Improved analysis of clipping algorithms for non-convex optimization B Zhang, J Jin, C Fang, L Wang Advances in Neural Information Processing Systems 33, 15511-15521, 2020 | 61 | 2020 |
Non-convex distributionally robust optimization: Non-asymptotic analysis J Jin, B Zhang, H Wang, L Wang Advances in Neural Information Processing Systems 34, 2771-2782, 2021 | 35 | 2021 |
Understanding incremental learning of gradient descent: A fine-grained analysis of matrix sensing J Jin, Z Li, K Lyu, SS Du, JD Lee International Conference on Machine Learning, 15200-15238, 2023 | 26 | 2023 |
Why robust generalization in deep learning is difficult: Perspective of expressive power B Li, J Jin, H Zhong, J Hopcroft, L Wang Advances in Neural Information Processing Systems 35, 4370-4384, 2022 | 21 | 2022 |
Dichotomy of early and late phase implicit biases can provably induce grokking K Lyu, J Jin, Z Li, SS Du, JD Lee, W Hu The Twelfth International Conference on Learning Representations, 2024 | 14 | 2024 |
Minimax Optimal Kernel Operator Learning via Multilevel Training J Jin, Y Lu, J Blanchet, L Ying The Eleventh International Conference on Learning Representations, 2023 | 10 | 2023 |
Understanding Riemannian Acceleration via a Proximal Extragradient Framework J Jin, S Sra Proceedings of Thirty Fifth Conference on Learning Theory, PMLR 178, 2924-2962, 2022 | 10* | 2022 |
On the convergence of first order methods for quasar-convex optimization J Jin 12th Annual Workshop on Optimization for Machine Learning, 2020 | 7 | 2020 |
Learning causal representations from general environments: Identifiability and intrinsic ambiguity J Jin, V Syrgkanis arXiv preprint arXiv:2311.12267, 2023 | 3 | 2023 |
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation J Jin, V Syrgkanis arXiv preprint arXiv:2402.14264, 2024 | 1 | 2024 |