Preprints
Controllable Machine Unlearning via Gradient Pivoting
Youngsik Hwang and Dong-Young Lim, preprint [pdf]
Flatness-Aware Stochastic Gradient Langevin Dynamics
Stefano Bruno, Youngsik Hwang, Jaehyeon An, Sotirios Sabanis, and Dong-Young Lim, preprint [pdf]
Global Sharpness-Aware Minimization Is Suboptimal in Domain Generalization: Towards Individual Sharpness-Aware Minimization
Youngjun Song, Youngsik Hwang, Jonghun Lee, Heechang Lee, and Dong-Young Lim, preprint [pdf]
Continuum Dropout for Neural Differential Equations
Jonghun Lee, Yongkyung Oh, Sungil Kim, and Dong-Young Lim, preprint [pdf]
Dual Cone Gradient Descent for Multi-Objective Optimization and Its Applications
Youngsik Hwang and Dong-Young Lim, preprint [pdf][slides]
Neural Information Processing Systems (NeurIPS) 2024
The conference version of this paper was entitled "Dual cone gradient descent for training physics-informed neural networks". [pdf]
Publications
TANDEM: Temporal Attention-guided Neural Differential Equations for Missingness in Time Series Classification
Yongkyung Oh, Dong-Young Lim, Sungil Kim, and Alex Bui
Conference on Information and Knowledge Management (CIKM), 2025 [pdf]
Langevin Dynamics Based Algorithm e-THεO POULA for Stochastic Optimization Problems with Discontinuous Stochastic Gradient
Dong-Young Lim, Ariel Neufeld, Sotirios Sabanis, and Ying Zhang
Mathematics of Operations Research, 2025 [pdf]
presented at the Isaac Newton Institute workshop "Diffusions in Machine Learning" [slides]
On Diffusion-Based Generative Models and Their Error Bounds: The Log-Concave Case with Full Convergence Estimates
Stefano Bruno, Ying Zhang, Dong-Young Lim, Ömer Deniz Akyildiz, and Sotirios Sabanis
Transactions on Machine Learning Research (TMLR), 2025 [pdf]
DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysis
Yongkyung Oh, Dong-Young Lim, and Sungil Kim
AAAI Conference on Artificial Intelligence (AAAI), 2025 [pdf]
Polygonal Unadjusted Langevin Algorithms: Creating Stable and Efficient Adaptive Algorithms for Neural Networks
Dong-Young Lim and Sotirios Sabanis
Journal of Machine Learning Reserach (JMLR), 2024 [pdf]
Interational Conference on Machine Learning (ICML), 2024
Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
Yongkyung Oh, Dong-Young Lim, and Sungil Kim
International Conference on Learning Representations (ICLR), Spotlight, 2024 [pdf]
Nonasymptotic Estimates for TUSLA Algorithm for Nonconvex Learning with Applications to Neural Networks with ReLU Activation Function
Dong-Young Lim, Ariel Neufeld, Sotirios Sabanis, and Ying Zhang
IMA Journal of Numerical Analysis, 2024 [pdf]
Stop-Loss Adjusted Labels for Machine Learning-based Trading of Risky Assets
Yoontae Hwang, Junpyo Park, Yongjae Lee, and Dong-Young Lim
Finance Research Letters, 2023 [pdf]
Static Replication of Barrier-Type Options via Integral Equations
Kyoung-Kuk Kim and Dong-Young Lim
Quantitative Finance, 2021 [pdf]
Learning Multi-Market Microstructure from Order Book Data
Geonhwan Ju, Kyoung-Kuk Kim, and Dong-Young Lim
Quantitative Finance, 2019 [pdf]
Recursive Method for Static Replication of Autocallable Structured Products
Kyoung-Kuk Kim and Dong-Young Lim
Quantitative Finance, 2019 [pdf]
Risk Analysis and Hedging of Parisian Options under a Jump-Diffusion Model
Kyoung-Kuk Kim and Dong-Young Lim
Journal of Futures Markets, 2016 [pdf]