Distribution aligning refinery of pseudo-label for imbalanced semi-supervised learning J Kim, Y Hur, S Park, E Yang, SJ Hwang, J Shin
Advances in neural information processing systems 33, 14567-14579, 2020
166 2020 Minimum width for universal approximation S Park, C Yun, J Lee, J Shin
arXiv preprint arXiv:2006.08859, 2020
120 2020 Layer-adaptive sparsity for the magnitude-based pruning J Lee, S Park, S Mo, S Ahn, J Shin
arXiv preprint arXiv:2010.07611, 2020
116 2020 Lookahead: A far-sighted alternative of magnitude-based pruning S Park, J Lee, S Mo, J Shin
arXiv preprint arXiv:2002.04809, 2020
99 2020 Exact Topology and Parameter Estimation in Distribution Grids with Minimal Observability S Park, D Deka, M Chertkov
Power Systems Computation Conference (PSCC), 2018, 2018
84 2018 Learning bounds for risk-sensitive learning J Lee, S Park, J Shin
Advances in Neural Information Processing Systems 33, 13867-13879, 2020
46 2020 Smoothmix: Training confidence-calibrated smoothed classifiers for certified robustness J Jeong, S Park, M Kim, HC Lee, DG Kim, J Shin
Advances in Neural Information Processing Systems 34, 30153-30168, 2021
43 2021 Neural networks efficiently learn low-dimensional representations with sgd A Mousavi-Hosseini, S Park, M Girotti, I Mitliagkas, MA Erdogdu
arXiv preprint arXiv:2209.14863, 2022
40 2022 Learning with end-users in distribution grids: Topology and parameter estimation S Park, D Deka, S Backhaus, M Chertkov
IEEE Transactions on Control of Network Systems 7 (3), 1428-1440, 2020
34 2020 Provable memorization via deep neural networks using sub-linear parameters S Park, J Lee, C Yun, J Shin
Conference on Learning Theory, 3627-3661, 2021
30 2021 Max-product belief propagation for linear programming: applications to combinatorial optimization S Park, J Shin
arXiv preprint arXiv:1412.4972, 2014
11 2014 Generalization Bounds for Stochastic Gradient Descent via Localized -Covers S Park, U Simsekli, MA Erdogdu
Advances in Neural Information Processing Systems 35, 2790-2802, 2022
10 2022 Rapid mixing Swendsen-Wang sampler for stochastic partitioned attractive models S Park, Y Jang, A Galanis, J Shin, D Stefankovic, E Vigoda
Artificial Intelligence and Statistics, 440-449, 2017
8 2017 Minimum weight perfect matching via blossom belief propagation SS Ahn, S Park, M Chertkov, J Shin
Advances in neural information processing systems 28, 2015
8 2015 Guiding Energy-based Models via Contrastive Latent Variables H Lee, J Jeong, S Park, J Shin
arXiv preprint arXiv:2303.03023, 2023
7 2023 A deeper look at the layerwise sparsity of magnitude-based pruning J Lee, S Park, S Mo, S Ahn, J Shin
arXiv preprint arXiv:2010.07611 2 (3), 2020
6 2020 Spectral approximate inference S Park, E Yang, SY Yun, J Shin
International Conference on Machine Learning, 5052-5061, 2019
5 2019 Convergence and correctness of max-product belief propagation for linear programming S Park, J Shin
SIAM Journal on Discrete Mathematics 31 (3), 2228-2246, 2017
4 2017 Practical message-passing framework for large-scale combinatorial optimization I Cho, S Park, S Park, D Han, J Shin
2015 IEEE international conference on big data (Big Data), 24-31, 2015
3 2015 Learning in power distribution grids under correlated injections S Park, D Deka
2018 52nd Asilomar Conference on Signals, Systems, and Computers, 1863-1868, 2018
2 2018