Information-theoretic limits of subspace clustering. - Kwangjun Ahn; Kangwook Lee 001; Changho Suh (ISIT, 2017)

Computing the maximum matching width is NP-hard. - Kwangjun Ahn; Jisu Jeong (CoRR, 2017)

Binary Rating Estimation with Graph Side Information. - Kwangjun Ahn; Kangwook Lee 001; Hyunseung Cha; Changho Suh (NeurIPS, 2018)

Hypergraph Spectral Clustering in the Weighted Stochastic Block Model. - Kwangjun Ahn; Kangwook Lee 001; Changho Suh (IEEE J. Sel. Top. Signal Process., 2018)

Community Recovery in Hypergraphs. - Kwangjun Ahn; Kangwook Lee 001; Changho Suh (IEEE Trans. Inf. Theory, 2019)

A Simpler Strong Refutation of Random k-XOR. - Kwangjun Ahn (APPROX-RANDOM, 2020)

From Nesterov's Estimate Sequence to Riemannian Acceleration. - Kwangjun Ahn; Suvrit Sra (COLT, 2020)

SGD with shuffling: optimal rates without component convexity and large epoch requirements. - Kwangjun Ahn; Chulhee Yun; Suvrit Sra (NeurIPS, 2020)

On Tight Convergence Rates of Without-replacement SGD. - Kwangjun Ahn; Suvrit Sra (CoRR, 2020)

From Proximal Point Method to Nesterov's Acceleration. - Kwangjun Ahn (CoRR, 2020)

Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm. - Sinho Chewi; Chen Lu 002; Kwangjun Ahn; Xiang Cheng; Thibaut Le Gouic; Philippe Rigollet (COLT, 2021)

Efficient constrained sampling via the mirror-Langevin algorithm. - Kwangjun Ahn; Sinho Chewi (NeurIPS, 2021)

Riemannian Perspective on Matrix Factorization. - Kwangjun Ahn; Felipe Suarez (CoRR, 2021)

One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares. - Youngjae Min; Kwangjun Ahn; Navid Azizan (CDC, 2022)

Understanding the unstable convergence of gradient descent. - Kwangjun Ahn; Jingzhao Zhang; Suvrit Sra (ICML, 2022)

Agnostic Learnability of Halfspaces via Logistic Loss. - Ziwei Ji; Kwangjun Ahn; Pranjal Awasthi; Satyen Kale; Stefani Karp (ICML, 2022)

Reproducibility in Optimization: Theoretical Framework and Limits. - Kwangjun Ahn; Prateek Jain 002; Ziwei Ji; Satyen Kale; Praneeth Netrapalli; Gil I. Shamir (NeurIPS, 2022)

Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently. - Haoyuan Sun; Kwangjun Ahn; Christos Thrampoulidis; Navid Azizan (NeurIPS, 2022)

Understanding Nesterov's Acceleration via Proximal Point Method. - Kwangjun Ahn; Suvrit Sra (SOSA, 2022)

Model Predictive Control via On-Policy Imitation Learning. - Kwangjun Ahn; Zakaria Mhammedi; Horia Mania; Zhang-Wei Hong; Ali Jadbabaie (L4DC, 2023)

The Crucial Role of Normalization in Sharpness-Aware Minimization. - Yan Dai 002; Kwangjun Ahn; Suvrit Sra (NeurIPS, 2023)

Learning threshold neurons via edge of stability. - Kwangjun Ahn; Sébastien Bubeck; Sinho Chewi; Yin Tat Lee; Felipe Suarez; Yi Zhang (NeurIPS, 2023)

Transformers learn to implement preconditioned gradient descent for in-context learning. - Kwangjun Ahn; Xiang Cheng; Hadi Daneshmand; Suvrit Sra (NeurIPS, 2023)

How to escape sharp minima. - Kwangjun Ahn; Ali Jadbabaie; Suvrit Sra (CoRR, 2023)

Smooth Model Predictive Control with Applications to Statistical Learning. - Kwangjun Ahn; Daniel Pfrommer; Jack Umenberger; Tobia Marcucci; Zakaria Mhammedi; Ali Jadbabaie (CoRR, 2023)

A Unified Approach to Controlling Implicit Regularization via Mirror Descent. - Haoyuan Sun; Khashayar Gatmiry; Kwangjun Ahn; Navid Azizan (J. Mach. Learn. Res., 2023)

On the Sample Complexity of Imitation Learning for Smoothed Model Predictive Control. - Daniel Pfrommer; Swati Padmanabhan; Kwangjun Ahn; Jack Umenberger; Tobia Marcucci; Zakaria Mhammedi; Ali Jadbabaie (CDC, 2024)

Linear attention is (maybe) all you need (to understand Transformer optimization). - Kwangjun Ahn; Xiang Cheng; Minhak Song; Chulhee Yun; Ali Jadbabaie; Suvrit Sra (ICLR, 2024)

How to Escape Sharp Minima with Random Perturbations. - Kwangjun Ahn; Ali Jadbabaie; Suvrit Sra (ICML, 2024)

Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise. - Kwangjun Ahn; Zhiyu Zhang; Yunbum Kook; Yan Dai 002 (ICML, 2024)

Adam with model exponential moving average is effective for nonconvex optimization. - Kwangjun Ahn; Ashok Cutkosky (NeurIPS, 2024)

Improved Sample Complexity of Imitation Learning for Barrier Model Predictive Control. - Daniel Pfrommer; Swati Padmanabhan; Kwangjun Ahn; Jack Umenberger; Tobia Marcucci; Zakaria Mhammedi; Ali Jadbabaie (CoRR, 2024)

Learning to Achieve Goals with Belief State Transformers. - Edward S. Hu; Kwangjun Ahn; Qinghua Liu; Haoran Xu; Manan Tomar; Ada Langford; Dinesh Jayaraman; Alex Lamb; John Langford 001 (CoRR, 2024)

General framework for online-to-nonconvex conversion: Schedule-free SGD is also effective for nonconvex optimization. - Kwangjun Ahn; Gagik Magakyan; Ashok Cutkosky (CoRR, 2024)

The Belief State Transformer. - Edward S. Hu; Kwangjun Ahn; Qinghua Liu; Haoran Xu; Manan Tomar; Ada Langford; Dinesh Jayaraman; Alex Lamb; John Langford 001 (ICLR, 2025)

Does SGD really happen in tiny subspaces? - Minhak Song; Kwangjun Ahn; Chulhee Yun (ICLR, 2025)

Efficient Joint Prediction of Multiple Future Tokens. - Kwangjun Ahn; Alex Lamb; John Langford 001 (CoRR, 2025)

Dion: A Communication-Efficient Optimizer for Large Models. - Kwangjun Ahn; Byron Xu (CoRR, 2025)

Through the River: Understanding the Benefit of Schedule-Free Methods for Language Model Training. - Minhak Song; Beomhan Baek; Kwangjun Ahn; Chulhee Yun (CoRR, 2025)