Research Interests: Meta-Learning, Few-Shot Learning, Hyper-Parameter Optimization Thesis Title: Learning Loss Functions via Meta-Learning Supervisor:Dr. Qi Chen, Prof. Bing Xue
Publications
Raymond, C., Chen, Q., Xue, B., and Zhang, M. (2023). Online Loss Function Learning. arXiv preprint arXiv:2301.13247.
Raymond, C., Chen, Q., Xue, B., and Zhang, M. (2022). Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning. arXiv preprint arXiv:2209.08907.
Raymond, C., Chen, Q., Xue, B., and Zhang, M. "Fast and Efficient Local-Search for Genetic Programming Based Loss Function Learning". In the Proceedings of the 2023 Genetic and Evolutionary Computation Conference (GECCO 2023). Lisbon, Portugal. pp. 1003–1011.
Raymond, C., Chen, Q., Xue, B., and Zhang, M. Multi-objective Genetic Programming with the Adaptive Weighted Splines Representation for Symbolic Regression. In Genetic Programming: 25th European Conference, (EuroGP 2022), pp. 51-67.
Raymond, C., Chen, Q., Xue, B., and Zhang, M. "Adaptive Weighted Splines: A New Representation to Genetic Programming for Symbolic Regression". In the Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO 2020). Cancun, Mexico. pp. 1003–1011.
Raymond, C., Chen, Q., Xue, B., and Zhang, M. "Genetic Programming with Rademacher Complexity for Symbolic Regression". In the Proceedings of 2019IEEE Congress on Evolutionary Computation (CEC 2019). Wellington, New Zealand. pp. 2657-2664.