From Information Theory to Machine Learning Algorithms: A Few Vignettes - Jongha Jon Ryu (2022)

Group Fairness with Uncertainty in Sensitive Attributes. - Abhin Shah; Maohao Shen; Jongha Jon Ryu; Subhro Das; Prasanna Sattigeri; Yuheng Bu; Gregory W. Wornell (CoRR, 2023)

Operator SVD with Neural Networks via Nested Low-Rank Approximation. - Jongha Jon Ryu; Xiangxiang Xu 001; Hasan Sabri Melihcan Erol; Yuheng Bu; Lizhong Zheng; Gregory W. Wornell (ICML, 2024)

Gambling-Based Confidence Sequences for Bounded Random Vectors. - Jongha Jon Ryu; Gregory W. Wornell (ICML, 2024)

Group Fairness with Uncertain Sensitive Attributes. - Abhin Shah; Maohao Shen; Jongha Jon Ryu; Subhro Das; Prasanna Sattigeri; Yuheng Bu; Gregory W. Wornell (ISIT, 2024)

Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage? - Maohao Shen; Jongha Jon Ryu; Soumya Ghosh; Yuheng Bu; Prasanna Sattigeri; Subhro Das; Gregory W. Wornell (NeurIPS, 2024)

Improved Evidential Deep Learning via a Mixture of Dirichlet Distributions. - Jongha Jon Ryu; Maohao Shen; Soumya Ghosh; Yuheng Bu; Prasanna Sattigeri; Subhro Das; Gregory W. Wornell (CoRR, 2024)

A Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimation. - Jongha Jon Ryu; Abhin Shah; Gregory W. Wornell (CoRR, 2024)

Score-of-Mixture Training: Training One-Step Generative Models Made Simple via Score Estimation of Mixture Distributions. - Tejas Jayashankar; Jongha Jon Ryu; Gregory W. Wornell (CoRR, 2025)

Alignment as Distribution Learning: Your Preference Model is Explicitly a Language Model. - Jihun Yun; Juno Kim; Jongho Park; Junhyuck Kim; Jongha Jon Ryu; Jaewoong Cho; Kwang-Sung Jun (CoRR, 2025)