Jaggi, Martin

Advances and Open Problems in Federated Learning

Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al. (2019) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 7,857

Sparsified SGD with Memory

Stich, S. U., Cordonnier, J.-B., & Jaggi, M. (2018) · arXiv

Stochastic Gradient Optimization TechniquesDistributed Control Multi-Agent Systems

Cited by 508

On the Relationship between Self-Attention and Convolutional Layers

Cordonnier, J.-B., Loukas, A., & Jaggi, M. (2019) · arXiv

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 354

Unsupervised Scalable Representation Learning for Multivariate Time Series

Franceschi, J.-Y., Dieuleveut, A., & Jaggi, M. (2019) · arXiv

Time Series Analysis and ForecastingMusic and Audio Processing

Cited by 305

Dynamic Model Pruning with Feedback

Lin, T., Stich, S. U., Barba, L., Dmitriev, D., & Jaggi, M. (2020) · arXiv

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 126

MEDITRON-70B: Scaling Medical Pretraining for Large Language Models

Chen, Z., Cano, A. H., Romanou, A., Bonnet, A., Matoba, K., Salvi, F., Pagliardini, M., Fan, S., Köpf, A., Mohtashami, A., et al. (2023) · arXiv

Artificial Intelligence in Healthcare and EducationTopic Modeling

Cited by 122

Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Karimireddy, S. P., Jaggi, M., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., & Suresh, A. T. (2020) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 96

SKILL: Structured Knowledge Infusion for Large Language Models

Moiseev, F., Dong, Z., Alfonseca, E., & Jaggi, M. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingNatural Language Processing Techniques

Cited by 71

An Equivalence between the Lasso and Support Vector Machines

Jaggi, M. (2013) · arXiv

Statistical Methods and InferenceSparse and Compressive Sensing Techniques

Cited by 35

Random-Access Infinite Context Length for Transformers

Mohtashami, A., & Jaggi, M. (2023) · Advances in Neural Information Processing Systems 36

Topic ModelingNatural Language Processing Techniques

Cited by 5

Landmark Attention: Random-Access Infinite Context Length for Transformers

Mohtashami, A., & Jaggi, M. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 4

DoGE: Domain Reweighting with Generalization Estimation

Fan, S., Pagliardini, M., & Jaggi, M. (2023) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 3

Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

Hägele, A., Bakouch, E., Kosson, A., Allal, L. B., Von Werra, L., & Jaggi, M. (2024) · arXiv

Stochastic Gradient Optimization TechniquesMachine Learning and Data Classification

Cited by 2

Use and Effects of LLMs in Peer Review: A Randomized Experiment and Survey at ICML 2026

Kim, S. S. Y., Deng, W. H., Vaughan, J. W., Su, B., Su, W., Agarwal, A., Li, S., Jaggi, M., Goldstein, D. G., Shah, N. B., et al. (2026) · arXiv

Publishing and Scholarly CommunicationAcademic Writing and Publishing

Cited by 0

FineWeb2: One Pipeline to Scale Them All - Adapting Pre-Training Data Processing to Every Language

Penedo, G., Kydlíček, H., Sabolčec, V., Messmer, B., Foroutan, N., Kargaran, A. H., Raffel, C., Jaggi, M., Von Werra, L., & Wolf, T. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0

Mitigating Unintended Memorization with LoRA in Federated Learning for LLMs

Bossy, T., Vignoud, J., Rabbani, T., Pastoriza, J. R. T., & Jaggi, M. (2025) · arXiv

Privacy-Preserving Technologies in DataDomain Adaptation and Few-Shot Learning

Cited by 0