Investigating bi-level optimization for learning and vision from a unified perspective: A survey and beyond R Liu, J Gao, J Zhang, D Meng, Z Lin IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 | 205 | 2021 |
A generic first-order algorithmic framework for bi-level programming beyond lower-level singleton R Liu, P Mu, X Yuan, S Zeng, J Zhang International Conference on Machine Learning 2020, 6305-6315, 2020 | 121 | 2020 |
Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond R Liu, Y Liu, S Zeng, J Zhang Conference on Neural Information Processing Systems 2021, 2021 | 68 | 2021 |
A Value-Function-based Interior-point Method for Non-convex Bi-level Optimization R Liu, X Liu, X Yuan, S Zeng, J Zhang International Conference on Machine Learning 2021, 2021 | 64 | 2021 |
Sensitivity analysis of the value function for parametric mathematical programs with equilibrium constraints L Guo, GH Lin, JJ Ye, J Zhang SIAM Journal on Optimization 24 (3), 1206-1237, 2014 | 45 | 2014 |
Enhanced Karush–Kuhn–Tucker condition and weaker constraint qualifications JJ Ye, J Zhang Mathematical Programming 139 (1), 353-381, 2013 | 45 | 2013 |
Averaged Method of Multipliers for Bi-Level Optimization without Lower-Level Strong Convexity R Liu, Y Liu, W Yao, S Zeng, J Zhang International Conference on Machine Learning 2023, 2023 | 43 | 2023 |
Enhanced Karush–Kuhn–Tucker conditions for mathematical programs with equilibrium constraints JJ Ye, J Zhang Journal of Optimization Theory and Applications 163, 777-794, 2014 | 43 | 2014 |
A generic descent aggregation framework for gradient-based bi-level optimization R Liu, P Mu, X Yuan, S Zeng, J Zhang IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 | 42 | 2022 |
Mathematical programs with geometric constraints in Banach spaces: enhanced optimality, exact penalty, and sensitivity L Guo, JJ Ye, J Zhang SIAM Journal on Optimization 23 (4), 2295-2319, 2013 | 39 | 2013 |
Distributionally robust equilibrium for continuous games: Nash and Stackelberg models Y Liu, H Xu, SJS Yang, J Zhang European Journal of Operational Research 265 (2), 631-643, 2018 | 36 | 2018 |
Discerning the Linear Convergence of ADMM for Structured Convex Optimization through the Lens of Variational Analysis. X Yuan, S Zeng, J Zhang Journal of Machine Learning Research 21, 83:1-83:75, 2020 | 35 | 2020 |
Difference of convex algorithms for bilevel programs with applications in hyperparameter selection JJ Ye, X Yuan, S Zeng, J Zhang Mathematical Programming, 2022 | 32 | 2022 |
Variational analysis perspective on linear convergence of some first order methods for nonsmooth convex optimization problems JY Jane, X Yuan, S Zeng, J Zhang Set-Valued and Variational Analysis, 2021 | 32 | 2021 |
A globally convergent proximal Newton-type method in nonsmooth convex optimization BS Mordukhovich, X Yuan, S Zeng, J Zhang Mathematical Programming, 2022 | 30 | 2022 |
Directional quasi-/pseudo-normality as sufficient conditions for metric subregularity K Bai, JJ Ye, J Zhang SIAM Journal on Optimization 29 (4), 2625-2649, 2019 | 30 | 2019 |
Modeling the bids of wind power producers in the day-ahead market with stochastic market clearing M Lei, J Zhang, X Dong, JY Jane Sustainable Energy Technologies and Assessments 16, 151-161, 2016 | 28 | 2016 |
Partial error bound conditions and the linear convergence rate of the alternating direction method of multipliers Y Liu, X Yuan, S Zeng, J Zhang SIAM Journal on Numerical Analysis 56 (4), 2095-2123, 2018 | 25 | 2018 |
Value-function-based sequential minimization for bi-level optimization R Liu, X Liu, S Zeng, J Zhang, Y Zhang IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 | 23 | 2023 |
Perturbation techniques for convergence analysis of proximal gradient method and other first-order algorithms via variational analysis X Wang, JJ Ye, X Yuan, S Zeng, J Zhang Set-Valued and Variational Analysis, 1-41, 2021 | 23 | 2021 |