Sejun Park

I am a postdoctoral researcher at Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST), working with Jinwoo Shin. My research interests were on probabilistic graphical models, Markov chain Monte Carlo and combinatorial optimization. My recent research interests are on the expressive power of neural networks. (CV, last updated on 210114)

Education

    Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, Mar. 2014 - Aug. 2020
    Ph.D. in Electrical Engineering

    Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, Feb. 2010 - Feb. 2014
    B.S. in Electrical Engineering and Mathematical Science (double major)

Preprints


[P1] Provable Memorization via Deep Neural Networks using Sub-linear Parameters (pdf)

Sejun Park, Jaeho Lee, Chulhee Yun, and Jinwoo Shin
Part of this work was presented at Conference on the Mathematical Theory of Deep Neural Networks (DEEPMATH), 2020 (contributed talk)

Conferences

[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, and Jinwoo Shin
Neural Information Processing Systems (NeurIPS), 2020

[C9] Learning Bounds for Risk-sensitive Learning (pdf)

Jaeho Lee, Sejun Park, and Jinwoo Shin
N
eural 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 (invited)

[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
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

[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 (SIDMA), 2017

Contact

Email: sejun.park@kaist.ac.kr