Hyun Oh Song

60 5th Ave, New York, NY 10011

I'm an associate professor in the Department of Computer Science and Engineering at Seoul National University. Previously, I was a research scientist at Google Research, in Mountain View, where I worked on machine learning research in Kevin Murphy's team. Before Google, I was a postdoctoral fellow in SAIL in the Computer Science Department at Stanford University.

I received my Ph.D. in Computer Science at UC Berkeley in 2014, where I worked with Trevor Darrell and Stefanie Jegelka. My graduate study was fully supported by Samsung Lee Kun Hee Scholarship Foundation for five years. In summer 2013, I spent time at LEAR, INRIA as a visiting student researcher. I did my B.S. at Hanyang University.

My research interests are in machine learning, combinatorial optimization, and algorithms. Broadly, I'm interested in solving challenging problems in artificial intelligence.

Email  /  Google Scholar  /  Github (personal)  /  Github (mllab)  /  LinkedIn

News

  • I'll serve as a virtual deep-dive session chair at NeurIPS 2022 on offline reinforcement learning.
  • I gave an invited talk at Samsung AI Forum 2022 on learning with combinatorial structures.
  • I founded an AI health care startup, DeepMetrics. We raised $1.1M VC seed funding in May 2021 and additional $1.1M government research funding in July 2021. We are actively hiring!
  • I'll serve as an Area chair at NeurIPS 2021, 2022, 2023, ICML 2023, 2024, and a Senior program committee member at AAAI 2022.
  • Tensorflow code for the suite of deep metric learning methods I worked on at Google is officially open sourced at TensorFlow Addons.
  • I'm moving to Seoul National University as an assistant professor starting Sep 2017.
  • I'll join Google Research as a research scientist starting July, 2016.
  • I'll serve as an Oral and Spotlight session chair at CVPR 2016.


For prospective students


Selected publications
ICLR2024

Compressed Context Memory For Online Language Model Interaction
Jang-Hyun Kim, Junyoung Yeom, Sangdoo Yun, Hyun Oh Song
International Conference on Learning Representations (ICLR), 2024
paper / code / bibtex / project page

NeurIPS2023

Direct Preference-based Policy Optimization without Reward Modeling
Gaon An*, Junhyeok Lee*, Xingdong Zuo, Norio Kosaka, Kyung-Min Kim, Hyun Oh Song
Neural Information Processing Systems (NeurIPS), 2023
paper / code / bibtex

NeurIPS2023

Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data
Jang-Hyun Kim, Sangdoo Yun, Hyun Oh Song
Neural Information Processing Systems (NeurIPS), 2023
paper / code / bibtex

NeurIPS2023

Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning
Seungyong Moon, Junyoung Yeom, Bumsoo Park, Hyun Oh Song
Neural Information Processing Systems (NeurIPS), 2023
paper / code / bibtex

ACL2023

Query-Efficient Black-Box Red Teaming via Bayesian Optimization
Deokjae Lee, JunYeong Lee, Jung-Woo Ha, Jin-Hwa Kim, Sang-Woo Lee, Hwaran Lee, Hyun Oh Song
Association for Computational Linguistics (ACL), 2023
paper / code / bibtex

ICML2023

Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming
Jinuk Kim*, Yeonwoo Jeong*, Deokjae Lee, Hyun Oh Song
International Conference on Machine Learning (ICML), 2023
paper / code / bibtex

NeurIPS2022

Rethinking Value Function Learning for Generalization in Reinforcement Learning
Seungyong Moon, JunYeong Lee, Hyun Oh Song
Neural Information Processing Systems (NeurIPS), 2022
paper / code / bibtex

ICML2022

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
International Conference on Machine Learning (ICML), 2022
paper / code / bibtex

ICML2022

Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization
Deokjae Lee, Seungyong Moon, Junhyeok Lee, Hyun Oh Song
International Conference on Machine Learning (ICML), 2022
paper / code / bibtex

AISTAT2022

Optimal channel selection with discrete QCQP
Yeonwoo Jeong*, Deokjae Lee*, Gaon An, Changyong Son, Hyun Oh Song
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
paper / code / bibtex

AAAI2022

Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial Attacks
Seungyong Moon*, Gaon An*, Hyun Oh Song
AAAI Conference on Artificial Intelligence (AAAI), 2022
paper / code / bibtex

Neurips2021

Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble
Gaon An*, Seungyong Moon*, Jang-Hyun Kim, Hyun Oh Song
Neural Information Processing Systems (NeurIPS), 2021
paper / code / bibtex

ICLR2021

Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
Jang-Hyun Kim, Wonho Choo, Hosan Jeong, Hyun Oh Song
International Conference on Learning Representations (ICLR), 2021
Oral presentation (53/2997=1.7%)
paper / supp / code / bibtex

ICML2020

Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Jang-Hyun Kim, Wonho Choo, Hyun Oh Song
International Conference on Machine Learning (ICML), 2020
paper / supp / code / bibtex / talk video

CVPR2019

End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization
Yeonwoo Jeong, Yoonsung Kim, Hyun Oh Song
IEEE Computer Vision and Pattern Recognition (CVPR), 2019
paper / supp / code / bibtex

ICML2019b

Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization
Seungyong Moon*, Gaon An*, Hyun Oh Song
International Conference on Machine Learning (ICML), 2019
Long talk (159/3424=4.6%)
paper / supp / code / bibtex / talk video

ICML2019c

Learning Discrete and Continuous Factors of Data via Alternating Disentanglement
Yeonwoo Jeong, Hyun Oh Song
International Conference on Machine Learning (ICML), 2019
paper / supp / code / bibtex / talk video

ICML2019a

EMI: Exploration with Mutual Information
Hyoungseok Kim*, Jaekyeom Kim*, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song
International Conference on Machine Learning (ICML), 2019
Long talk (159/3424=4.6%)
paper / supp / code / bibtex / talk video

icml2018

Efficient end-to-end learning for quantizable representations
Yeonwoo Jeong, Hyun Oh Song
International Conference on Machine Learning (ICML), 2018
Long talk (213/2473=8.6%)
paper / code / bibtex / talk video

cvpr2017

Deep Metric Learning via Facility Location
Hyun Oh Song, Stefanie Jegelka, Vivek Rathod, Kevin Murphy
IEEE Computer Vision and Pattern Recognition (CVPR), 2017
Spotlight presentation (144/2680=5.3%)
paper / code / bibtex / talk video

nips2016

Learning Transferrable Representations for Unsupervised Domain Adaptation
Ozan Sener, Hyun Oh Song, Ashutosh Saxena, Silvio Savarese
Neural Information Processing Systems (NIPS), 2016
paper / bibtex

cvpr2016

Deep Metric Learning via Lifted Structured Feature Embedding
Hyun Oh Song, Yu Xiang, Stefanie Jegelka, Silvio Savarese
IEEE Computer Vision and Pattern Recognition (CVPR), 2016
Spotlight presentation (123/2145=5.7%)
paper / code / dataset / bibtex / talk video

Sparselet_TPAMI2014

Generalized Sparselet Models for Real-Time Multiclass Object Recognition
Hyun Oh Song, Ross Girshick, Stefan Zickler, Christopher Geyer, Pedro Felzenszwalb, Trevor Darrell
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015
paper / code / demo video 1 / demo video 2 / bibtex

TASE2014

Learning to detect visual grasp affordance
Hyun Oh Song, Mario Fritz, Daniel Goehring, Trevor Darrell
IEEE Transactions on Automation Science and Engineering (TASE), 2015
paper / demo video / bibtex

OBOD_ICML14

Weakly-supervised Discovery of Visual Pattern Configurations
Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka, Trevor Darrell
Neural Information Processing Systems (NIPS), 2014
paper / bibtex

OBOD_ICML14

On learning to localize objects with minimal supervision
Hyun Oh Song, Ross Girshick, Stefanie Jegelka, Julien Mairal, Zaid Harchaoui, Trevor Darrell
International Conference on Machine Learning (ICML), 2014
paper / talk video / code / bibtex

DAS_ICML13

Discriminatively Activated Sparselets
Hyun Oh Song*, Ross Girshick*, Trevor Darrell
International Conference on Machine Learning (ICML), 2013
Long talk (143/1204=11.8%)
paper / slide / poster / supp / bibtex

Bbank_ACMMM12

Detection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition
Tim Althoff, Hyun Oh Song, Trevor Darrell
ACM Multimedia (ACMMM), 2012
paper / poster / bibtex

Sparselets_ECCV12

Sparselet Models for Efficient Multiclass Object Detection
Hyun Oh Song, Stefan Zickler, Tim Althoff, Ross Girshick, Mario Fritz, Christopher Geyer, Pedro Felzenszwalb, Trevor Darrell
European Conference on Computer Vision (ECCV), 2012
paper / demo video / poster / bibtex / code


Awards
SLSF_Logo

Samsung Lee Kun Hee Scholarship Foundation
5 year Ph.D. fellowship


Teaching
4190.666

M2177.0043 Introduction to Deep Learning
Spring 2018/2019/2020/2021/2022/2023

4190.666 Machine Learning
Fall 2017/2018/2019/2020/2021/2022

033.015 Probability and Computing (Eng. Math. II)
Fall 2018/2019/2020/2021/2022

CS70

CS70 Discrete Mathematics and Probability Theory
GSI, UC Berkeley, 2012


Erdös = 3 (via Pedro Felzenszwalb)   /   Dijkstra = 4 (via Sergey Levine)

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