Publications

Preprints:

  1. Understanding the unstable convergence of gradient descent
    Kwangjun Ahn, Jingzhao Zhang, Suvrit Sra
    To appear at ICML 2022, Jul., Baltimore
  2. Reproducibility in Optimization: Theoretical Framework and Limits
    Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir
    Feb. 2022
  3. Agnostic Learnability of Halfspaces via Logistic Loss
    Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi, Satyen Kale, Stefani Karp

    To appear at ICML 2022, Jul., Baltimore 
  4. 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:

  1. Understanding Nesterov's Acceleration via Proximal Point Method
    Kwangjun Ahn and Suvrit Sra
    SIAM Symposium on Simplicity in Algorithms (SOSA), Jan. 2022
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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.
  8. 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. 
  9. 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. 
  10. Information-theoretic Limits of Subspace Clustering 
    Kwangjun Ahn, Kangwook Lee, and Changho Suh
    IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, Jun. 2017.
  11. 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:

  1. Computing the Maximum Matching Width is NP-hard
    Kwangjun Ahn and Jisu Jeong
    Sep. 2017. 
  2. Riemannian Perspective on Matrix Factorization
    Kwangjun Ahn and Felipe Suarez
    Feb. 2021

Presentation Videos:

  1. 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