| SCAFFOLD: Stochastic Controlled Averaging for Federated Learning SP Karimireddy, S Kale, M Mohri, SJ Reddi, SU Stich, AT Suresh ICML 2020 - International Conference on Machine Learning, 2019 | 3464 | 2019 |
| Error Feedback Fixes SignSGD and other Gradient Compression Schemes SP Karimireddy, Q Rebjock, SU Stich, M Jaggi ICML 2019 - International Conference on Machine Learning, 2019 | 579 | 2019 |
| A Field Guide to Federated Optimization J Wang*, Z Charles*, Z Xu*, G Joshi*, HB McMahan, M Al-Shedivat, ... arXiv preprint arXiv:2107.06917, 2021 | 399 | 2021 |
| PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization T Vogels, SP Karimireddy, M Jaggi NeurIPS 2019 - Conference on Neural Information Processing Systems, 2019 | 370 | 2019 |
| Why are adaptive methods good for attention models? J Zhang, SP Karimireddy, A Veit, S Kim, SJ Reddi, S Kumar, S Sra NeurIPS 2020 - Conference on Neural Information Processing Systems, 2019 | 358* | 2019 |
| Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning SP Karimireddy, M Jaggi, S Kale, M Mohri, SJ Reddi, SU Stich, AT Suresh NeurIPS 2021 - Conference on Neural Information Processing Systems, 2020 | 318* | 2020 |
| The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication SU Stich, SP Karimireddy JMLR 2020 - Journal of Machine Learning Research, 2019 | 303* | 2019 |
| Learning from History for Byzantine Robust Optimization SP Karimireddy, L He, M Jaggi ICML 2021 - International Conference on Machine Learning, 2020 | 210 | 2020 |
| Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing SP Karimireddy*, L He*, M Jaggi ICLR 2022 - International Conference on Learning Representations, 2021 | 186* | 2021 |
| FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings JO Terrail, SS Ayed, E Cyffers, F Grimberg, C He, R Loeb, P Mangold, ... NeurIPS 2022 - Conference on Neural Information Processing Systems, 2022 | 137* | 2022 |
| Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data T Lin, SP Karimireddy, SU Stich, M Jaggi ICML 2021 - International Conference on Machine Learning, 2021 | 110 | 2021 |
| Secure Byzantine-Robust Machine Learning L He, SP Karimireddy, M Jaggi NeurIPS workshop on Federated Learning (FL-NeurIPS), 2020 | 77 | 2020 |
| Agree to Disagree: Diversity through Disagreement for Better Transferability M Pagliardini, M Jaggi, F Fleuret, SP Karimireddy ICLR 2023 - International Conference on Learning Representations, 2022 | 76 | 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 | 70 | 2021 |
| Towards Model Agnostic Federated Learning Using Knowledge Distillation A Afonin, SP Karimireddy ICLR 2022 - International Conference on Learning Representations, 2021 | 63 | 2021 |
| Mechanisms that Incentivize Data Sharing in Federated Learning SP Karimireddy, W Guo, MI Jordan Workshop on Federated Learning: Recent Advances and New Challenges (in …, 2022 | 57 | 2022 |
| PowerGossip: Practical Low-Rank Communication Compression in Decentralized Deep Learning T Vogels, SP Karimireddy, M Jaggi NeurIPS 2020 - Conference on Neural Information Processing Systems, 2020 | 57* | 2020 |
| Byzantine-Robust Decentralized Learning via Self-Centered Clipping L He, SP Karimireddy, M Jaggi ICML Workshop on Federated learning (FL-ICML), 2022 | 55* | 2022 |
| Accelerating Gradient Boosting Machine H Lu*, SP Karimireddy*, N Ponomareva, V Mirrokni AISTATS 2020 - International Conference on Artificial Intelligence and …, 2019 | 51 | 2019 |
| Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients SP Karimireddy, SU Stich, M Jaggi NeurIPS 2019 Workshop 'Beyond First Order Methods in ML', 2018 | 47 | 2018 |
Martin JaggiEPFLEmail verificata su epfl.ch
