| 2026 | ACL | Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction. | Sejun Park, Yoonah Park, Jongwon Lim, Yohan Jo |
| 2025 | CAV | Floating-Point Neural Networks are Provably Robust Universal Approximators. | Geonho Hwang, Wonyeol Lee, Yeachan Park, Sejun Park, Feras Saad |
| 2025 | ICML | Floating-Point Neural Networks Can Represent Almost All Floating-Point Functions. | Geonho Hwang, Yeachan Park, Wonyeol Lee, Sejun Park |
| 2025 | ICML | Minimum Width for Universal Approximation using Squashable Activation Functions. | Jonghyun Shin, Namjun Kim, Geonho Hwang, Sejun Park |
| 2024 | ICLR | Minimum width for universal approximation using ReLU networks on compact domain. | Namjun Kim, Chanho Min, Sejun Park |
| 2024 | ICLR | What does automatic differentiation compute for neural networks? | Sejun Park, Sanghyuk Chun, Wonyeol Lee |
| 2023 | ICIS | Status Regain and Validator Performance: Evidence from Blockchain Platform. | Sejun Park, Alain Pinsonneault, Warut Khern-am-nuai |
| 2023 | ICLR | Neural Networks Efficiently Learn Low-Dimensional Representations with SGD. | Alireza Mousavi-Hosseini, Sejun Park, Manuela Girotti, Ioannis Mitliagkas, Murat A. Erdogdu |
| 2023 | ICLR | Guiding Energy-based Models via Contrastive Latent Variables. | Hankook Lee, Jongheon Jeong, Sejun Park, Jinwoo Shin |
| 2023 | ICML | On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters. | Wonyeol Lee, Sejun Park, Alex Aiken |
| 2023 | ICML | Towards Understanding Ensemble Distillation in Federated Learning. | Sejun Park, Kihun Hong, Ganguk Hwang |
| 2021 | COLT | Provable Memorization via Deep Neural Networks using Sub-linear Parameters. | Sejun Park, Jaeho Lee, Chulhee Yun, Jinwoo Shin |
| 2021 | ICLR | Layer-adaptive Sparsity for the Magnitude-based Pruning. | Jaeho Lee, Sejun Park, Sangwoo Mo, Sungsoo Ahn, Jinwoo Shin |
| 2021 | ICLR | Minimum Width for Universal Approximation. | Sejun Park, Chulhee Yun, Jaeho Lee, Jinwoo Shin |
| 2020 | ICLR | Lookahead: A Far-sighted Alternative of Magnitude-based Pruning. | Sejun Park, Jaeho Lee, Sangwoo Mo, Jinwoo Shin |
| 2019 | ICML | Spectral Approximate Inference. | Sejun Park, Eunho Yang, Se-Young Yun, Jinwoo Shin |
| 2018 | ACSSC | Learning in Power Distribution Grids under Correlated Injections. | Sejun Park, Deepjyoti Deka, Michael Chertkov |
| 2017 | AISTATS | Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models. | Sejun Park, Yunhun Jang, Andreas Galanis, Jinwoo Shin, Daniel Stefankovic, Eric Vigoda |
| 2015 | UAI | Max-Product Belief Propagation for Linear Programming: Applications to Combinatorial Optimization. | Sejun Park, Jinwoo Shin |