Preprints:
- Model Predictive Control via On-Policy Imitation Learning
Kwangjun Ahn, Zakaria Mhammedi, Horia Mania, Zhang-Wei Hong, Ali Jadbabaie
Oct. 2022. - Graph Matrices: Norm Bounds and Applications
Kwangjun Ahn, Dhruv Medarametla, and Aaron Potechin
Oct. 2020.
[Talk Video at MIT LIDS/STAT Tea Talk 2020]
Published Works:
- Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently
Haoyuan Sun, Kwangjun Ahn, Christos Thrampoulidis, Navid Azizan
Accepted to Neural Information Processing Systems (NeurIPS), Dec. 2022 - Reproducibility in Optimization: Theoretical Framework and Limits
Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir
Accepted to Neural Information Processing Systems (NeurIPS), Dec. 2022
(Selected for Oral Presentation) - One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares
Youngjae Min, Kwangjun Ahn, Navid Azizan
IEEE 61st Conference on Decision and Control (CDC), Dec. 2022 - Understanding the unstable convergence of gradient descent
Kwangjun Ahn, Jingzhao Zhang, Suvrit Sra
Proceedings of the 39th International Conference on Machine Learning(ICML), Jul. 2022 Baltimore - Agnostic Learnability of Halfspaces via Logistic Loss
Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp
Proceedings of the 39th International Conference on Machine Learning(ICML), Jul. 2022 Baltimore - Understanding Nesterov's Acceleration via Proximal Point Method
Kwangjun Ahn and Suvrit Sra
SIAM Symposium on Simplicity in Algorithms (SOSA), Jan. 2022 - Efficient constrained sampling via the mirror-Langevin algorithm
Kwangjun Ahn and Sinho Chewi
Adavnces in Neural Information Processing Systems (NeruIPS), Dec. 2021
[Talk Video at NeurIPS 2021] [Talk Video by Sinho at Simons Institute] - Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm
Sinho Chewi, Chen Lu, Kwangjun Ahn, Xiang Cheng, Thibaut Le Gouic, Philippe Rigollet
34th Annual Conference on Learning Theory (COLT), Boulder, Colorado, Aug. 2021
[Talk video by Sinho at Simons Institute] - SGD with shuffling: optimal rates without component convexity and large epoch requirements
Kwangjun Ahn, Chulhee Yun, and Suvrit Sra
Advances in Neural Information Processing Systems (NeurIPS), Dec. 2020.
(Selected for Spotlight Presentation)
[Talk Video at NeurIPS 2020] [40min Talk Video by Suvrit at OPTML] - A Simpler Strong Refutation of Random k-XOR
Kwangjun Ahn
International Conference on Randomization and Computation (RANDOM) 2020, Seattle, Washington, USA, Aug. 2020.
[Talk Video at RANDOM 2020] - From Nesterov's Estimate Sequence to Riemannian Acceleration
Kwangjun Ahn and Suvrit Sra
Annual Conference on Learning Theory (COLT), Graz, Austria, Jul. 2020.
[Talk Video at COLT 2020] [1hr Talk Video by Suvrit] - Community Recovery in Hypergraphs
Kwangjun Ahn, Kangwook Lee, and Changho Suh
IEEE Transactions on Information Theory, vol. 65, no. 10, pp. 6561-6579, Oct. 2019. - Binary Rating Estimation with Graph Side Information
Kwangjun Ahn, Kangwook Lee, Hyunseung Cha, and Changho Suh
Advances in Neural Information Processing Systems (NeurIPS), Montreal, Canada, Dec. 2018. - Hypergraph Spectral Clustering in the Weighted Stochastic Block Model
Kwangjun Ahn, Kangwook Lee, and Changho Suh
IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 10, Oct. 2018. - Information-theoretic Limits of Subspace Clustering
Kwangjun Ahn, Kangwook Lee, and Changho Suh
IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, Jun. 2017. - Community Recovery in Hypergraphs
Kwangjun Ahn, Kangwook Lee, and Changho Suh
The 53rd Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA, Sep. 2016.
Technical Reports:
- Computing the Maximum Matching Width is NP-hard
Kwangjun Ahn and Jisu Jeong
Sep. 2017. - Riemannian Perspective on Matrix Factorization
Kwangjun Ahn and Felipe Suarez
Feb. 2021
Presentation Videos:
- Presentation on "Optimal Convergence Rate of Hamiltonian Monte Carlo for Strongly Logconcave Distributions"
[1hr Presentation Given at Simons Institute Reading Group]
Sep. 2021, Berkeley CA