Christian Raymond
PhD Student
School of Engineering and Computer Science
Thesis Info
Research Interests: Meta-Learning, Few-Shot Learning, Hyper-Parameter Optimization
Thesis Title: Meta-Learning Loss Functions for Deep Neural Networks
Supervisor: Dr. Qi Chen, Prof. Bing Xue, Prof. Mengjie Zhang
Thesis Title: Meta-Learning Loss Functions for Deep Neural Networks
Supervisor: Dr. Qi Chen, Prof. Bing Xue, Prof. Mengjie Zhang
Publications
- Raymond, C. (2024). Meta-Learning Loss Functions for Deep Neural Networks. arXiv:2406.09713 (Preprint).
- Raymond, C., Chen, Q., Xue, B., and Zhang, M. (2024). Meta-Learning Neural Procedural Biases. arXiv:2406.07983 (Preprint).
- Raymond, C., Chen, Q., Xue, B., and Zhang, M. (2024). Online Loss Function Learning. arXiv:2301.13247 (Preprint).
- Raymond, C., Chen, Q., Xue, B., and Zhang, M. (2023). Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
- Raymond, C., Chen, Q., Xue, B., and Zhang, M. (2023). Fast and Efficient Local-Search for Genetic Programming Based Loss Function Learning. ACM Genetic and Evolutionary Computation Conference (GECCO). Nominated for Best Paper.
- Raymond, C., Chen, Q., Xue, B., and Zhang, M. (2022). Multi-objective Genetic Programming with the Adaptive Weighted Splines Representation for Symbolic Regression. \textit{European Conference on Genetic Programming (EuroGP).
- Raymond, C., Chen, Q., Xue, B., and Zhang, M. (2020). A New Representation for Genetic Programming Based Symbolic Regression. ACM Genetic and Evolutionary Computation Conference (GECCO).
- Raymond, C., Chen, Q., Xue, B., and Zhang, M. (2019). Genetic Programming with Rademacher Complexity for Symbolic Regression. IEEE Congress on Evolutionary Computation (CEC).