Research Interests
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Optimization for training deep neural networks
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AutoML: automating the tasks of applying machine learning to real-world problems
•
Trustworthy and real-world AI/ML Challenges
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Federated Learning: train an algorithm across multiple decentralized edge devices
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Title
Conference and Journal
Author
Keyword
Specification
Link
Open
Arxiv
1st author
Precipitation Nowcasting
Benchmark Dataset
Under Review
Open
Workshop
ICML2022
1st author
Federated Learning
AutoML
Image Classification
Architecture Search
International
Top-tier
Oral
Under Review
Open
Workshop
ICML2022
1st author
Architecture Search
Bayesian Optimization
Knowledge Distillation
Orthogonality Regularization
International
Top-tier
Under Review
Open
Workshop
NeurIPS2022
Co-author
Federated Learning
International
Top-tier
Open
IEEE Access
1st author
Orthogonality Regularization
International
Journal
Open
Arxiv
Co-author
Hyperparameter Optimization
Graph Neural Network
Working In Progress
Open
Arxiv
Technical Report
1st author
Federated Learning
Hyperparameter Optimization
Technical Report
Open
NeurIPS2021
1st author
Noisy Label Classification
Image Classification
SVD
International
Top-tier
Open
IJCAI2021
1st author
Knowledge Distillation
Noisy Label Classification
Image Classification
International
Top-tier
Open
Competition
NeurIPS2020
1st author
AutoML
Hyperparameter Optimization
Bayesian Optimization
Multi-armed Bandit
International
8th rank
Open
PMLR
Competition
NeurIPS2019
1st author
Orthogonality Regularization
Architecture Search
Augmentation
Model Compression
Image Classification
International
Top-tier
2nd rank
Open
Master Thesis
KAIST
1st author
Adversarial Attack
Orthogonality Regularization
Image Classification
Thesis
Open
Competition
NeurIPS2022
1st author
Precipitation Nowcasting
Segmentation
International
Top-tier
4th rank
Open
Workshop
NeurIPS2022
Co-author
Multi-Label Classification
Efficient
Transfer Learning
International
Top-tier