Dohyun Kwon

Orcid: 0000-0001-9198-4735

Affiliations:
  • University of Seoul, Department of Mathematics, Dongdaemun, South Korea
  • University of Wisconsin-Madison, Department of Mathematics, WI, USA (former)
  • University of California Los Angeles, Department of Mathematics, CA, USA (PhD 2020)


According to our database1, Dohyun Kwon authored at least 12 papers between 2021 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Target Tracking Systems on a Sphere With Topographic Information.
IEEE Trans. Cybern., May, 2024

Memorization Capacity for Additive Fine-Tuning with Small ReLU Networks.
CoRR, 2024

Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models.
CoRR, 2024

On The Complexity of First-Order Methods in Stochastic Bilevel Optimization.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
Generalized Contrastive Divergence: Joint Training of Energy-Based Model and Diffusion Model through Inverse Reinforcement Learning.
CoRR, 2023

On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation.
CoRR, 2023

Multi-agent system for target tracking on a sphere and its asymptotic behavior.
Commun. Nonlinear Sci. Numer. Simul., 2023

Complexity of Block Coordinate Descent with Proximal Regularization and Applications to Wasserstein CP-dictionary Learning.
Proceedings of the International Conference on Machine Learning, 2023

A Fully First-Order Method for Stochastic Bilevel Optimization.
Proceedings of the International Conference on Machine Learning, 2023

2022
Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Training Wasserstein GANs without gradient penalties.
CoRR, 2021


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