Jang-Hyun Kim
Hello!
I am a PhD student in computer science at Seoul National University, advised by Hyun Oh Song.
I am currently at New York University as a visiting scholar, hosted by Kyunghyun Cho (~2024.08).
I previously interned at NAVER
AI in 2018.
I completed BSc with Mathematics at Seoul National University in 2019. My PhD studies are
supported by the Korea Foundation for Advanced Studies (KFAS).
Email   / 
CV   / 
Scholar   / 
Github   / 
Twitter
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Research
My research focuses on developing a robust and efficient machine learning system with a data-centric
approach, particularly leveraging the interplay between data and a model. Across various domains
including image, speech, and language, I address real-world challenges.
My key research observations are:
- [Data Generation] During training, models learn to locate informative parts of
the data for inference. Saliency-guided data augmentation can reinforce the training.
[Puzzle Mix,
Co-Mixup].
- [Data Compression] Trained models can compress datasets or contexts, enhancing the efficiency
of
training and inference processes.
[Context Memory,
Data Parameterization,
Spherical Principal Curves].
- [Data Identification] We can infer relationships within data by leveraging trained models,
facilitating the characterization of problematic data in large datasets.
[Neural Relation Graph].
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Compressed Context Memory For Online Language Model Interaction
Jang-Hyun Kim, Junyoung Yeom, Sangdoo Yun†, Hyun Oh
Song†
ICLR, 2024
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Code |
Project Page |
Bibtex
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Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data
Jang-Hyun Kim, Sangdoo Yun, Hyun Oh Song
NeurIPS, 2023
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Code |
Bibtex
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Dataset Condensation via Efficient Synthetic-Data Parameterization
Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun
Jeong, Jung-Woo Ha, Hyun Oh Song
ICML, 2022
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Code |
Bibtex
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Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble
Gaon An*, Seungyong Moon*, Jang-Hyun Kim, Hyun Oh Song
NeurIPS, 2021
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Code |
Bibtex
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Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
Jang-Hyun Kim, Wonho Choo, Hosan Jeong, Hyun Oh Song
ICLR (
Oral Presentation
), 2021
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Code |
Bibtex
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Spherical Principal Curves
Jongmin Lee*, Jang-Hyun Kim*, Hee-Seok Oh (*: equal contribution)
TPAMI, 2021 | R Journal, 2022
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R Journal |
Code |
Bibtex
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Puzzle Mix: Exploiting Saliency and Local statistics for Optimal Mixup
Jang-Hyun Kim, Wonho Choo, Hyun Oh Song
ICML, 2020
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Code |
Bibtex
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Phase-Aware Speech Enhancement with Deep Complex U-Net
Hyeong-Seok Choi, Jang-Hyun Kim, Jaesung Huh, Adrian Kim, Jung-Woo Ha, Kyogu Lee
arxiv, 2019
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Bibtex
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Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement
Jang-Hyun Kim*, Jaejun Yoo*, Sanghyuk Chun, Adrian Kim, Jung-Woo Ha
arxiv, 2018
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Code |
Bibtex |
Demo
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Google's Speaker Verification
Code |
Kaggle
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Caricature Generation
Code
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Image Mosaic via Mixed Integer Programming
Code
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Notes
Mathematical Backgrounds for Machine Learning,
An undergraduate dissertation (in Korean, 2018), Paper
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Academic Services
Workshop Program Committee
- First Workshop on Interpolation Regularizers and Beyond (NeurIPS 2022), Website
- Workshop on ImageNet: Past, Present, and Future (NeurIPS 2021), Website
Reviewing Activities
- NeurIPS (2021-), ICLR (2022-), ICML (2022-), TMLR (2022-)
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