publications

publications by categories in reversed chronological order. generated by jekyll-scholar.
note: * indicates equal contributions. † indicates that the author ordering is alphabetical.

Working papers

2025

  1. working
    Revisiting Orbital Minimization for Neural Operator Decomposition
    J. Jon Ryu, Samuel Zhou, and Gregory W. Wornell
    2025
    Submitted.
  2. working
    Contrastive Predictive Coding Done Right for Mutual Information Estimation
    J. Jon Ryu, Pavan Yeddanapudi, Xiangxiang Xu, and Gregory W. Wornell
    2025
    In preparation.

2024

  1. working

Preprints

2025

  1. working
    Efficient Parametric SVD of Koopman Operator for Stochastic Dynamical Systems
    2025
    Submitted.

2022

  1. arXiv
    Minimax Optimal Algorithms with Fixed-k-Nearest Neighbors
    J. Jon Ryu and Young-Han Kim
    2022
    Submitted.
  2. arXiv
    Learning with Succinct Common Representation with Wyner’s Common Information
    2022
    Submitted. A preliminary version of this manuscript was presented at the Bayesian Deep Learning Workshop at NeurIPS 2018, and an abridged version of the current manuscript was presented at the Bayesian Deep Learning workshop at NeurIPS 2021.

Journal articles

2024

  1. On Confidence Sequences for Bounded Random Processes via Universal Gambling Strategies
    J. Jon Ryu and Alankrita Bhatt
    IEEE Trans. Inf. Theory, 2024

2022

  1. Nearest neighbor density functional estimation from inverse Laplace transform
    IEEE Trans. Inf. Theory, February 2022

Conference papers

2025

  1. A Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimation
    J. Jon Ryu, Abhin Shah, and Gregory W. Wornell
    In Proc. Int. Conf. Mach. Learn. (ICML), July 2025
  2. Score-of-Mixture Training: Training One-Step Generative Models Made Simple
    In Proc. Int. Conf. Mach. Learn. (ICML), July 2025
    Spotlight (top 2.6%)
  3. Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing
    J. Jon Ryu, Jeongyeol Kwon, Benjamin Koppe, and Kwang-Sung Jun
    In Conf. Learn. Theory (COLT), July 2025

2024

  1. ISIT
    Group Fairness with Uncertainty in Sensitive Attributes
    Abhin Shah, Maohao Shen, J. Jon Ryu, Subhro Das, Prasanna Sattigeri, Yuheng Bu, and Gregory W. Wornell
    In Proc. IEEE Int. Symp. Inf. Theory (ISIT), July 2024
  2. Operator SVD with Neural Networks via Nested Low-Rank Approximation
    J. Jon Ryu, Xiangxiang Xu, H. S. Melichan Erol, Yuheng Bu, Lizhong Zheng, and Gregory W. Wornell
    In Proc. Int. Conf. Mach. Learn. (ICML), July 2024
  3. Gambling-Based Confidence Sequences for Bounded Random Vectors
    J. Jon Ryu and Gregory W. Wornell
    In Proc. Int. Conf. Mach. Learn. (ICML), July 2024
    Spotlight (top 3.5%)
  4. Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?
    Maohao Shen*, J. Jon Ryu*, Soumya Ghosh, Yuheng Bu, Prasanna Sattigeri, Subhro Das, and Gregory W. Wornell
    In Adv. Neural Inf. Proc. Syst. (NeurIPS), July 2024

2023

  1. On Universal Portfolios with Continuous Side Information
    Alankrita Bhatt*, J. Jon Ryu*, and Young-Han Kim
    In Proc. Int. Conf. Artif. Int. Statist. (AISTATS), April 2023

2022

  1. Parameter-Free Online Linear Optimization with Side Information via Universal Coin Betting
    J. Jon Ryu, Alankrita Bhatt, and Young-Han Kim
    In Proc. Int. Conf. Artif. Int. Statist. (AISTATS), March 2022

2021

  1. ISIT
    On the Role of Eigendecomposition in Kernel Embedding
    J. Jon Ryu, Jiun-Ting Huang, and Young-Han Kim
    In Proc. IEEE Int. Symp. Inf. Theory (ISIT), March 2021

2020

  1. ICASSP
    Feedback Recurrent Autoencoder
    Yang Yang, Guillaume Sautière, J. Jon Ryu, and Taco S. Cohen
    In Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), March 2020

2018

  1. ICIP
    Conditional distribution learning with neural networks and its application to universal image denoising
    Jongha Ryu and Young-Han Kim
    In Proc. IEEE Int. Conf. Image Proc. (ICIP), March 2018
  2. ITW
    Variations on a theme by Liu, Cuff, and Verdú: The power of posterior sampling
    Alankrita Bhatt, Jiun-Ting Huang, Young-Han Kim, J. Jon Ryu, and Pinar Sen
    In Proc. IEEE Inf. Theory Workshop (ITW), March 2018
  3. Allerton
    Monte Carlo methods for randomized likelihood decoding
    Alankrita Bhatt, Jiun-Ting Huang, Young-Han Kim, J. Jon Ryu, and Pinar Sen
    In Proc. Ann. Allerton Conf. Comm. Control Comput. (Allerton), March 2018

Miscellaneous

2022

  1. arXiv
    An Information-Theoretic Proof of Kac–Bernstein Theorem
    J. Jon Ryu and Young-Han Kim
    March 2022

2017

  1. arXiv
    Energy-based sequence GANs for recommendation and their connection to imitation learning
    Jaeyoon Yoo, Heonseok Ha, Jihun Yi, Jongha Ryu, Chanju Kim, Jung-Woo Ha, Young-Han Kim, and Sungroh Yoon
    March 2017

Theses

2022

  1. PhD thesis
    From Information Theory to Machine Learning Algorithms: A Few Vignettes
    Jongha Jon Ryu
    University of California San Diego, September 2022