Articles with public access mandates - Kwangjun AhnLearn more
Available somewhere: 10
From Nesterov's Estimate Sequence to Riemannian Acceleration
K Ahn, S Sra
Proceedings of Thirty Third Conference on Learning Theory (COLT), PMLR 125 …, 2020
Mandates: US National Science Foundation
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
Mandates: US National Science Foundation, US Department of Defense
Understanding the unstable convergence of gradient descent
K Ahn, J Zhang, S Sra
International Conference on Machine Learning, 247-257, 2022
Mandates: US National Science Foundation
Sgd with shuffling: optimal rates without component convexity and large epoch requirements
K Ahn, C Yun, S Sra
Advances in Neural Information Processing Systems 33, 17526-17535, 2020
Mandates: US National Science Foundation
Efficient constrained sampling via the mirror-Langevin algorithm
K Ahn, S Chewi
Advances in Neural Information Processing Systems 34, 28405-28418, 2021
Mandates: US National Science Foundation, US Department of Defense
Reproducibility in optimization: Theoretical framework and limits
K Ahn, P Jain, Z Ji, S Kale, P Netrapalli, GI Shamir
Advances in Neural Information Processing Systems 35, 18022-18033, 2022
Mandates: US National Science Foundation, US Department of Defense
Mirror descent maximizes generalized margin and can be implemented efficiently
H Sun, K Ahn, C Thrampoulidis, N Azizan
Advances in Neural Information Processing Systems 35, 31089-31101, 2022
Mandates: US National Science Foundation, US Department of Defense
Understanding Nesterov's Acceleration via Proximal Point Method
K Ahn, S Sra
Symposium on Simplicity in Algorithms (SOSA), 117-130, 2022
Mandates: US National Science Foundation
Agnostic learnability of halfspaces via logistic loss
Z Ji, K Ahn, P Awasthi, S Kale, S Karp
International Conference on Machine Learning, 10068-10103, 2022
Mandates: US National Science Foundation
Model predictive control via on-policy imitation learning
K Ahn, Z Mhammedi, H Mania, ZW Hong, A Jadbabaie
Learning for Dynamics and Control Conference, 1493-1505, 2023
Mandates: US Department of Defense
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