| Bilevel optimization: Convergence analysis and enhanced design K Ji, J Yang, Y Liang International conference on machine learning, 4882-4892, 2021 | 205* | 2021 |
| Provably faster algorithms for bilevel optimization J Yang, K Ji, Y Liang Advances in Neural Information Processing Systems 34, 13670-13682, 2021 | 108 | 2021 |
| Multi-step model-agnostic meta-learning: Convergence and improved algorithms K Ji, J Yang, Y Liang arXiv preprint arXiv:2002.07836 2, 2020 | 77* | 2020 |
| Sgd converges to global minimum in deep learning via star-convex path Y Zhou, J Yang, H Zhang, Y Liang, V Tarokh arXiv preprint arXiv:1901.00451, 2019 | 72 | 2019 |
| A machine learning approach to user association in enterprise small cell networks J Yang, C Wang, X Wang, C Shen 2018 IEEE/CIC International Conference on Communications in China (ICCC …, 2018 | 5 | 2018 |
| Learning to generalize provably in learning to optimize J Yang, T Chen, M Zhu, F He, D Tao, Y Liang, Z Wang International Conference on Artificial Intelligence and Statistics, 9807-9825, 2023 | 4 | 2023 |
| M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation J Yang, X Chen, T Chen, Z Wang, Y Liang arXiv preprint arXiv:2303.00039, 2023 | 3 | 2023 |
| Meta ControlNet: Enhancing Task Adaptation via Meta Learning J Yang, J Zhao, P Wang, Z Wang, Y Liang arXiv preprint arXiv:2312.01255, 2023 | 1 | 2023 |
| Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization X Chen, Z Wang, D Sow, J Yang, T Chen, Y Liang, M Zhou, Z Wang arXiv preprint arXiv:2402.14270, 2024 | | 2024 |
| Rethinking PGD Attack: Is Sign Function Necessary? J Yang, T Chen, X Chen, Z Wang, Y Liang arXiv preprint arXiv:2312.01260, 2023 | | 2023 |
| Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study C Chen, J Yang, Y Zhou 2020 IEEE International Conference on Big Data (Big Data), 141-146, 2020 | | 2020 |
Yingbin LiangThe Ohio State UniversityVerified email at osu.edu
