Jihun Hamm
18 papers · 2008–2026 · 10 venues · across top CS/AI venues
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Achievements
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Research topics
Machine Learning
(14)
Artificial Intelligence
(9)
Application Areas
(9)
Optimization & Theory
(8)
Core AI
(7)
Deep Learning
(6)
Learning Types Β· Machine Learning
(6)
Learning Paradigms
(3)
Learning Types Β· Deep Learning
(3)
Mathematics & Optimization
(2)
Core Methods
(2)
Optimization
(2)
Computer Vision
(1)
Healthcare & Medicine
(1)
Security & Privacy
(1)
Keywords
minimax optimization
(3)
privacy preservation
(2)
differential privacy
(2)
adversarial learning
(2)
bilevel optimization
(2)
domain adaptation
(2)
test-time adaptation
(2)
domain shift
(2)
data poisoning
(2)
domain generalization
(2)
high-dimensional data
(2)
certified robustness
(1)
transfer learning
(1)
subspace learning
(1)
black-box optimization
(1)
vision transformer
(1)
representation learning
(1)
autonomous driving
(1)
knowledge transfer
(1)
few-shot learning
(1)
Papers
Prime Once, then Reprogram Locally: An Efficient Alternative to Black-Box Service Model Adaptation
CVPR 2026
Seeing Clearly, Reasoning Confidently: Plug-and-Play Remedies for Vision Language Model Blindness
CVPR 2026
DPCore: Dynamic Prompt Coreset for Continual Test-Time Adaptation
ICML 2025
Enhancing Skin Disease Diagnosis: Interpretable Visual Concept Discovery with SAM
WACV 2025
OT-VP: Optimal Transport-Guided Visual Prompting for Test-Time Adaptation
WACV 2025
SOFA: Deep Learning Framework for Simulating and Optimizing Atrial Fibrillation Ablation
MICCAI 2025
On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization
WACV 2024
Understanding the Transferability of Representations via Task-Relatedness
NeurIPS 2024
A Spectral View of Randomized Smoothing under Common Corruptions: Benchmarking and Improving Certified Robustness
ECCV 2022
Online Evasion Attacks on Recurrent Models:The Power of Hallucinating the Future
IJCAI 2022
How Robust Are Randomized Smoothing Based Defenses to Data Poisoning?
CVPR 2021
Penalty Method for Inversion-Free Deep Bilevel Optimization
ACML 2021
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning
NeurIPS 2021
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning
ICML 2018
Minimax Filter: Learning to Preserve Privacy from Inference Attacks
JMLR 2017
Learning privately from multiparty data
ICML 2016
Preserving Privacy of Continuous High-dimensional Data with Minimax Filters
AISTATS 2015
Extended Grassmann Kernels for Subspace-Based Learning
NeurIPS 2008