| Subset selection by Pareto optimization C Qian, Y Yu, ZH Zhou Advances in neural information processing systems 28, 2015 | 125 | 2015 |
| Pareto ensemble pruning C Qian, Y Yu, ZH Zhou Twenty-ninth AAAI conference on artificial intelligence, 2015 | 100 | 2015 |
| An analysis on recombination in multi-objective evolutionary optimization C Qian, Y Yu, ZH Zhou Artificial Intelligence 204, 99-119, 2013 | 81 | 2013 |
| Evolutionary learning: Advances in theories and algorithms ZH Zhou, Y Yu, C Qian Springer Singapore, 2019 | 53 | 2019 |
| 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 | 52* | 2018 |
| Subset selection under noise C Qian, JC Shi, Y Yu, K Tang, ZH Zhou Advances in neural information processing systems 30, 2017 | 47 | 2017 |
| On Subset Selection with General Cost Constraints. C Qian, JC Shi, Y Yu, K Tang IJCAI 17, 2613-2619, 2017 | 44 | 2017 |
| Analyzing evolutionary optimization in noisy environments C Qian, Y Yu, ZH Zhou Evolutionary computation 26 (1), 1-41, 2018 | 42 | 2018 |
| Parallel Pareto Optimization for Subset Selection. C Qian, JC Shi, Y Yu, K Tang, ZH Zhou IJCAI, 1939-1945, 2016 | 38 | 2016 |
| 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 | 35 | 2016 |
| 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 | 34 | 2017 |
| 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 | 33 | 2014 |
| Optimization based layer-wise magnitude-based pruning for DNN compression. G Li, C Qian, C Jiang, X Lu, K Tang IJCAI, 2383-2389, 2018 | 32 | 2018 |
| On constrained boolean pareto optimization C Qian, Y Yu, ZH Zhou Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015 | 29 | 2015 |
| 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 | 27* | 2019 |
| Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error. C Jiang, G Li, C Qian, K Tang IJCAI 2018, 2-2, 2018 | 26 | 2018 |
| Running Time Analysis of the ( 1 + 1 )-EA for OneMax and LeadingOnes Under Bit-Wise Noise C Qian, C Bian, W Jiang, K Tang Algorithmica 81 (2), 749-795, 2019 | 25 | 2019 |
| Unsupervised feature selection by Pareto optimization C Feng, C Qian, K Tang Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3534-3541, 2019 | 20 | 2019 |
| Approximation Guarantees of Stochastic Greedy Algorithms for Subset Selection. C Qian, Y Yu, K Tang IJCAI, 1478-1484, 2018 | 20 | 2018 |
| Variable solution structure can be helpful in evolutionary optimization C Qian, Y Yu, ZH Zhou Science China Information Sciences 58 (11), 1-17, 2015 | 16 | 2015 |
