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.
LibreCat: https://research-explorer.ista.ac.at/record/15011
arXiv: 2312.13547

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

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

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

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

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

Predictive learning enables neural networks to learn complex working memory tasks
T.L. Van Der Plas, T.P. Vogels, S.G. Manohar, in:, Proceedings of Machine Learning Research, ML Research Press, 2022, pp. 518–531.
LibreCat: https://research-explorer.ista.ac.at/record/13239

On the impossibility of fairness-aware learning from corrupted data
N.H. Konstantinov, C. Lampert, in:, Proceedings of Machine Learning Research, ML Research Press, 2022, pp. 59–83.
LibreCat: https://research-explorer.ista.ac.at/record/13241
arXiv: 2102.06004

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

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

Capacity releasing diffusion for speed and locality
D. Wang, K. Fountoulakis, M.H. Henzinger, M.W. Mahoney,  Satish Rao , in:, Proceedings of the 34th International Conference on Machine Learning, ML Research Press, 2017, pp. 3598–3607.
LibreCat: https://research-explorer.ista.ac.at/record/11651
arXiv: 1706.05826