1 citations · 1 across the 6 of their papers we have counts for
6 papers
Linear Bandits with Partially Observable Features
Wonyoung Kim, Sungwoo Park, Garud Iyengar +2
We study the linear bandit problem that accounts for partially observable features. Without proper handling, unobserved features can lead to linear regret in the decision horizon $…
HFI: A unified framework for training-free detection and implicit watermarking of latent diffusion model generated images
Sungik Choi, Hankook Lee, Jaehoon Lee +3
Dramatic advances in the quality of the latent diffusion models (LDMs) also led to the malicious use of AI-generated images. While current AI-generated image detection methods assu…
Mean-field Chaos Diffusion Models
Sungwoo Park, Dongjun Kim, Ahmed Alaa
In this paper, we introduce a new class of score-based generative models (SGMs) designed to handle high-cardinality data distributions by leveraging concepts from mean-field theory…
Parameter-Free Algorithms for Performative Regret Minimization under Decision-Dependent Distributions
Sungwoo Park, Junyeop Kwon, Byeongnoh Kim +3
This paper studies performative risk minimization, a formulation of stochastic optimization under decision-dependent distributions. We consider the general case where the performat…
InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists
Yulu Gan, Sungwoo Park, Alexander Schubert +2
Recent advances in generative diffusion models have enabled text-controlled synthesis of realistic and diverse images with impressive quality. Despite these remarkable advances, th…
Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning
Sungjun Cho, Seunghyuk Cho, Sungwoo Park +3
Real-world graphs naturally exhibit hierarchical or cyclical structures that are unfit for the typical Euclidean space. While there exist graph neural networks that leverage hyperb…