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Jihun Hamm
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- affiliation: Tulane University, New Orleans, LA, USA
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2020 – today
- 2024
- [j7]Taotao Jing
, Haifeng Xia
Marginalized Augmented Few-Shot Domain Adaptation. IEEE Trans. Neural Networks Learn. Syst. 35(9): 12459-12469 (2024) - [c33]Akshay Mehra, Yunbei Zhang, Jihun Hamm:
Test-time Assessment of a Model's Performance on Unseen Domains via Optimal Transport. CVPR Workshops 2024: 173-182 - [c32]Janet Wang, Yunbei Zhang, Zhengming Ding, Jihun Hamm:
Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation. CVPR Workshops 2024: 5157-5166 - [c31]Akshay Mehra, Yunbei Zhang, Bhavya Kailkhura, Jihun Hamm:
On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization. WACV 2024: 3788-3799 - [i24]Akshay Mehra, Yunbei Zhang, Jihun Hamm:
Test-time Assessment of a Model's Performance on Unseen Domains via Optimal Transport. CoRR abs/2405.01451 (2024) - [i23]Yunbei Zhang, Akshay Mehra, Jihun Hamm:
Dynamic Domains, Dynamic Solutions: DPCore for Continual Test-Time Adaptation. CoRR abs/2406.10737 (2024) - [i22]Janet Wang, Yunsung Chung, Zhengming Ding, Jihun Hamm:
From Majority to Minority: A Diffusion-based Augmentation for Underrepresented Groups in Skin Lesion Analysis. CoRR abs/2406.18375 (2024) - [i21]Yunbei Zhang, Akshay Mehra, Jihun Hamm:
OT-VP: Optimal Transport-guided Visual Prompting for Test-Time Adaptation. CoRR abs/2407.09498 (2024) - [i20]Xin Hu, Janet Wang, Jihun Hamm, Rie Roselyne Yotsu, Zhengming Ding:
Enhancing Skin Disease Diagnosis: Interpretable Visual Concept Discovery with SAM Empowerment. CoRR abs/2409.09520 (2024) - 2023
- [c30]Yunsung Chung, Chanho Lim, Chao Huang, Nassir Marrouche, Jihun Hamm:
FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation. MILLanD@MICCAI 2023: 106-116 - [i19]Yunsung Chung, Chanho Lim, Chao Huang, Nassir Marrouche, Jihun Hamm:
FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation. CoRR abs/2306.15189 (2023) - [i18]Akshay Mehra, Yunbei Zhang, Jihun Hamm:
Analysis of Task Transferability in Large Pre-trained Classifiers. CoRR abs/2307.00823 (2023) - [i17]Janet Wang, Yunbei Zhang, Zhengming Ding, Jihun Hamm:
Can Domain Adaptation Improve Accuracy and Fairness of Skin Lesion Classification? CoRR abs/2307.03157 (2023) - [i16]Akshay Mehra, Yunbei Zhang, Bhavya Kailkhura, Jihun Hamm:
On the Fly Neural Style Smoothing for Risk-Averse Domain Generalization. CoRR abs/2307.08551 (2023) - 2022
- [j6]Xiaokuan Zhang
Defeating traffic analysis via differential privacy: a case study on streaming traffic. Int. J. Inf. Sec. 21(3): 689-706 (2022) - [j5]Taotao Jing
Augmented Multimodality Fusion for Generalized Zero-Shot Sketch-Based Visual Retrieval. IEEE Trans. Image Process. 31: 3657-3668 (2022) - [c29]Jiachen Sun
A Spectral View of Randomized Smoothing Under Common Corruptions: Benchmarking and Improving Certified Robustness. ECCV (4) 2022: 654-671 - [c28]Byunggill Joe, Insik Shin, Jihun Hamm:
Online Evasion Attacks on Recurrent Models: The Power of Hallucinating the Future. IJCAI 2022: 3121-3127 - [i15]Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm:
On Certifying and Improving Generalization to Unseen Domains. CoRR abs/2206.12364 (2022) - [i14]Byunggill Joe, Insik Shin, Jihun Hamm:
Online Evasion Attacks on Recurrent Models: The Power of Hallucinating the Future. CoRR abs/2207.09912 (2022) - 2021
- [c27]Akshay Mehra, Jihun Hamm:
Penalty Method for Inversion-Free Deep Bilevel Optimization. ACML 2021: 347-362 - [c26]Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm:
How Robust Are Randomized Smoothing Based Defenses to Data Poisoning? CVPR 2021: 13244-13253 - [c25]Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm:
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning. NeurIPS 2021: 17347-17359 - [i13]Byunggill Joe, Akshay Mehra, Insik Shin, Jihun Hamm:
Machine Learning with Electronic Health Records is vulnerable to Backdoor Trigger Attacks. CoRR abs/2106.07925 (2021) - [i12]Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm:
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning. CoRR abs/2107.03919 (2021) - [i11]Jiachen Sun, Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Dan Hendrycks, Jihun Hamm, Z. Morley Mao:
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines. CoRR abs/2112.00659 (2021) - 2020
- [i10]Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm:
How Robust are Randomized Smoothing based Defenses to Data Poisoning? CoRR abs/2012.01274 (2020) - [i9]Byunggill Joe, Jihun Hamm, Sung Ju Hwang, Sooel Son, Insik Shin:
Learning to Separate Clusters of Adversarial Representations for Robust Adversarial Detection. CoRR abs/2012.03483 (2020)
2010 – 2019
- 2019
- [c24]Xiaokuan Zhang, Jihun Hamm, Michael K. Reiter, Yinqian Zhang:
Statistical Privacy for Streaming Traffic. NDSS 2019 - [i8]Akshay Mehra, Jihun Hamm:
Penalty Method for Inversion-Free Deep Bilevel Optimization. CoRR abs/1911.03432 (2019) - 2018
- [j4]Yung-Kyun Noh
Fluid Dynamic Models for Bhattacharyya-Based Discriminant Analysis. IEEE Trans. Pattern Anal. Mach. Intell. 40(1): 92-105 (2018) - [c23]Jihun Hamm, Yung-Kyun Noh:
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning. ICML 2018: 1876-1884 - [i7]Akshay Mehra, Jihun Hamm, Mikhail Belkin:
Fast Interactive Image Retrieval using large-scale unlabeled data. CoRR abs/1802.04204 (2018) - [i6]Jihun Hamm, Yung-Kyun Noh:
K-Beam Subgradient Descent for Minimax Optimization. CoRR abs/1805.11640 (2018) - 2017
- [j3]Jihun Hamm:
Minimax Filter: Learning to Preserve Privacy from Inference Attacks. J. Mach. Learn. Res. 18: 129:1-129:31 (2017) - [c22]Jihun Hamm:
Enhancing utility and privacy with noisy minimax filters. ICASSP 2017: 6389-6393 - [c21]Jihun Hamm, Jackson Luken, Yani Xie:
Crowd-ML: A library for privacy-preserving machine learning on smart devices. ICASSP 2017: 6394-6398 - [i5]Jihun Hamm:
Machine vs Machine: Defending Classifiers Against Learning-based Adversarial Attacks. CoRR abs/1711.04368 (2017) - 2016
- [c20]Jihun Hamm, Yingjun Cao, Mikhail Belkin:
Learning privately from multiparty data. ICML 2016: 555-563 - [c19]Gordon Euhyun Moon, Jihun Hamm:
A Large-Scale Study in Predictability of Daily Activities and Places. MobiCASE 2016: 86-97 - [i4]Jihun Hamm, Paul Cao, Mikhail Belkin:
Learning Privately from Multiparty Data. CoRR abs/1602.03552 (2016) - [i3]Jihun Hamm:
Minimax Filter: Learning to Preserve Privacy from Inference Attacks. CoRR abs/1610.03577 (2016) - 2015
- [c18]Jihun Hamm:
Preserving Privacy of Continuous High-dimensional Data with Minimax Filters. AISTATS 2015 - [c17]Jihun Hamm, Adam C. Champion, Guoxing Chen, Mikhail Belkin, Dong Xuan:
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices. ICDCS 2015: 11-20 - [c16]Bohyung Han, Jihun Hamm:
Qualitative Tracking Performance Evaluation without Ground-Truth. WACV 2015: 55-62 - [i2]Jihun Hamm, Adam C. Champion, Guoxing Chen, Mikhail Belkin, Dong Xuan:
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices. CoRR abs/1501.02484 (2015) - [i1]Jihun Hamm, Mikhail Belkin:
Probabilistic Zero-shot Classification with Semantic Rankings. CoRR abs/1502.08039 (2015) - 2014
- [j2]Dong Hye Ye, Benoit Desjardins, Jihun Hamm, Harold Litt, Kilian M. Pohl
Regional Manifold Learning for Disease Classification. IEEE Trans. Medical Imaging 33(6): 1236-1247 (2014) - 2013
- [c15]Dong Hye Ye, Jihun Hamm, Benoit Desjardins, Kilian M. Pohl:
FLOOR: Fusing Locally Optimal Registrations. MICCAI (3) 2013: 195-202 - 2012
- [c14]Dong Hye Ye, Jihun Hamm, Kilian M. Pohl:
Combining regional metrics for disease-related brain population analysis. ISBI 2012: 1515-1518 - [c13]Dong Hye Ye, Jihun Hamm, Dongjin Kwon, Christos Davatzikos, Kilian M. Pohl:
Regional Manifold Learning for Deformable Registration of Brain MR Images. MICCAI (3) 2012: 131-138 - [c12]Jihun Hamm, Benjamin Stone
Automatic Annotation of Daily Activity from Smartphone-Based Multisensory Streams. MobiCASE 2012: 328-342 - 2011
- [c11]Bohyung Han, Jihun Hamm, Jack Sim:
Personalized video summarization with human in the loop. WACV 2011: 51-57 - 2010
- [j1]Jihun Hamm, Dong Hye Ye, Ragini Verma, Christos Davatzikos
GRAM: A framework for geodesic registration on anatomical manifolds. Medical Image Anal. 14(5): 633-642 (2010)
2000 – 2009
- 2009
- [c10]Jihun Hamm, Christos Davatzikos
Efficient Large Deformation Registration via Geodesics on a Learned Manifold of Images. MICCAI (1) 2009: 680-687 - 2008
- [c9]Jihun Ham, Daniel D. Lee:
Grassmann discriminant analysis: a unifying view on subspace-based learning. ICML 2008: 376-383 - [c8]Yung-Kyun Noh, Jihun Ham, Daniel D. Lee:
Regularized discriminant analysis for transformation-invariant object recognition. ICPR 2008: 1-5 - [c7]Jihun Ham, Daniel D. Lee:
Extended Grassmann Kernels for Subspace-Based Learning. NIPS 2008: 601-608 - [c6]Jihun Ham, Daniel D. Lee:
Learning a Warped Subspace Model of Faces with Images of Unknown Pose and Illumination. VISAPP (1) 2008: 219-226 - 2006
- [c5]Jihun Ham, Ikkjin Ahn, Daniel D. Lee:
Learning a manifold-constrained map between image sets: applications to matching and pose estimation. CVPR (1) 2006: 817-824 - [p1]Lawrence K. Saul, Kilian Q. Weinberger, Fei Sha, Jihun Ham, Daniel D. Lee:
Spectral Methods for Dimensionality Reduction. Semi-Supervised Learning 2006: 292-308 - 2005
- [c4]Jihun Ham, Daniel D. Lee, Lawrence K. Saul:
Semisupervised alignment of manifolds. AISTATS 2005: 120-127 - [c3]Jihun Ham, Yuanqing Lin, Daniel D. Lee:
Learning nonlinear appearance manifolds for robot localization. IROS 2005: 2971-2976 - [c2]Yuanqing Lin, Paul Vernaza, Jihun Ham, Daniel D. Lee:
Cooperative relative robot localization with audible acoustic sensing. IROS 2005: 3764-3769 - 2004
- [c1]Jihun Ham, Daniel D. Lee, Sebastian Mika, Bernhard Schölkopf:
A kernel view of the dimensionality reduction of manifolds. ICML 2004
Coauthor Index
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last updated on 2024-10-14 23:27 CEST by the dblp team
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