| Advances and open problems in federated learning P Kairouz, HB McMahan Foundations and trends in machine learning 14 (1-2), 1-210, 2021 | 9933 | 2021 |
| Learning from history for byzantine robust optimization SP Karimireddy, L He, M Jaggi ICML 2021 - International Conference on Machine Learning, 5311-5319, 2021 | 298 | 2021 |
| Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing SP Karimireddy*, L He*, M Jaggi ICLR 2022 - International Conference on Learning Representations, 2020 | 260* | 2020 |
| Cola: Decentralized linear learning L He*, A Bian*, M Jaggi NeurIPS 2018 - Conference on Neural Information Processing Systems, 4541–4551, 2018 | 196 | 2018 |
| Byzantine-robust decentralized learning via clippedgossip L He, SP Karimireddy, M Jaggi arXiv preprint arXiv:2202.01545, 2022 | 91 | 2022 |
| RelaySum for Decentralized Deep Learning on Heterogeneous Data T Vogels*, L He*, A Koloskova, T Lin, SP Karimireddy, SU Stich, M Jaggi NeurIPS 2021 - Conference on Neural Information Processing Systems, 2021 | 84 | 2021 |
| Secure byzantine-robust machine learning L He, SP Karimireddy, M Jaggi NeurIPS Workshop 2020 (SPICY-FL) Conference on Neural Information Processing …, 2020 | 79 | 2020 |
| Provably Personalized and Robust Federated Learning M Werner, L He, SP Karimireddy, M Jordan, M Jaggi TMLR 2023, 2023 | 20* | 2023 |
| Debiasing Conditional Stochastic Optimization L He, SP Kasiviswanathan NeurIPS 2023 - Conference on Neural Information Processing Systems, 2023 | 9 | 2023 |
| CoBo: Collaborative Learning via Bilevel Optimization D Hashemi, L He, M Jaggi NeurIPS 2024 - Conference on Neural Information Processing Systems, 2024 | 6 | 2024 |
| Leveraging Sparsity for Sample-Efficient Preference Learning: A Theoretical Perspective Y Yao, L He, M Gastpar ICML 2025 - International Conference on Machine Learning, 2025 | 1 | 2025 |
Martin JaggiEPFLVerified email at epfl.ch
