Kwangjun Ahn

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 46

Top Topics

Predictive Control

Subspace Clustering

Monotone Dnf Formulas

Pac Learnability

Top Venues

Publications

Kwangjun Ahn, Xiang Cheng, Minhak Song, Chulhee Yun, Ali Jadbabaie, Suvrit Sra
Linear attention is (maybe) all you need (to understand Transformer optimization). ICLR (2024)
Minhak Song, Kwangjun Ahn, Chulhee Yun
Does SGD really happen in tiny subspaces? CoRR (2024)
Kwangjun Ahn, Ashok Cutkosky
Adam with model exponential moving average is effective for nonconvex optimization. CoRR (2024)
Kwangjun Ahn, Zhiyu Zhang, Yunbum Kook, Yan Dai
Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise. CoRR (2024)
Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand, Suvrit Sra
Transformers learn to implement preconditioned gradient descent for in-context learning. CoRR (2023)
Kwangjun Ahn, Zakaria Mhammedi, Horia Mania, Zhang-Wei Hong, Ali Jadbabaie
Model Predictive Control via On-Policy Imitation Learning. L4DC (2023)
Kwangjun Ahn, Daniel Pfrommer, Jack Umenberger, Tobia Marcucci, Zakaria Mhammedi, Ali Jadbabaie
Smooth Model Predictive Control with Applications to Statistical Learning. CoRR (2023)
Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand, Suvrit Sra
Transformers learn to implement preconditioned gradient descent for in-context learning. NeurIPS (2023)
Kwangjun Ahn, Ali Jadbabaie, Suvrit Sra
How to escape sharp minima. CoRR (2023)
Kwangjun Ahn, Xiang Cheng, Minhak Song, Chulhee Yun, Ali Jadbabaie, Suvrit Sra
Linear attention is (maybe) all you need (to understand transformer optimization). CoRR (2023)
Haoyuan Sun, Khashayar Gatmiry, Kwangjun Ahn, Navid Azizan
A Unified Approach to Controlling Implicit Regularization via Mirror Descent. CoRR (2023)
Yan Dai, Kwangjun Ahn, Suvrit Sra
The Crucial Role of Normalization in Sharpness-Aware Minimization. CoRR (2023)
Yan Dai, Kwangjun Ahn, Suvrit Sra
The Crucial Role of Normalization in Sharpness-Aware Minimization. NeurIPS (2023)
Kwangjun Ahn, Sébastien Bubeck, Sinho Chewi, Yin Tat Lee, Felipe Suarez, Yi Zhang
Learning threshold neurons via edge of stability. NeurIPS (2023)
Kwangjun Ahn, Jingzhao Zhang, Suvrit Sra
Understanding the unstable convergence of gradient descent. CoRR (2022)
Kwangjun Ahn, Suvrit Sra
Understanding Nesterov's Acceleration via Proximal Point Method. SOSA (2022)
Kwangjun Ahn, Zakaria Mhammedi, Horia Mania, Zhang-Wei Hong, Ali Jadbabaie
Model Predictive Control via On-Policy Imitation Learning. CoRR (2022)
Kwangjun Ahn, Jingzhao Zhang, Suvrit Sra
Understanding the unstable convergence of gradient descent. ICML (2022)
Haoyuan Sun, Kwangjun Ahn, Christos Thrampoulidis, Navid Azizan
Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently. NeurIPS (2022)
Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp
Agnostic Learnability of Halfspaces via Logistic Loss. CoRR (2022)
Youngjae Min, Kwangjun Ahn, Navid Azizan
One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares. CoRR (2022)
Haoyuan Sun, Kwangjun Ahn, Christos Thrampoulidis, Navid Azizan
Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently. CoRR (2022)
Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir
Reproducibility in Optimization: Theoretical Framework and Limits. CoRR (2022)
Youngjae Min, Kwangjun Ahn, Navid Azizan
One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares. CDC (2022)
Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp
Agnostic Learnability of Halfspaces via Logistic Loss. ICML (2022)
Kwangjun Ahn, Sébastien Bubeck, Sinho Chewi, Yin Tat Lee, Felipe Suarez, Yi Zhang
Learning threshold neurons via the "edge of stability". CoRR (2022)
Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir
Reproducibility in Optimization: Theoretical Framework and Limits. NeurIPS (2022)
Sinho Chewi, Chen Lu, Kwangjun Ahn, Xiang Cheng, Thibaut Le Gouic, Philippe Rigollet
Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm. COLT (2021)
Kwangjun Ahn, Sinho Chewi
Efficient constrained sampling via the mirror-Langevin algorithm. NeurIPS (2021)
Kwangjun Ahn, Felipe Suarez
Riemannian Perspective on Matrix Factorization. CoRR (2021)
Kwangjun Ahn, Sinho Chewi
Efficient constrained sampling via the mirror-Langevin algorithm. CoRR (2020)
Kwangjun Ahn
A simpler strong refutation of random k-XOR. CoRR (2020)
Kwangjun Ahn
From Proximal Point Method to Nesterov's Acceleration. CoRR (2020)
Sinho Chewi, Chen Lu, Kwangjun Ahn, Xiang Cheng, Thibaut Le Gouic, Philippe Rigollet
Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm. CoRR (2020)
Kwangjun Ahn, Suvrit Sra
From Nesterov's Estimate Sequence to Riemannian Acceleration. COLT (2020)
Kwangjun Ahn
A Simpler Strong Refutation of Random k-XOR. APPROX-RANDOM (2020)
Kwangjun Ahn, Suvrit Sra
On Tight Convergence Rates of Without-replacement SGD. CoRR (2020)
Kwangjun Ahn, Chulhee Yun, Suvrit Sra
SGD with shuffling: optimal rates without component convexity and large epoch requirements. NeurIPS (2020)
Kwangjun Ahn, Kangwook Lee, Changho Suh
Community Recovery in Hypergraphs. IEEE Trans. Inf. Theory 65 (10) (2019)
Kwangjun Ahn, Kangwook Lee, Hyunseung Cha, Changho Suh
Binary Rating Estimation with Graph Side Information. NeurIPS (2018)
Kwangjun Ahn, Kangwook Lee, Changho Suh
Hypergraph Spectral Clustering in the Weighted Stochastic Block Model. IEEE J. Sel. Top. Signal Process. 12 (5) (2018)
Kwangjun Ahn, Kangwook Lee, Changho Suh
Hypergraph Spectral Clustering in the Weighted Stochastic Block Model. CoRR (2018)
Kwangjun Ahn, Kangwook Lee, Changho Suh
Information-theoretic limits of subspace clustering. ISIT (2017)
Kwangjun Ahn, Jisu Jeong
Computing the maximum matching width is NP-hard. CoRR (2017)
Kwangjun Ahn, Kangwook Lee, Changho Suh
Community Recovery in Hypergraphs. CoRR (2017)
Kwangjun Ahn, Kangwook Lee, Changho Suh
Community recovery in hypergraphs. Allerton (2016)