Gao, C., Yao, X., Weise, T. and Li, J., 2015. An efficient local search heuristic with row weighting for the unicost set covering problem. European Journal of Operational Research, 246(3), pp.750-761.
Gao, C., Lu, G., Yao, X. and Li, J., 2017. An iterative pseudo-gap enumeration approach for the Multidimensional Multiple-choice Knapsack Problem. European Journal of Operational Research, 260(1), pp.1-11.
Gao, C., Müller, M. and Hayward, R., 2017, January. Focused Depth-first Proof Number Search using Convolutional Neural Networks for the Game of Hex. In IJCAI (pp. 3668-3674).
Gao, C., Hayward, R. and Müller, M., 2017. Move prediction using deep convolutional neural networks in Hex. IEEE Transactions on Games, 10(4), pp.336-343.
Gao, C., Müller, M. and Hayward, R., 2018, January. Three-Head Neural Network Architecture for Monte Carlo Tree Search. In IJCAI (pp. 3762-3768).
Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M. and Jagersand, M., 2019. Basnet: Boundary-aware salient object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7479-7489).
Gao, C., Kartal, B., Hernandez-Leal, P. and Taylor, M.E., 2019, October. On hard exploration for reinforcement learning: A case study in pommerman. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 15, No. 1, pp. 24-30).
Resnick, C., Gao, C., Marton, G., Osogami, T., Pang, L. and Takahashi, T., 2020. Pommerman & neurips 2018. In The NeurIPS-18 Competition (pp. 11-36). Springer, Cham.
Gao et al., Bansor: Improving Tensor Program Auto-Scheduling with Bandit Based Reinforcement Learning
Wang et al., Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability
Chao Gao et al., A Memory-Bounded Best-First Beam Search and Its Application to Scheduling Halide Programs