| From Nesterov's Estimate Sequence to Riemannian Acceleration K Ahn, S Sra Proceedings of Thirty Third Conference on Learning Theory (COLT), PMLR 125 …, 2020 | 70 | 2020 |
| Hypergraph spectral clustering in the weighted stochastic block model K Ahn, K Lee, C Suh IEEE Journal of Selected Topics in Signal Processing 12 (5), 959-974, 2018 | 67 | 2018 |
| SGD with shuffling: optimal rates without component convexity and large epoch requirements K Ahn, C Yun, S Sra NeurIPS 2020 (*Spotlight* Presentation), 2020 | 54* | 2020 |
| Understanding the unstable convergence of gradient descent K Ahn, J Zhang, S Sra ICML 2022 (arXiv:2204.01050), 2022 | 47 | 2022 |
| Optimal dimension dependence of the metropolis-adjusted langevin algorithm S Chewi, C Lu, K Ahn, X Cheng, T Le Gouic, P Rigollet Conference on Learning Theory (COLT), 1260-1300, 2021 | 47 | 2021 |
| Efficient constrained sampling via the mirror-Langevin algorithm K Ahn, S Chewi NeurIPS 2021, 2021 | 45 | 2021 |
| Community recovery in hypergraphs K Ahn, K Lee, C Suh IEEE Transactions on Information Theory 65 (10), 6561-6579, 2019 | 42 | 2019 |
| Binary rating estimation with graph side information K Ahn, K Lee, H Cha, C Suh NeurIPS 2018, 4272-4283, 2018 | 32 | 2018 |
| Graph Matrices: Norm Bounds and Applications K Ahn, D Medarametla, A Potechin arXiv preprint 1604.03423, 2020 | 26* | 2020 |
| Learning threshold neurons via the "edge of stability" K Ahn, S Bubeck, S Chewi, YT Lee, F Suarez, Y Zhang NeurIPS 2023 (arXiv:2212.07469), 2022 | 18 | 2022 |
| Transformers learn to implement preconditioned gradient descent for in-context learning K Ahn, X Cheng, H Daneshmand, S Sra NeurIPS 2023 (arXiv:2306.00297), 2023 | 17 | 2023 |
| Reproducibility in Optimization: Theoretical Framework and Limits K Ahn, P Jain, Z Ji, S Kale, P Netrapalli, GI Shamir NeurIPS 2022 (Selected for *Oral* presentation), 2022 | 14 | 2022 |
| Riemannian perspective on matrix factorization K Ahn, F Suarez arXiv preprint arXiv:2102.00937, 2021 | 11 | 2021 |
| Understanding Nesterov's Acceleration via Proximal Point Method K Ahn, S Sra Symposium on Simplicity in Algorithms (SOSA), 117-130, 2022 | 10* | 2022 |
| One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares Y Min, K Ahn, N Azizan CDC 2022 (arXiv preprint arXiv:2207.13853), 2022 | 7 | 2022 |
| Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently H Sun, K Ahn, C Thrampoulidis, N Azizan NeurIPS 2022 (arXiv preprint arXiv:2205.12808), 2022 | 7 | 2022 |
| Agnostic Learnability of Halfspaces via Logistic Loss Z Ji, K Ahn, P Awasthi, S Kale, S Karp ICML 2022 (arXiv:2201.13419), 2022 | 5 | 2022 |
| Information-theoretic limits of subspace clustering K Ahn, K Lee, C Suh 2017 IEEE International Symposium on Information Theory (ISIT), 2473-2477, 2017 | 5 | 2017 |
| How to escape sharp minima K Ahn, A Jadbabaie, S Sra arXiv preprint arXiv:2305.15659, 2023 | 2 | 2023 |
| The Crucial Role of Normalization in Sharpness-Aware Minimization Y Dai, K Ahn, S Sra NeurIPS 2023 (arXiv:2305.15287), 2023 | 2 | 2023 |
Suvrit SraTU Munich / MITVerified email at mit.edu
