| Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program A Verma, JE Huffman, A Rodriguez, M Conery, M Liu, YL Ho, Y Kim, ... Science 385 (6706), eadj1182, 2024 | 306 | 2024 |
| APPFL: open-source software framework for privacy-preserving federated learning M Ryu, Y Kim, K Kim, RK Madduri 2022 IEEE international parallel and distributed processing symposium …, 2022 | 49 | 2022 |
| A reinforcement learning approach to parameter selection for distributed optimal power flow S Zeng, A Kody, Y Kim, K Kim, DK Molzahn Electric Power Systems Research 212, 108546, 2022 | 44 | 2022 |
| MPEC methods for bilevel optimization problems Y Kim, S Leyffer, T Munson Bilevel optimization: advances and next challenges, 2020 | 44 | 2020 |
| RV-SVM: An efficient method for learning ranking SVM H Yu, Y Kim, S Hwang Pacific-Asia Conference on Knowledge Discovery and Data Mining, 426-438, 2009 | 44 | 2009 |
| Solving equilibrium problems using extended mathematical programming Y Kim, MC Ferris Mathematical programming computation 11 (3), 457-501, 2019 | 34 | 2019 |
| An efficient method for learning nonlinear ranking SVM functions H Yu, J Kim, Y Kim, S Hwang, YH Lee Information Sciences 209, 37-48, 2012 | 34 | 2012 |
| Accelerated computation and tracking of AC optimal power flow solutions using GPUs Y Kim, K Kim Workshop Proceedings of the 51st International Conference on Parallel …, 2022 | 23 | 2022 |
| Leveraging GPU batching for scalable nonlinear programming through massive Lagrangian decomposition Y Kim, F Pacaud, K Kim, M Anitescu arXiv preprint arXiv:2106.14995, 2021 | 19 | 2021 |
| Adaptively refined dynamic program for linear spline regression N Goldberg, Y Kim, S Leyffer, TD Veselka Computational Optimization and Applications 58 (3), 523-541, 2014 | 19 | 2014 |
| Exact indexing for support vector machines H Yu, I Ko, Y Kim, S Hwang, WS Han Proceedings of the 2011 ACM SIGMOD International Conference on Management of …, 2011 | 14 | 2011 |
| MINLP formulations for continuous piecewise linear function fitting N Goldberg, S Rebennack, Y Kim, V Krasko, S Leyffer Computational Optimization and Applications, 2021 | 13 | 2021 |
| GPU-accelerated DNS of compressible turbulent flows Y Kim, D Ghosh, EM Constantinescu, R Balakrishnan Computers and Fluids 251, 2023 | 11 | 2023 |
| A real-time optimization with warm-start of multiperiod AC optimal power flows Y Kim, M Anitescu Electric Power Systems Research 189, 2020 | 9 | 2020 |
| A structure-preserving pivotal method for affine variational inequalities Y Kim, O Huber, MC Ferris Mathematical Programming 168 (1), 93-121, 2018 | 9 | 2018 |
| A reinforcement learning approach to parameter selection for distributed optimization in power systems S Zeng, A Kody, Y Kim, K Kim, DK Molzahn arXiv preprint arXiv:2110.11991, 2021 | 6 | 2021 |
| Julia as a portable high-level language for numerical solvers of power flow equations on GPU architectures M Schanen, DA Maldonado, F Pacaud, A Montoison, M Anitescu, K Kim, ... Les Cahiers du GERAD ISSN 711, 2440, 2020 | 4 | 2020 |
| iKernel: Exact indexing for support vector machines Y Kim, I Ko, WS Han, H Yu Information Sciences 257, 32-53, 2014 | 4 | 2014 |
| Escaping a dominance region at minimum cost Y Kim, G You, S Hwang International Conference on Database and Expert Systems Applications, 800-807, 2008 | 4 | 2008 |
| QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints S Zeng, Y Kim, Y Ren, K Kim 6th Annual Learning for Dynamics & Control Conference 242, 1539-1551, 2024 | 3 | 2024 |
Kibaek KimArgonne National LaboratoryVerified email at anl.gov
