings of Machine Learning Research, ML Research Press, 2024, pp. 542–553.

Extreme compression of large language models via additive quantization
V. Egiazarian, A. Panferov, D. Kuznedelev, E. Frantar, A. Babenko, D.-A. Alistarh, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 12284–12303.

Mechanistic neural networks for scientific machine learning
A.A. Pervez, F. Locatello, E. Gavves, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 40484–40501.

RoSA: Accurate parameter-efficient fine-tuning via robust adaptation
M. Nikdan, S. Tabesh, E. Crncevic, D.-A. Alistarh, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 38187–38206.

More flexible PAC-Bayesian meta-learning by learning learning algorithms
H. Zakerinia, A. Behjati, C. Lampert, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 58122–58139.