8 citations · 14 across the 9 of their papers we have counts for
13 papers
Dual-Anchor Acceleration Is Near-Optimal for Stochastic Monotone Root-Finding
TaeHo Yoon, Nicolas Loizou
Among distinct optimal acceleration mechanisms for deterministic monotone root-finding problems and fixed-point problems, dual-anchoring has recently been shown to admit a more rob…
Anchored Extra-Proximal Methods: Optimal Higher-Order Methods for Monotone Inclusion Problems
Ruichen Jiang, TaeHo Yoon
We study the deterministic oracle complexity of finding approximate solutions to composite monotone inclusion problems, formed by the sum of a smooth single-valued monotone operato…
Polyak-Type Extragradient Methods for Monotone Root-Finding Problems
TaeHo Yoon, Sayantan Choudhury, Ezra Greenberg +1
We study Polyak-type step-size selection for extragradient methods for solving deterministic and stochastic monotone root-finding problems. We show that the known projection-type c…
Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization
TaeHo Yoon, Nicolas Loizou
Acceleration for deterministic root-finding problems has been extensively studied in recent years; specifically, the anchor-based, or Halpern-type methods achieve optimal convergen…
On Same-Sample and Independent-Sample Stochastic Extragradient for Monotone Variational Inequalities
TaeHo Yoon, Nicolas Loizou
We study stochastic extragradient (SEG) methods for solving monotone variational inequality problems (VIPs) over a feasible set. Although extragradient is a foundational algorithm…
H-invariance theory: A complete characterization of minimax optimal fixed-point algorithms
TaeHo Yoon, Ernest K. Ryu, Benjamin Grimmer
For nonexpansive fixed-point problems, Halpern's method with optimal parameters, its so-called H-dual algorithm, and in fact, an infinite family of algorithms containing them, all…
Multiplayer Federated Learning: Reaching Equilibrium with Less Communication
TaeHo Yoon, Sayantan Choudhury, Nicolas Loizou
Traditional Federated Learning (FL) approaches assume collaborative clients with aligned objectives working towards a shared global model. However, in many real-world scenarios, cl…
Optimal Acceleration for Minimax and Fixed-Point Problems is Not Unique
TaeHo Yoon, Jaeyeon Kim, Jaewook J. Suh +1
Recently, accelerated algorithms using the anchoring mechanism for minimax optimization and fixed-point problems have been proposed, and matching complexity lower bounds establish…
Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback
TaeHo Yoon, Kibeom Myoung, Keon Lee +3
Diffusion models have recently shown remarkable success in high-quality image generation. Sometimes, however, a pre-trained diffusion model exhibits partial misalignment in the sen…
Accelerated Minimax Algorithms Flock Together
TaeHo Yoon, Ernest K. Ryu
Several new accelerated methods in minimax optimization and fixed-point iterations have recently been discovered, and, interestingly, they rely on a mechanism distinct from Nestero…
Robust Probabilistic Time Series Forecasting
TaeHo Yoon, Youngsuk Park, Ernest K. Ryu +1
Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties. Deep forecasting models, however, could…
Accelerated Algorithms for Smooth Convex-Concave Minimax Problems with Rate on Squared Gradient Norm
TaeHo Yoon, Ernest K. Ryu
In this work, we study the computational complexity of reducing the squared gradient magnitude for smooth minimax optimization problems. First, we present algorithms with accelerat…
WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points
Albert No, TaeHo Yoon, Sehyun Kwon +1
Generative adversarial networks (GAN) are a widely used class of deep generative models, but their minimax training dynamics are not understood very well. In this work, we show tha…