TaeHo Yoon



I'm a Rufus Isaacs Postdoctoral Fellow at Johns Hopkins University,
Department of Applied Mathematics & Statistics.
I study optimization and theoretical aspects of machine learning.

Selected publications

Multiplayer Federated Learning: Reaching Equilibrium with Less Communication

TaeHo Yoon, Sayantan Choudhury, Nicolas Loizou

optimization federated-learning multiplayer-game-theory

Optimal Acceleration for Minimax and Fixed-Point Problems is Not Unique

TaeHo Yoon, Jaeyeon Kim, Jaewook J. Suh, Ernest K. Ryu

optimization minimax-optimization fixed-point acceleration H-duality

Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback

TaeHo Yoon, Kibeom Myoung, Keon Lee, Jaewoong Cho, Albert No and Ernest K. Ryu

generative-models diffusion-models controlled-generation RLHF

Diffusion Probabilistic Models Generalize when They Fail to Memorize

TaeHo Yoon, Joo Young Choi, Sehyun Kwon and Ernest K. Ryu

generative-models diffusion-models generalization

Accelerated Minimax Algorithms Flock Together

TaeHo Yoon and Ernest K. Ryu

optimization minimax-optimization acceleration

Robust Probabilistic Time Series Forecasting

TaeHo Yoon, Youngsuk Park, Ernest K. Ryu and Yuyang Wang

probabilistic-forecasting time-series adversarial-robustness

Accelerated Algorithms for Smooth Convex-Concave Minimax Problems with $\mathcal{O}(1/k^2)$ Rate on Squared Gradient Norm

TaeHo Yoon and Ernest K. Ryu

optimization minimax-optimization acceleration

WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points

Albert No, TaeHo Yoon, Sehyun Kwon and Ernest K. Ryu

generative-models GANs infinite-neural-networks