- Large-Scale Convex Optimization: Algorithms & Analyses via Monotone Operators. E. K. Ryu and W. Yin,
*Cambridge University Press*2022.

- LoRA Training in the NTK Regime has No Spurious Local Minima. U. Jang, J. D. Lee, and E. K. Ryu,
*International Conference on Machine Learning (Oral, top 144/9473=1.5% of papers)*, 2024. - Optimal Acceleration for Minimax and Fixed-Point Problems is Not Unique. T. Yoon, J. Kim, J. J. Suh, E. K. Ryu,
*International Conference on Machine Learning (Spotlight, top (144+191)/9473=3.5% of papers)*, 2024. - Simple Drop-in LoRA Conditioning on Attention Layers Will Improve Your Diffusion Model. J. Y. Choi, J. R. Park, I. Park, J. Cho, A. No, and E. K. Ryu,
*Manuscript*, 2024. - Convergence Analyses of Davis–Yin Splitting via Scaled Relative Graphs. J. Lee, S. Yi, and E. K. Ryu,
*SIAM Journal on Optimization*, 2024. - Accelerated Minimax Algorithms Flock Together. T. Yoon and E. K. Ryu,
*SIAM Journal on Optimization*, 2024. - Optimal First-Order Algorithms as a Function of Inequalities. C. Park and E. K. Ryu,
*Journal of Machine Learning Research*, 2024. - Image Clustering Conditioned on Text Criteria. S. Kwon, J. Park, M. Kim, J. Cho, E. K. Ryu, and K. Lee,
*International Conference on Learning Representations*, 2024. - Branch-and-Bound Performance Estimation Programming: A Unified Methodology for Constructing Optimal Optimization Methods. S. Das Gupta, B. P. G. Van Parys, and E. K. Ryu,
*Mathematical Programming Series A*, 2024. - Mirror Duality in Convex Optimization. J. Kim, C. Park, A. Ozdaglar, J. Diakonikolas, and E. K. Ryu,
*Manuscript*, 2023. - Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback. T. Yoon, K. Myoung, K. Lee, J. Cho, A. No, and E. K. Ryu,
*Neural Information Processing Systems*, 2023. - Time-Reversed Dissipation Induces Duality Between Minimizing Gradient Norm and Function Value. J. Kim, A. Ozdaglar, C.Park, and E. K. Ryu,
*Neural Information Processing Systems*, 2023. - Accelerating Value Iteration with Anchoring. J. Lee and E. K. Ryu,
*Neural Information Processing Systems*, 2023. - Continuous-Time Analysis of Anchor Acceleration. J. J. Suh, J. Park, and E. K. Ryu,
*Neural Information Processing Systems*, 2023. - Computer-Assisted Design of Accelerated Composite Optimization Methods: OptISTA. U. Jang, S. Das Gupta, and E. K. Ryu,
*Manuscript*, 2023. - Coordinate-Update Algorithms can Efficiently Detect Infeasible Optimization Problems. J. Paeng, J. Park, and E. K. Ryu,
*Manuscript*, 2023. - Rotation and Translation Invariant Representation Learning with Implicit Neural Representations. S. Kwon, J. Y. Choi, and E. K. Ryu,
*International Conference on Machine Learning*, 2023. - Accelerated Infeasibility Detection of Constrained Optimization and Fixed-Point Iterations. J. Park and E. K. Ryu,
*International Conference on Machine Learning*, 2023. - Factor-$\sqrt{2}$ Acceleration of Accelerated Gradient Methods. C. Park, J. Park, and E. K. Ryu,
*Applied Mathematics & Optimization*, 2023. - Convergence Analyses of Davis–Yin Splitting via Scaled Relative Graphs II: Convex Optimization Problems. S. Yi and E. K. Ryu,
*Manuscript*, 2022. - Exact Optimal Accelerated Complexity for Fixed-Point Iterations. J. Park and E. K. Ryu,
*International Conference on Machine Learning (long presentation, top 118/5630=2% of papers)*, 2022. - Continuous-Time Analysis of AGM via Conservation Laws in Dilated Coordinate Systems. J. J. Suh, G. Roh, and E. K. Ryu,
*International Conference on Machine Learning (long presentation, top 118/5630=2% of papers)*, 2022. - Neural Tangent Kernel Analysis of Deep Narrow Neural Networks. J. Lee, J. Y. Choi, E. K. Ryu, and A. No,
*International Conference on Machine Learning*, 2022. - Robust Probabilistic Time Series Forecasting. T. Yoon, Y. Park, E. K. Ryu, and Y. Wang,
*International Conference on Artificial Intelligence and Statistics*, 2022. - Scaled Relative Graph: Nonexpansive operators via 2D Euclidean Geometry. E. K. Ryu, R. Hannah, and W. Yin,
*Mathematical Programming Series A*, 2022. - A Geometric Structure of Acceleration and Its Role in Making Gradients Small Fast. J. Lee, C. Park, and E. K. Ryu,
*Neural Information Processing Systems*, 2021. - Accelerated Algorithms for Smooth Convex-Concave Minimax Problems with $\mathcal{O}(1/k^2)$ Rate on Squared Gradient Norm. T. Yoon and E. K. Ryu,
*International Conference on Machine Learning (long presentation, top 166/5513=3% of papers)*, 2021. - WGAN with an Infinitely Wide Generator Has No Spurious Stationary Points. A. No, T. Yoon, S. Kwon, and E. K. Ryu,
*International Conference on Machine Learning*, 2021. - Decentralized Proximal Gradient Algorithms with Linear Convergence Rates. S. A. Alghunaim, E. K. Ryu, K. Yuan, and A. H. Sayed,
*IEEE Transactions on Automatic Control*, 2021. - Tight Coefficients of Averaged Operators via Scaled Relative Graph. X. Huang, E. K. Ryu, and W. Yin,
*Journal of Mathematical Analysis and Applications*, 2020. - Scaled Relative Graph of Normal Matrices. X. Huang, E. K. Ryu, and W. Yin,
*Manuscript*. - Operator Splitting Performance Estimation: Tight Contraction Factors and Optimal Parameter Selection. E. K. Ryu, A. B. Taylor, C. Bergeling, P. Giselsson,
*SIAM Journal on Optimization*, 2020. Code. - Splitting with Near-Circulant Linear Systems: Applications to Total Variation CT and PET. E. K. Ryu, S. Ko, and J.-H. Won,
*SIAM Journal on Scientific Computing*, 2020. Code, Slides. - Linear Convergence of Cyclic SAGA. Y. Park, E. K. Ryu,
*Optimization Letters*, 2020. - Finding the Forward-Douglas-Rachford-Forward Method. E. K. Ryu and B. C. Vũ,
*Journal of Optimization Theory and Applications*, 2020. - Uniqueness of DRS as the 2 Operator Resolvent-Splitting and Impossibility of 3 Operator Resolvent-Splitting. E. K. Ryu,
*Mathematical Programming Series A*, 2020. Slides, Video (Overview), Video (Proof of Theorem 1), Video (Proof of Theorem 2), Video (Proof of Theorem 3), Video (Proof of Theorem 4). - Plug-and-Play Methods Provably Converge with Properly Trained Denoisers. E. K. Ryu, J. Liu, S. Wang, X. Chen, Z. Wang, and W. Yin,
*International Conference on Machine Learning*, 2019. Code, Slides, Video. - Douglas-Rachford Splitting and ADMM for Pathological Convex Optimization. E. K. Ryu, Y. Liu, and W. Yin,
*Computational Optimization and Applications*, 2019. Slides. - A New Use of Douglas-Rachford Splitting for Identifying Infeasible, Unbounded, and Pathological Conic Programs. Y. Liu, E. K. Ryu, and W. Yin,
*Mathematical Programming Series A*, 2019. - Vector and Matrix Optimal Mass Transport: Theory, Algorithm, and Applications. E. K. Ryu, Y. Chen, W. Li, and S. Osher,
*SIAM Journal on Scientific Computing*, 2018. Code. - Cosmic Divergence, Weak Cosmic Convergence, and Fixed Points at Infinity. E. K. Ryu,
*Journal of Fixed Point Theory and Applications*, 2018. - Unbalanced and Partial L1 Monge-Kantorovich Problem: A Scalable Parallel First-Order Method. E. K. Ryu, W. Li, P. Yin, and S. Osher,
*Journal of Scientific Computing*, 2018, Slides. - A Parallel Method for Earth Mover's Distance. W. Li, E. K. Ryu, S. Osher, W. Yin, and W. Gangbo,
*Journal of Scientific Computing*, 2018. Code. - Convex Optimization for Monte Carlo: Stochastic Optimization for Importance Sampling. E. K. Ryu, Stanford University PhD thesis, Advisor: Stephen P. Boyd, 2016.
- A Primer on Monotone Operator Methods. E. K. Ryu and S. Boyd,
*Applied and Computational Mathematics*, 2016. - Risk-Constrained Kelly Gambling. E. Busseti, E. K. Ryu, and S. Boyd,
*Journal of Investing*, 2016. - Extensions of Gauss Quadrature via Linear Programming. E. K. Ryu and S. Boyd,
*Foundations of Computational Mathematics*, 2015. - Computing Reaction Rates in Bio-molecular Systems Using Discrete Macro-states. E. Darve and E. K. Ryu. In T. Schlick, editor,
*Innovations in Biomolecular Modeling and Simulations.*Royal Society of Chemistry, 2012. - Structural Characterization of Unsaturated Phosphatidylcholines Using Traveling Wave Ion Mobility Spectrometry. H. Kim, H. Kim, E. Pang, E. K. Ryu, L. Beegle, J. Loo, W. Goddard, and I. Kanik.
*Analytical Chemistry*, 2009.

- LoRA can Replace Time and Class Embeddings in Diffusion Probabilistic Models. J. Y. Choi, J. Park, I. Park, J. Cho, A. No, and E. K. Ryu,
*NeurIPS Workshop on Diffusion Models*, 2023. - Diffusion Probabilistic Models Generalize when They Fail to Memorize. T. Yoon, J. Y. Choi, S. Kwon, and E. K. Ryu,
*ICML Workshop on Structured Probabilistic Inference & Generative Modeling*, 2023.