| Ensemble Distillation for Robust Model Fusion in Federated Learning T Lin*, L Kong*, SU Stich, M Jaggi NeurIPS 2020 - Advances in Neural Information Processing Systems, 2020, 2020 | 1186 | 2020 |
| Don't Use Large Mini-Batches, Use Local SGD T Lin, SU Stich, KK Patel, M Jaggi ICLR 2020 - International Conference on Learning Representations, 2020 | 508 | 2020 |
| Fog orchestration for internet of things services Z Wen, R Yang, P Garraghan, T Lin, J Xu, M Rovatsos IEEE Internet Computing 21 (2), 16-24, 2017 | 421 | 2017 |
| Decentralized Deep Learning with Arbitrary Communication Compression A Koloskova*, T Lin*, SU Stich, M Jaggi ICLR 2020 - International Conference on Learning Representations, 2020 | 269 | 2020 |
| Dynamic Model Pruning with Feedback T Lin, SU Stich, L Barba, D Dmitriev, M Jaggi ICLR 2020 - International Conference on Learning Representations, 2020 | 249 | 2020 |
| Exploring interpretable LSTM neural networks over multi-variable data T Guo, T Lin, N Antulov-Fantulin ICML 2019 - International Conference on Machine Learning, 2494-2504, 2019 | 233 | 2019 |
| Hybrid Neural Networks for Learning the Trend in Time Series T Lin*, T Guo*, K Aberer IJCAI 2017 - Proceedings of the Twenty-Sixth International Joint Conference …, 2017 | 191 | 2017 |
| Training DNNs with Hybrid Block Floating Point M Drumond, T Lin, M Jaggi, B Falsafi NeurIPS 2018 - Advances in Neural Information Processing Systems, 2018, 2018 | 134 | 2018 |
| Masking as an Efficient Alternative to Finetuning for Pretrained Language Models M Zhao*, T Lin*, M Jaggi, H Schütze EMNLP 2020 - Empirical Methods in Natural Language Processing, 2020 | 114 | 2020 |
| Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data T Lin, SP Karimireddy, SU Stich, M Jaggi ICML 2021 - Proceedings of the 38th International Conference on Machine Learning, 2021 | 110 | 2021 |
| An Improved Analysis of Gradient Tracking for Decentralized Machine Learning A Koloskova, T Lin, SU Stich NeurIPS 2021 - Advances in Neural Information Processing Systems, 2021 34, 2021 | 108 | 2021 |
| On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them C Liu, M Salzmann, T Lin, R Tomioka, S Süsstrunk NeurIPS 2020 - Advances in Neural Information Processing Systems, 2020, 2020 | 103 | 2020 |
| Consensus Control for Decentralized Deep Learning L Kong*, T Lin*, A Koloskova, M Jaggi, SU Stich ICML 2021 - Proceedings of the 38th International Conference on Machine Learning, 2021 | 91 | 2021 |
| Revisiting Weighted Aggregation in Federated Learning with Neural Networks Z Li, T Lin, X Shang, C Wu ICML 2023 - International Conference on Machine Learning, 2023 | 74 | 2023 |
| RelaySum for Decentralized Deep Learning on Heterogeneous Data T Vogels*, L He*, A Koloskova, T Lin, SP Karimireddy, SU Stich, M Jaggi NeurIPS 2021 - Advances in Neural Information Processing Systems, 2021, 2021 | 69 | 2021 |
| No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier Z Li, X Shang, R He, T Lin, C Wu ICCV 2023 - International Conference on Computer Vision, 2023 | 66 | 2023 |
| An Integrated Systems Genetics and Omics Toolkit to Probe Gene Function H Li*, X Wang*, D Rukina, Q Huang, T Lin, V Sorrentino, H Zhang, ... Cell systems 6 (1), 90-102. e4, 2018 | 59 | 2018 |
| On Pitfalls of Test-Time Adaptation H Zhao*, Y Liu*, A Alahi, T Lin ICML 2023 - International Conference on Machine Learning, abridged in ICLR …, 2023 | 55 | 2023 |
| Test-Time Robust Personalization for Federated Learning L Jiang*, T Lin* ICLR 2023 - International Conference on Learning Representations, 2023 | 54 | 2023 |
| On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm P Sun, B Shi, D Yu, T Lin CVPR 2024 - The IEEE / CVF Computer Vision and Pattern Recognition Conference, 2024 | 52 | 2024 |
