| An adaptive stochastic sequential quadratic programming with differentiable exact augmented lagrangians S Na, M Anitescu, M Kolar Mathematical Programming, 1-71, 2022 | 29 | 2022 |
| Estimating differential latent variable graphical models with applications to brain connectivity S Na, M Kolar, O Koyejo Biometrika 108 (2), 425-442, 2021 | 24 | 2021 |
| The graph-based behavior-aware recommendation for interactive news M Ma, S Na, H Wang, C Chen, J Xu Applied Intelligence 52 (2), 1913-1929, 2022 | 22 | 2022 |
| Inequality constrained stochastic nonlinear optimization via active-set sequential quadratic programming S Na, M Anitescu, M Kolar Mathematical Programming 202 (1), 279-353, 2023 | 21 | 2023 |
| AEGCN: An autoencoder-constrained graph convolutional network M Ma, S Na, H Wang Neurocomputing 432, 21-31, 2021 | 21 | 2021 |
| High-dimensional varying index coefficient models via Stein's identity S Na, Z Yang, Z Wang, M Kolar Journal of Machine Learning Research 20 (152), 1-44, 2019 | 19 | 2019 |
| On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control S Na, S Shin, M Anitescu, VM Zavala IEEE Transactions on Automatic Control, 2020 | 14 | 2020 |
| Exponential decay in the sensitivity analysis of nonlinear dynamic programming S Na, M Anitescu SIAM Journal on Optimization 30 (2), 1527-1554, 2020 | 14 | 2020 |
| Hessian averaging in stochastic Newton methods achieves superlinear convergence S Na, M Dereziński, MW Mahoney Mathematical Programming 201 (1), 473-520, 2023 | 12 | 2023 |
| SFGAE: a self-feature-based graph autoencoder model for miRNA–disease associations prediction M Ma, S Na, X Zhang, C Chen, J Xu Briefings in Bioinformatics 23 (5), bbac340, 2022 | 12 | 2022 |
| High-dimensional index volatility models via stein’s identity S Na, M Kolar Bernoulli 27 (2), 794-817, 2021 | 12 | 2021 |
| Superconvergence of online optimization for model predictive control S Na, M Anitescu IEEE Transactions on Automatic Control, 2020 | 11 | 2020 |
| Asymptotic convergence rate and statistical inference for stochastic sequential quadratic programming S Na, MW Mahoney arXiv preprint arXiv:2205.13687, 2022 | 8 | 2022 |
| Convergence Analysis of Accelerated Stochastic Gradient Descent under the Growth Condition YL Chen, S Na, M Kolar To appear in Mathematics of Operations Research, 2021 | 8* | 2021 |
| A fast temporal decomposition procedure for long-horizon nonlinear dynamic programming S Na, M Anitescu, M Kolar Mathematics of Operations Research, 2021 | 7 | 2021 |
| Semiparametric nonlinear bipartite graph representation learning with provable guarantees S Na, Y Luo, Z Yang, Z Wang, M Kolar International Conference on Machine Learning, 7141-7152, 2020 | 7 | 2020 |
| Fully stochastic trust-region sequential quadratic programming for equality-constrained optimization problems Y Fang, S Na, MW Mahoney, M Kolar arXiv preprint arXiv:2211.15943, 2022 | 6 | 2022 |
| Global Convergence of Online Optimization for Nonlinear Model Predictive Control S Na 35th Conference on Neural Information Processing Systems, 2021 | 6 | 2021 |
| Sparse learning with semi-proximal-based strictly contractive Peaceman-Rachford splitting method S Na, CJ Hsieh arXiv preprint arXiv:1612.09357, 2016 | 4 | 2016 |
| Globally Convergent Distributed Sequential Quadratic Programming with Overlapping Decomposition and Exact Augmented Lagrangian Merit Function R Ni, S Na, S Shin, M Anitescu arXiv preprint arXiv:2402.17170, 2024 | | 2024 |
Mladen KolarThe University of Chicago, Booth School of BusinessAdresă de e-mail confirmată pe chicagobooth.edu
