Publications

Conference and journal papers are labeled [C#] and [J#], respectively. Lab members are in bold. Papers also presented at a workshop are labeled with /W.

[C26] On Minimum Depth and Width of Floating-Point Neural Networks for Representing Floating-Point Functions
Sejun Park, Yeachan Park, and Geonho Hwang.
International Conference on Machine Learning (ICML), 2026 (oral presentation)

[C25] On the Expressive Power of Floating-Point Transformers
Sejun Park, Yeachan Park, and Geonho Hwang.
International Conference on Machine Learning (ICML), 2026

[C24] Floating-Point Networks with Automatic Differentiation Can Represent Almost All Floating-Point Functions and Their Gradients
Sejun Park, Yeachan Park, and Geonho Hwang.
International Conference on Machine Learning (ICML), 2026

[C23] Minimum Width for Universal Approximation using Squashable Activation Functions
Jonghyun Shin, Namjun Kim, Geonho Hwang, and Sejun Park.
International Conference on Machine Learning (ICML), 2025

[C22] Floating-Point Neural Networks Can Represent Almost All Floating-Point Functions
Geonho Hwang, Yeachan Park, Wonyeol Lee, and Sejun Park.
International Conference on Machine Learning (ICML), 2025

[C21] Floating-Point Neural Networks Are Provably Robust Universal Approximators
Geonho Hwang, Wonyeol Lee, Yeachan Park, Sejun Park^, and Feras Saad. (^: corresponding author)
International Conference on Computer Aided Verification (CAV), 2025

[J4] Expressive Power of ReLU and Step Networks under Floating-Point Operations
Yeachan Park*, Geonho Hwang*, Wonyeol Lee, and Sejun Park. (*: equal contribution)
Neural Networks, 2024

[C20] What does automatic differentiation compute for neural networks?
Sejun Park*, Sanghyuk Chun*, and 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
Namjun Kim, Chanho Min, and Sejun Park.
International Conference on Learning Representations (ICLR), 2024

[C18] On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters
Wonyeol Lee^, Sejun Park^, and Alex Aiken. (^: corresponding authors)
International Conference on Machine Learning (ICML), 2023

[C17/W] Neural Networks Efficiently Learn Low-Dimensional Representations with SGD
Alireza Mousavi-Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, and 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
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
Sejun Park, Umut Simsekli, and Murat A. Erdogdu.
Advances in Neural Information Processing Systems (NeurIPS), 2022

[C14/W] SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness
Jongheon Jeong, Sejun Park, Minkyu Kim, Heung-Chang Lee, Doguk Kim, and Jinwoo Shin.
Advances in 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
Sejun Park, Jaeho Lee, Chulhee Yun, and Jinwoo Shin.
Conference on Learning Theory (COLT), 2021
Conference on the Mathematical Theory of Deep Neural Networks (DEEPMATH), 2020 (contributed talk)

[C12] Layer-adaptive Sparsity for the Magnitude-based Pruning
Jaeho Lee, Sejun Park, Sangwoo Mo, Sungsoo Ahn, and Jinwoo Shin.
International Conference on Learning Representations (ICLR), 2021

[C11] Minimum Width for Universal Approximation
Sejun Park, Chulhee Yun, Jaeho Lee, and Jinwoo Shin.
International Conference on Learning Representations (ICLR), 2021 (spotlight presentation)
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
Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, and Jinwoo Shin.
Advances in Neural Information Processing Systems (NeurIPS), 2020

[C9] Learning Bounds for Risk-sensitive Learning
Jaeho Lee, Sejun Park, and Jinwoo Shin.
Advances in Neural Information Processing Systems (NeurIPS), 2020

[J3] Learning With End-Users in Distribution Grids: Topology and Parameter Estimation
Sejun Park, Deepjyoti Deka, Scott Backhaus, and Michael Chertkov.
IEEE Transactions on Control of Network Systems, 2020

[C8] Lookahead: A Far-Sighted Alternative of Magnitude-based Pruning
Sejun Park*, Jaeho Lee*, Sangwoo Mo, and Jinwoo Shin. (*: equal contribution)
International Conference on Learning Representations (ICLR), 2020

[C7] Spectral Approximate Inference
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
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
Sejun Park, Deepjyoti Deka, and Michael Chertkov.
Power Systems Computation Conference (PSCC), 2018

[J2] Maximum Weight Matching Using Odd-Sized Cycles: Max-Product Belief Propagation and Half-Integrality
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
Sejun Park and Jinwoo Shin.
SIAM Journal on Discrete Mathematics, 2017

[C4] Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models
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
Inho Cho, Soya Park, Sejun Park, Dongsu Han, and Jinwoo Shin.
IEEE International Conference on Big Data (BigData), 2015

[C2] Minimum Weight Perfect Matching via Blossom Belief Propagation
Sungsoo Ahn, Sejun Park, Michael Chertkov, and Jinwoo Shin.
Advances in Neural Information Processing Systems (NeurIPS), 2015 (spotlight presentation)

[C1] Max-Product Belief Propagation for Linear Programming: Applications to Combinatorial Optimization
Sejun Park and Jinwoo Shin.
Conference on Uncertainty in Artificial Intelligence (UAI), 2015