Using NLP Specific Tools for Non-NLP Specific Tasks. A Web Security Application. - Octavia-Maria Sulea; Liviu P. Dinu; Alexandra Peste (ICONIP (4), 2015)

Learning in Variational Autoencoders with Kullback-Leibler and Renyi Integral Bounds. - Septimia Sârbu; Riccardo Volpi; Alexandra Peste; Luigi Malagò (CoRR, 2018)

AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks. - Alexandra Peste; Eugenia Iofinova; Adrian Vladu; Dan Alistarh (NeurIPS, 2021)

SSSE: Efficiently Erasing Samples from Trained Machine Learning Models. - Alexandra Peste; Dan Alistarh; Christoph H. Lampert (CoRR, 2021)

Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. - Torsten Hoefler; Dan Alistarh; Tal Ben-Nun; Nikoli Dryden; Alexandra Peste (J. Mach. Learn. Res., 2021)

How Well Do Sparse ImageNet Models Transfer? - Eugenia Iofinova; Alexandra Peste; Mark Kurtz; Dan Alistarh (CVPR, 2022)

CrAM: A Compression-Aware Minimizer. - Alexandra Peste; Adrian Vladu; Dan Alistarh; Christoph H. Lampert (CoRR, 2022)

Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures. - Eugenia Iofinova; Alexandra Peste; Dan Alistarh (CVPR, 2023)

CrAM: A Compression-Aware Minimizer. - Alexandra Peste; Adrian Vladu; Eldar Kurtic; Christoph H. Lampert; Dan Alistarh (ICLR, 2023)

Knowledge Distillation Performs Partial Variance Reduction. - Mher Safaryan; Alexandra Peste; Dan Alistarh (NeurIPS, 2023)

ELSA: Partial Weight Freezing for Overhead-Free Sparse Network Deployment. - Paniz Halvachi; Alexandra Peste; Dan Alistarh; Christoph H. Lampert (CoRR, 2023)

Accurate Neural Network Pruning Requires Rethinking Sparse Optimization. - Denis Kuznedelev; Eldar Kurtic; Eugenia Iofinova; Elias Frantar; Alexandra Peste; Dan Alistarh (Trans. Mach. Learn. Res., 2024)