| Lora: Low-rank adaptation of large language models EJ Hu, Y Shen, P Wallis, Z Allen-Zhu, Y Li, S Wang, L Wang, W Chen arXiv preprint arXiv:2106.09685, 2021 | 7396 | 2021 |
| Sparks of artificial general intelligence: Early experiments with gpt-4 S Bubeck, V Chandrasekaran, R Eldan, J Gehrke, E Horvitz, E Kamar, ... arXiv preprint arXiv:2303.12712, 2023 | 3043 | 2023 |
| A convergence theory for deep learning via over-parameterization Z Allen-Zhu, Y Li, Z Song International conference on machine learning, 242-252, 2019 | 1597 | 2019 |
| Learning and generalization in overparameterized neural networks, going beyond two layers Z Allen-Zhu, Y Li, Y Liang Advances in neural information processing systems 32, 2019 | 867 | 2019 |
| Convergence analysis of two-layer neural networks with relu activation Y Li, Y Yuan Advances in neural information processing systems 30, 2017 | 767 | 2017 |
| Learning overparameterized neural networks via stochastic gradient descent on structured data Y Li, Y Liang Advances in neural information processing systems 31, 2018 | 722 | 2018 |
| A theoretical analysis of NDCG type ranking measures Y Wang, L Wang, Y Li, D He, TY Liu Conference on learning theory, 25-54, 2013 | 708 | 2013 |
| A latent variable model approach to pmi-based word embeddings S Arora, Y Li, Y Liang, T Ma, A Risteski Transactions of the Association for Computational Linguistics 4, 385-399, 2016 | 659* | 2016 |
| Towards understanding ensemble, knowledge distillation and self-distillation in deep learning Z Allen-Zhu, Y Li arXiv preprint arXiv:2012.09816, 2020 | 389 | 2020 |
| Textbooks are all you need S Gunasekar, Y Zhang, J Aneja, CCT Mendes, A Del Giorno, S Gopi, ... arXiv preprint arXiv:2306.11644, 2023 | 381 | 2023 |
| An alternative view: When does SGD escape local minima? B Kleinberg, Y Li, Y Yuan International conference on machine learning, 2698-2707, 2018 | 363 | 2018 |
| Algorithmic regularization in over-parameterized matrix sensing and neural networks with quadratic activations Y Li, T Ma, H Zhang Conference On Learning Theory, 2-47, 2018 | 349 | 2018 |
| Towards explaining the regularization effect of initial large learning rate in training neural networks Y Li, C Wei, T Ma Advances in neural information processing systems 32, 2019 | 344 | 2019 |
| Linear algebraic structure of word senses, with applications to polysemy S Arora, Y Li, Y Liang, T Ma, A Risteski Transactions of the Association for Computational Linguistics 6, 483-495, 2018 | 268 | 2018 |
| Textbooks are all you need ii: phi-1.5 technical report Y Li, S Bubeck, R Eldan, A Del Giorno, S Gunasekar, YT Lee arXiv preprint arXiv:2309.05463, 2023 | 261 | 2023 |
| Algorithmic framework for model-based deep reinforcement learning with theoretical guarantees Y Luo, H Xu, Y Li, Y Tian, T Darrell, T Ma arXiv preprint arXiv:1807.03858, 2018 | 255 | 2018 |
| Phi-3 technical report: A highly capable language model locally on your phone M Abdin, SA Jacobs, AA Awan, J Aneja, A Awadallah, H Awadalla, ... arXiv preprint arXiv:2404.14219, 2024 | 237 | 2024 |
| Gradient descent on neural networks typically occurs at the edge of stability J Cohen, S Kaur, Y Li, JZ Kolter, A Talwalkar International Conference on Learning Representations, 2021 | 225 | 2021 |
| Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions S Chen, S Chewi, J Li, Y Li, A Salim, AR Zhang arXiv preprint arXiv:2209.11215, 2022 | 222 | 2022 |
| What can resnet learn efficiently, going beyond kernels? Z Allen-Zhu, Y Li Advances in Neural Information Processing Systems 32, 2019 | 220 | 2019 |
