Kurtic E, Hoefler T, Alistarh D-A. How to prune your language model: Recovering accuracy on the “Sparsity May Cry” benchmark. In: Proceedings of Machine Learning Research. Vol 234. ML Research Press; 2024:542-553.
LibreCat: https://research-explorer.ista.ac.at/record/15011
arXiv: 2312.13547

Markov I, Vladu A, Guo Q, Alistarh D-A. Quantized distributed training of large models with convergence guarantees. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:24020-24044.
LibreCat: https://research-explorer.ista.ac.at/record/14461
arXiv: 2302.02390

Fichtenberger H, Henzinger MH, Upadhyay J. Constant matters: Fine-grained error bound on differentially private continual observation. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:10072-10092.
LibreCat: https://research-explorer.ista.ac.at/record/14462

Shevchenko A, Kögler K, Hassani H, Mondelli M. Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:31151-31209.
LibreCat: https://research-explorer.ista.ac.at/record/14459
arXiv: 2212.13468

Nikdan M, Pegolotti T, Iofinova EB, Kurtic E, Alistarh D-A. SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:26215-26227.
LibreCat: https://research-explorer.ista.ac.at/record/14460
arXiv: 2302.04852

Frantar E, Alistarh D-A. SparseGPT: Massive language models can be accurately pruned in one-shot. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:10323-10337.
LibreCat: https://research-explorer.ista.ac.at/record/14458
arXiv: 2301.00774

Van Der Plas TL, Vogels TP, Manohar SG. Predictive learning enables neural networks to learn complex working memory tasks. In: Proceedings of Machine Learning Research. Vol 199. ML Research Press; 2022:518-531.
LibreCat: https://research-explorer.ista.ac.at/record/13239

Konstantinov NH, Lampert C. On the impossibility of fairness-aware learning from corrupted data. In: Proceedings of Machine Learning Research. Vol 171. ML Research Press; 2022:59-83.
LibreCat: https://research-explorer.ista.ac.at/record/13241
arXiv: 2102.06004

Alimisis F, Davies P, Alistarh D-A. Communication-efficient distributed optimization with quantized preconditioners. In: Proceedings of the 38th International Conference on Machine Learning. Vol 139. ML Research Press; 2021:196-206.
LibreCat: https://research-explorer.ista.ac.at/record/13147
arXiv: 2102.07214

Nguyen Q, Mondelli M, Montufar G. Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks. In: Proceedings of the 38th International Conference on Machine Learning. Vol 139. ML Research Press; 2021:8119-8129.
LibreCat: https://research-explorer.ista.ac.at/record/13146
arXiv: 2012.11654

Wang D, Fountoulakis K, Henzinger MH, Mahoney MW, Rao   Satish. Capacity releasing diffusion for speed and locality. In: Proceedings of the 34th International Conference on Machine Learning. Vol 70. ML Research Press; 2017:3598-3607.
LibreCat: https://research-explorer.ista.ac.at/record/11651
arXiv: 1706.05826