| Adagrad stepsizes: Sharp convergence over nonconvex landscapes R Ward, X Wu, L Bottou Journal of Machine Learning Research 21 (219), 1-30, 2020 | 299 | 2020 |
| Zeroquant: Efficient and affordable post-training quantization for large-scale transformers Z Yao, R Yazdani Aminabadi, M Zhang, X Wu, C Li, Y He Advances in Neural Information Processing Systems 35, 27168-27183, 2022 | 177 | 2022 |
| When do curricula work? X Wu, E Dyer, B Neyshabur arXiv preprint arXiv:2012.03107, 2020 | 118 | 2020 |
| Adagrad stepsizes: Sharp convergence over nonconvex landscapes, from any initialization R Ward, X Wu, L Bottou arXiv preprint arXiv:1806.01811, 2018 | 88 | 2018 |
| Wngrad: Learn the learning rate in gradient descent X Wu, R Ward, L Bottou arXiv preprint arXiv:1803.02865, 2018 | 82 | 2018 |
| Global convergence of adaptive gradient methods for an over-parameterized neural network X Wu, SS Du, R Ward arXiv preprint arXiv:1902.07111, 2019 | 65 | 2019 |
| Hierarchical learning for generation with long source sequences T Rohde, X Wu, Y Liu arXiv preprint arXiv:2104.07545, 2021 | 54 | 2021 |
| Linear convergence of adaptive stochastic gradient descent Y Xie, X Wu, R Ward International conference on artificial intelligence and statistics, 1475-1485, 2020 | 48 | 2020 |
| Choosing the sample with lowest loss makes sgd robust V Shah, X Wu, S Sanghavi International Conference on Artificial Intelligence and Statistics, 2120-2130, 2020 | 42 | 2020 |
| Zeroquant-v2: Exploring post-training quantization in llms from comprehensive study to low rank compensation Z Yao, X Wu, C Li, S Youn, Y He arXiv preprint arXiv:2303.08302, 2023 | 40* | 2023 |
| Value-at-Risk estimation with stochastic interest rate models for option-bond portfolios X Wang, D Xie, J Jiang, X Wu, J He Finance Research Letters 21, 10-20, 2017 | 28 | 2017 |
| Deepspeed-chat: Easy, fast and affordable rlhf training of chatgpt-like models at all scales Z Yao, RY Aminabadi, O Ruwase, S Rajbhandari, X Wu, AA Awan, ... arXiv preprint arXiv:2308.01320, 2023 | 25 | 2023 |
| Implicit regularization and convergence for weight normalization X Wu, E Dobriban, T Ren, S Wu, Z Li, S Gunasekar, R Ward, Q Liu Advances in Neural Information Processing Systems 33, 2835-2847, 2020 | 24* | 2020 |
| Understanding int4 quantization for transformer models: Latency speedup, composability, and failure cases X Wu, C Li, RY Aminabadi, Z Yao, Y He arXiv preprint arXiv:2301.12017, 2023 | 21* | 2023 |
| Zeroquant-fp: A leap forward in llms post-training w4a8 quantization using floating-point formats X Wu, Z Yao, Y He arXiv preprint arXiv:2307.09782, 2023 | 18 | 2023 |
| Xtc: Extreme compression for pre-trained transformers made simple and efficient X Wu, Z Yao, M Zhang, C Li, Y He Advances in Neural Information Processing Systems 35, 3217-3231, 2022 | 17 | 2022 |
| Mlpruning: A multilevel structured pruning framework for transformer-based models Z Yao, L Ma, S Shen, K Keutzer, MW Mahoney arXiv preprint arXiv:2105.14636, 2021 | 13 | 2021 |
| Random-ltd: Random and layerwise token dropping brings efficient training for large-scale transformers Z Yao, X Wu, C Li, C Holmes, M Zhang, C Li, Y He arXiv preprint arXiv:2211.11586, 2022 | 11 | 2022 |
| Zero++: Extremely efficient collective communication for giant model training G Wang, H Qin, SA Jacobs, C Holmes, S Rajbhandari, O Ruwase, F Yan, ... arXiv preprint arXiv:2306.10209, 2023 | 8 | 2023 |
| Adaloss: A computationally-efficient and provably convergent adaptive gradient method X Wu, Y Xie, SS Du, R Ward Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8691-8699, 2022 | 8 | 2022 |
