Hyun Oh Song (original) (raw)
| 1 Gwanak ro, Gwanak gu, Seoul 08826 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 combinatorial problems in artificial intelligence. |  |
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Email / Google Scholar / Github (mllab) / Github (personal) / LinkedIn
News
- I'll serve as an Area chair at NeurIPS 2021~2025, ICML 2023~2025, and a Senior program committee member at AAAI 2022.
- 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 1.1MVCseedfundinginMay2021andadditional1.1M VC seed funding in May 2021 and additional 1.1MVCseedfundinginMay2021andadditional1.1M government research funding in July 2021. We are actively hiring!
- 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.
For prospective students
- For undergrads at SNU, you should take my deep learning class (M2177.0043) and machine learning class (4190.666). For math & stat, I recommend you to take these classes: 300.203A (881.007), M1407.000600 (881.008), 881.319, 326.211, 326.311, 3341.454. For CS, please take: M1522.0009, 4190.407.
- It's best to first apply for undergraduate internship. Please contact (via email) at least one semester before your graduate application for the internship.
Selected publications
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GuidedQuant: Large Language Model Quantization via Exploiting End Loss GuidanceJinuk Kim, Marwa El Halabi, Wonpyo Park, Clemens JS Schaefer, Deokjae Lee, Yeonhong Park, Jae W. Lee, Hyun Oh Song International Conference on Machine Learning (ICML), 2025 paper / code / bibtex / project page |
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LayerMerge: Neural Network Depth Compression through Layer Pruning and MergingJinuk Kim, Marwa El Halabi, Mingi Ji, Hyun Oh Song International Conference on Machine Learning (ICML), 2024 paper / code / bibtex / project page / poster |
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Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial OptimizationDeokjae Lee, Hyun Oh Song, Kyunghyun Cho International Conference on Machine Learning (ICML), 2024 paper / code / bibtex |
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Compressed Context Memory For Online Language Model InteractionJang-Hyun Kim, Junyoung Yeom, Sangdoo Yun, Hyun Oh Song International Conference on Learning Representations (ICLR), 2024 paper / code / bibtex / project page |
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Direct Preference-based Policy Optimization without Reward ModelingGaon An*, Junhyeok Lee*, Xingdong Zuo, Norio Kosaka, Kyung-Min Kim, Hyun Oh Song Neural Information Processing Systems (NeurIPS), 2023 paper / code / bibtex |
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Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier DataJang-Hyun Kim, Sangdoo Yun, Hyun Oh Song Neural Information Processing Systems (NeurIPS), 2023 paper / code / bibtex |
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Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive LearningSeungyong Moon, Junyoung Yeom, Bumsoo Park, Hyun Oh Song Neural Information Processing Systems (NeurIPS), 2023 paper / code / bibtex |
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Query-Efficient Black-Box Red Teaming via Bayesian OptimizationDeokjae 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 |
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Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic ProgrammingJinuk Kim*, Yeonwoo Jeong*, Deokjae Lee, Hyun Oh Song International Conference on Machine Learning (ICML), 2023 paper / code / bibtex |
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Rethinking Value Function Learning for Generalization in Reinforcement LearningSeungyong Moon, JunYeong Lee, Hyun Oh Song Neural Information Processing Systems (NeurIPS), 2022 paper / code / bibtex |
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Dataset Condensation via Efficient Synthetic-Data ParameterizationJang-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 |
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Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian OptimizationDeokjae Lee, Seungyong Moon, Junhyeok Lee, Hyun Oh Song International Conference on Machine Learning (ICML), 2022 paper / code / bibtex |
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Optimal channel selection with discrete QCQPYeonwoo Jeong*, Deokjae Lee*, Gaon An, Changyong Son, Hyun Oh Song International Conference on Artificial Intelligence and Statistics (AISTATS), 2022 paper / code / bibtex |
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Preemptive Image Robustification for Protecting Users against Man-in-the-Middle Adversarial AttacksSeungyong Moon*, Gaon An*, Hyun Oh Song AAAI Conference on Artificial Intelligence (AAAI), 2022 paper / code / bibtex |
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Uncertainty-Based Offline Reinforcement Learning with Diversified Q-EnsembleGaon An*, Seungyong Moon*, Jang-Hyun Kim, Hyun Oh Song Neural Information Processing Systems (NeurIPS), 2021 paper / code / bibtex |
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Co-Mixup: Saliency Guided Joint Mixup with Supermodular DiversityJang-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 |
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Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal MixupJang-Hyun Kim, Wonho Choo, Hyun Oh Song International Conference on Machine Learning (ICML), 2020 paper / supp / code / bibtex / talk video |
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End-to-End Efficient Representation Learning via Cascading Combinatorial OptimizationYeonwoo Jeong, Yoonsung Kim, Hyun Oh Song IEEE Computer Vision and Pattern Recognition (CVPR), 2019 paper / supp / code / bibtex |
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Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial OptimizationSeungyong 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 |
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Learning Discrete and Continuous Factors of Data via Alternating DisentanglementYeonwoo Jeong, Hyun Oh Song International Conference on Machine Learning (ICML), 2019 paper / supp / code / bibtex / talk video |
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EMI: Exploration with Mutual InformationHyoungseok 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 |
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Efficient end-to-end learning for quantizable representationsYeonwoo Jeong, Hyun Oh Song International Conference on Machine Learning (ICML), 2018 Long talk (213/2473=8.6%) paper / code / bibtex / talk video |
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Deep Metric Learning via Facility LocationHyun 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 |
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Learning Transferrable Representations for Unsupervised Domain AdaptationOzan Sener, Hyun Oh Song, Ashutosh Saxena, Silvio Savarese Neural Information Processing Systems (NIPS), 2016 paper / bibtex |
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Deep Metric Learning via Lifted Structured Feature EmbeddingHyun 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 |
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Generalized Sparselet Models for Real-Time Multiclass Object RecognitionHyun 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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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
Teaching
Erdös = 3 (via Pedro Felzenszwalb) / Dijkstra = 4 (via Sergey Levine)