| Subset selection by Pareto optimization C Qian, Y Yu, ZH Zhou Advances in neural information processing systems 28, 2015 | 204 | 2015 |
| Evolutionary learning: Advances in theories and algorithms ZH Zhou, Y Yu, C Qian Springer Singapore, 2019 | 168 | 2019 |
| Pareto ensemble pruning C Qian, Y Yu, ZH Zhou Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 128 | 2015 |
| An analysis on recombination in multi-objective evolutionary optimization C Qian, Y Yu, ZH Zhou Proceedings of the 13th annual conference on Genetic and evolutionary …, 2011 | 123 | 2011 |
| Subset selection under noise C Qian, JC Shi, Y Yu, K Tang, ZH Zhou Advances in neural information processing systems 30, 2017 | 87 | 2017 |
| Optimization based Layer-wise Magnitude-based Pruning for DNN Compression. G Li, C Qian, C Jiang, X Lu, K Tang IJCAI 330, 2383-2389, 2018 | 84 | 2018 |
| On the effectiveness of sampling for evolutionary optimization in noisy environments C Qian, Y Yu, K Tang, Y Jin, X Yao, ZH Zhou Evolutionary computation 26 (2), 237-267, 2018 | 77* | 2018 |
| On Subset Selection with General Cost Constraints. C Qian, JC Shi, Y Yu, K Tang IJCAI 17, 2613-2619, 2017 | 75 | 2017 |
| Constrained Monotone-Submodular Function Maximization Using Multiobjective Evolutionary Algorithms With Theoretical Guarantee C Qian, JC Shi, K Tang, ZH Zhou IEEE Transactions on Evolutionary Computation 22 (4), 595-608, 2017 | 68 | 2017 |
| Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error. C Jiang, G Li, C Qian, K Tang IJCAI 2018, 2-2, 2018 | 57 | 2018 |
| Analyzing evolutionary optimization in noisy environments C Qian, Y Yu, ZH Zhou Evolutionary computation 26 (1), 1-41, 2018 | 57 | 2018 |
| Maximizing submodular or monotone approximately submodular functions by multi-objective evolutionary algorithms C Qian, Y Yu, K Tang, X Yao, ZH Zhou Artificial Intelligence 275, 279-294, 2019 | 56 | 2019 |
| Parallel Pareto Optimization for Subset Selection. C Qian, JC Shi, Y Yu, K Tang, ZH Zhou IJCAI, 1939-1945, 2016 | 56 | 2016 |
| Better running time of the non-dominated sorting genetic algorithm II (NSGA-II) by using stochastic tournament selection C Bian, C Qian International Conference on Parallel Problem Solving from Nature, 428-441, 2022 | 54* | 2022 |
| Selection hyper-heuristics can provably be helpful in evolutionary multi-objective optimization C Qian, K Tang, ZH Zhou International conference on parallel problem solving from nature, 835-846, 2016 | 54 | 2016 |
| Distributed Pareto optimization for large-scale noisy subset selection C Qian IEEE Transactions on Evolutionary Computation 24 (4), 694-707, 2019 | 46 | 2019 |
| On constrained boolean pareto optimization C Qian, Y Yu, ZH Zhou Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015 | 44 | 2015 |
| Switch analysis for running time analysis of evolutionary algorithms Y Yu, C Qian, ZH Zhou IEEE Transactions on Evolutionary Computation 19 (6), 777-792, 2014 | 43 | 2014 |
| An efficient evolutionary algorithm for subset selection with general cost constraints C Bian, C Feng, C Qian, Y Yu Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3267-3274, 2020 | 42 | 2020 |
| Efficient minimum cost seed selection with theoretical guarantees for competitive influence maximization W Hong, C Qian, K Tang IEEE transactions on cybernetics 51 (12), 6091-6104, 2020 | 38 | 2020 |