Sejun Park
I am an assistant professor in the Department of Artificial Intelligence at Korea University. Prior to this, I was a postdoctoral fellow at Vector Institute for Artificial Intelligence, hosted by Murat A. Erdogdu. I received my Ph.D. degree at KAIST, advised by Jinwoo Shin. My current research interest is the theory of deep learning, e.g., analyzing expressivity, optimization properties, and generalization properties of machine learning models. I also worked on network pruning, probabilistic graphical models, Markov chain Monte Carlo, and combinatorial optimization.
Research topics that I am currently working on (but not limited to):
Developing time/memory efficient inference/learning algorithms with provable guarantees.
The correctness of automatic differentiation, efficient algorithms for computing generalized derivatives of neural networks.
Mathematically characterizing minimum-size machine learning models sufficient for performing target tasks.
Required width/depth/#parameters for universal approximation and memorization.
I am broadly interested in math problems in machine learning. If you are interested in joining our group, send me an email with your transcript and CV.
Conferences
[C20] What does automatic differentiation compute for neural networks?
Sejun Park*, Sanghyuk Chun*, Wonyeol Lee (*=equal contribution)
International Conference on Learning Representations (ICLR), 2024 (spotlight presentation)
[C19] Minimum width for universal approximation using ReLU networks on compact domain (pdf)
Namjun Kim, Chanho Min, Sejun Park
International Conference on Learning Representations (ICLR), 2024
[C18] On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters (pdf)
Wonyeol Lee^, Sejun Park^, Alex Aiken (^=corresponding authors)
International Conference on Machine Learning (ICML), 2023
[C17/W] Neural Networks Efficiently Learn Low-Dimensional Representations with SGD (pdf)
Alireza Mousavi-Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A. Erdogdu
International Conference on Learning Representations (ICLR), 2023 (spotlight presentation)
NeurIPS workshop on Optimization for Machine Learning, 2022
[C16/W] Guiding Energy-based Models via Contrastive Latent Variables (pdf)
Hankook Lee, Jongheon Jeong, Sejun Park, and Jinwoo Shin
International Conference on Learning Representations (ICLR), 2023 (spotlight presentation)
NeurIPS workshop on Self-Supervised Learning: Theory and Practice, 2022 (oral presentation)
[C15] Generalization Bounds for Stochastic Gradient Descent via Localized ε-Covers (pdf)
Sejun Park, Umut Simsekli, and Murat A. Erdogdu
Conference on Neural Information Processing Systems (NeurIPS), 2022
[C14/W] SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Adversarial Robustness (pdf)
Jongheon Jeong, Sejun Park, Minkyu Kim, Heung-Chang Lee, Doguk Kim, and Jinwoo Shin
Conference on Neural Information Processing Systems (NeurIPS), 2021
ICML workshop on A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning, 2021
[C13] Provable Memorization via Deep Neural Networks using Sub-linear Parameters (pdf)
Sejun Park, Jaeho Lee, Chulhee Yun, and Jinwoo Shin
Conference on Learning Theory (COLT), 2021
Part of this work was presented at Conference on the Mathematical Theory of Deep Neural Networks (DEEPMATH), 2020 (contributed talk)
[C12] Layer-adaptive Sparsity for the Magnitude-based Pruning (pdf)
Jaeho Lee, Sejun Park, Sangwoo Mo, Sungsoo Ahn, and Jinwoo Shin
International Conference on Learning Representations (ICLR), 2021
[C11] Minimum Width for Universal Approximation (pdf)
Sejun Park, Chulhee Yun, Jaeho Lee, and Jinwoo Shin
International Conference on Learning Representations (ICLR), 2021 (spotlight presentation)
Part of this work was presented at Conference on the Mathematical Theory of Deep Neural Networks (DEEPMATH), 2020 (contributed talk)
[C10] Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning (pdf)
Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, and Jinwoo Shin
Conference on Neural Information Processing Systems (NeurIPS), 2020
[C9] Learning Bounds for Risk-sensitive Learning (pdf)
Jaeho Lee, Sejun Park, and Jinwoo Shin
Conference on Neural Information Processing Systems (NeurIPS), 2020
[C8] Lookahead: A Far-sighted Alternative of Magnitude-based Pruning (pdf)
Sejun Park*, Jaeho Lee*, Sangwoo Mo, and Jinwoo Shin (*=equal contribution)
International Conference on Learning Representations (ICLR), 2020
[C7] Spectral Approximate Inference (pdf, poster, slide)
Sejun Park, Eunho Yang, Se-Young Yun, and Jinwoo Shin
International Conference on Machine Learning (ICML), 2019
[C6] Learning in Power Distribution Grids under Correlated Injections (pdf)
Sejun Park, Deepjyoti Deka, and Michael Chertkov
Asilomar Conference on Signals, Systems and Computers (ACSSC), 2018
[C5] Exact Topology and Parameter Estimation in Distribution Grids with Minimal Observability (pdf)
Sejun Park, Deepjyoti Deka, and Michael Chertkov
Power Systems Computation Conference (PSCC), 2018
[C4] Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models (pdf, poster)
Sejun Park, Yunhun Jang, Andreas Galanis, Jinwoo Shin, Daniel Stefankovic, and Eric Vigoda
International Conference on Artificial Intelligence and Statistics (AISTATS), 2017
[C3] Practical message-passing framework for large-scale combinatorial optimization (pdf)
Inho Cho, Soya Park, Sejun Park, Dongsu Han, and Jinwoo Shin
IEEE International Conference on Big Data, 2015
[C2] Minimum Weight Perfect Matching via Blossom Belief Propagation (pdf)
Sungsoo Ahn, Sejun Park, Michael Chertkov, and Jinwoo Shin
Conference on Neural Information Processing Systems (NIPS), 2015 (spotlight presentation)
[C1] Max-Product Belief Propagation for Linear Programming: Applications to Combinatorial Optimization (pdf)
Sejun Park and Jinwoo Shin
Conference on Uncertainty in Artificial Intelligence (UAI), 2015
Journals
[J4] Expressive Power of ReLU and Step Networks under Floating-Point Operations (pdf)
Yeachan Park*, Geonho Hwang*, Wonyeol Lee, Sejun Park (*=equal contribution)
Neural Networks, 2024 (accepted to appear)
[J3] Learning with End-Users in Distribution Grids: Topology and Parameter Estimation (pdf)
Sejun Park, Deepjyoti Deka, Scott Backhaus, and Michael Chertkov
IEEE Transactions on Control of Network Systems, 2020
[J2] Maximum Weight Matching using Odd-sized Cycles: Max-Product Belief Propagation and Half-Integrality (pdf)
Sungsoo Ahn*, Michael Chertkov*, Andrew E. Gelfand*, Sejun Park*, and Jinwoo Shin* (*=alphabetical order)
IEEE Transactions on Information Theory, 2018
[J1] Convergence and Correctness of Max-Product Belief Propagation for Linear Programming (pdf)
Sejun Park and Jinwoo Shin
SIAM Journal on Discrete Mathematics, 2017
Contact
Email: sejun.park000@gmail.com