Ernest K. Ryu

According to our database, Ernest K. Ryu authored at least 51 papers between 2012 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

STORK: Improving the Fidelity of Mid-NFE Sampling for Diffusion and Flow Matching Models.

Zheng Tan, Weizhen Wang, Andrea L. Bertozzi, Ernest K. Ryu

CoRR, May, 2025

Decomposing Complex Visual Comprehension into Atomic Visual Skills for Vision Language Models.

Hyunsik Chae, Seungwoo Yoon, Jaden Park, Chloe Yewon Chun, Yongin Cho, Mu Cai, Yong Jae Lee, Ernest K. Ryu

CoRR, May, 2025

LoRA Training Provably Converges to a Low-Rank Global Minimum or It Fails Loudly (But it Probably Won't Fail).

Junsu Kim, Jaeyeon Kim, Ernest K. Ryu

CoRR, February, 2025

Deflated Dynamics Value Iteration.

Jongmin Lee, Amin Rakhsha, Ernest K. Ryu, Amir-massoud Farahmand

Trans. Mach. Learn. Res., 2025

Accelerated Minimax Algorithms Flock Together.

Taeho Yoon, Ernest K. Ryu

SIAM J. Optim., 2025

Convergence Analyses of Davis-Yin Splitting via Scaled Relative Graphs.

Jongmin Lee, Soheun Yi, Ernest K. Ryu

SIAM J. Optim., 2025

Encryption-Friendly LLM Architecture.

Donghwan Rho, Taeseong Kim, Minje Park, Jung Woo Kim, Hyunsik Chae, Ernest K. Ryu, Jung Hee Cheon

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Optimal Non-Asymptotic Rates of Value Iteration for Average-Reward Markov Decision Processes.

Jongmin Lee, Ernest K. Ryu

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Publisher Correction: Branch-and-bound performance estimation programming: a unified methodology for constructing optimal optimization methods.

Shuvomoy Das Gupta, Bart P. G. Van Parys, Ernest K. Ryu

Math. Program., March, 2024

Branch-and-bound performance estimation programming: a unified methodology for constructing optimal optimization methods.

Shuvomoy Das Gupta, Bart P. G. Van Parys, Ernest K. Ryu

Math. Program., March, 2024

Simple Drop-in LoRA Conditioning on Attention Layers Will Improve Your Diffusion Model.

Joo Young Choi, Jaesung R. Park, Inkyu Park, Jaewoong Cho, Albert No, Ernest K. Ryu

Trans. Mach. Learn. Res., 2024

Optimal First-Order Algorithms as a Function of Inequalities.

Chanwoo Park, Ernest K. Ryu

J. Mach. Learn. Res., 2024

Numerical Analysis of HiPPO-LegS ODE for Deep State Space Models.

Jaesung R. Park, Jaewook J. Suh, Ernest K. Ryu

CoRR, 2024

Optimization Algorithm Design via Electric Circuits.

Stephen P. Boyd, Tetiana Parshakova, Ernest K. Ryu, Jaewook J. Suh

CoRR, 2024

Task Diversity Shortens the ICL Plateau.

Jaeyeon Kim, Sehyun Kwon, Joo Young Choi, Jongho Park, Jaewoong Cho, Jason D. Lee, Ernest K. Ryu

CoRR, 2024

Gradient-free Decoder Inversion in Latent Diffusion Models.

Seongmin Hong, Suh Yoon Jeon, Kyeonghyun Lee, Ernest K. Ryu, Se Young Chun

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Optimization Algorithm Design via Electric Circuits.

Stephen Boyd, Tetiana Parshakova, Ernest K. Ryu, Jaewook J. Suh

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

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

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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

LoRA Training in the NTK Regime has No Spurious Local Minima.

Uijeong Jang, Jason D. Lee, Ernest K. Ryu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Image Clustering Conditioned on Text Criteria.

Sehyun Kwon, Jaeseung Park, Minkyu Kim, Jaewoong Cho, Ernest K. Ryu, Kangwook Lee

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback.

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Continuous-time Analysis of Anchor Acceleration.

Jaewook J. Suh, Jisun Park, Ernest K. Ryu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Accelerating Value Iteration with Anchoring.

Jongmin Lee, Ernest K. Ryu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Time-Reversed Dissipation Induces Duality Between Minimizing Gradient Norm and Function Value.

Jaeyeon Kim, Asuman E. Ozdaglar, Chanwoo Park, Ernest K. Ryu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Accelerated Infeasibility Detection of Constrained Optimization and Fixed-Point Iterations.

Jisun Park, Ernest K. Ryu

Proceedings of the International Conference on Machine Learning, 2023

Rotation and Translation Invariant Representation Learning with Implicit Neural Representations.

Sehyun Kwon, Joo Young Choi, Ernest K. Ryu

Proceedings of the International Conference on Machine Learning, 2023

2022

Scaled relative graphs: nonexpansive operators via 2D Euclidean geometry.

Ernest K. Ryu, Robert Hannah, Wotao Yin

Math. Program., 2022

Continuous-Time Analysis of Accelerated Gradient Methods via Conservation Laws in Dilated Coordinate Systems.

Jaewook J. Suh, Gyumin Roh, Ernest K. Ryu

Proceedings of the International Conference on Machine Learning, 2022

Exact Optimal Accelerated Complexity for Fixed-Point Iterations.

Jisun Park, Ernest K. Ryu

Proceedings of the International Conference on Machine Learning, 2022

Neural Tangent Kernel Analysis of Deep Narrow Neural Networks.

Jongmin Lee, Joo Young Choi, Ernest K. Ryu, Albert No

Proceedings of the International Conference on Machine Learning, 2022

Robust Probabilistic Time Series Forecasting.

Taeho Yoon, Youngsuk Park, Ernest K. Ryu, Yuyang Wang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates.

Sulaiman A. Alghunaim, Ernest K. Ryu, Kun Yuan, Ali H. Sayed

IEEE Trans. Autom. Control., 2021

A Geometric Structure of Acceleration and Its Role in Making Gradients Small Fast.

Jongmin Lee, Chanwoo Park, Ernest K. Ryu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Accelerated Algorithms for Smooth Convex-Concave Minimax Problems with O(1/k^2) Rate on Squared Gradient Norm.

Taeho Yoon, Ernest K. Ryu

Proceedings of the 38th International Conference on Machine Learning, 2021

WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points.

Albert No, Taeho Yoon, Sehyun Kwon, Ernest K. Ryu

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Splitting with Near-Circulant Linear Systems: Applications to Total Variation CT and PET.

Ernest K. Ryu, Seyoon Ko, Joong-Ho Won

SIAM J. Sci. Comput., 2020

Operator Splitting Performance Estimation: Tight Contraction Factors and Optimal Parameter Selection.

Ernest K. Ryu, Adrien B. Taylor, Carolina Bergeling, Pontus Giselsson

SIAM J. Optim., 2020

Linear convergence of cyclic SAGA.

Youngsuk Park, Ernest K. Ryu

Optim. Lett., 2020

Uniqueness of DRS as the 2 operator resolvent-splitting and impossibility of 3 operator resolvent-splitting.

Ernest K. Ryu

Math. Program., 2020

Finding the Forward-Douglas-Rachford-Forward Method.

Ernest K. Ryu, Bang Công Vu

J. Optim. Theory Appl., 2020

Scaled Relative Graph of Normal Matrices.

Xinmeng Huang, Ernest K. Ryu, Wotao Yin

CoRR, 2020

2019

A new use of Douglas-Rachford splitting for identifying infeasible, unbounded, and pathological conic programs.

Yanli Liu, Ernest K. Ryu, Wotao Yin

Math. Program., 2019

ODE Analysis of Stochastic Gradient Methods with Optimism and Anchoring for Minimax Problems and GANs.

Ernest K. Ryu, Kun Yuan, Wotao Yin

CoRR, 2019

Douglas-Rachford splitting and ADMM for pathological convex optimization.

Ernest K. Ryu, Yanli Liu, Wotao Yin

Comput. Optim. Appl., 2019

Plug-and-Play Methods Provably Converge with Properly Trained Denoisers.

Ernest K. Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang, Wotao Yin

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Vector and Matrix Optimal Mass Transport: Theory, Algorithm, and Applications.

Ernest K. Ryu, Yongxin Chen, Wuchen Li, Stanley J. Osher

SIAM J. Sci. Comput., 2018

Unbalanced and Partial L₁ Monge-Kantorovich Problem: A Scalable Parallel First-Order Method.

Ernest K. Ryu, Wuchen Li, Penghang Yin, Stanley J. Osher

J. Sci. Comput., 2018

A Parallel Method for Earth Mover's Distance.

Wuchen Li, Ernest K. Ryu, Stanley J. Osher, Wotao Yin, Wilfrid Gangbo

J. Sci. Comput., 2018

2017

A New Use of Douglas-Rachford Splitting and ADMM for Identifying Infeasible, Unbounded, and Pathological Conic Programs.

Yanli Liu, Ernest K. Ryu, Wotao Yin

CoRR, 2017

2015

Extensions of Gauss Quadrature Via Linear Programming.

Ernest K. Ryu, Stephen P. Boyd

Found. Comput. Math., 2015

2012

Stochastic Kronecker Graph on Vertex-Centric BSP

Ernest K. Ryu, Sean Choi

CoRR, 2012