29 citations · 92 across the 24 of their papers we have counts for
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cs.LG2023★ 3 cited
Meta-Learning Adaptive Loss Functions
Christian Raymond, Qi Chen, Bing Xue +1
Loss function learning is a new meta-learning paradigm that aims to automate the essential task of designing a loss function for a machine learning model. Existing techniques for l…
cs.LG2022★ 1 cited
Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning
Christian Raymond, Qi Chen, Bing Xue +1
In this paper, we develop upon the emerging topic of loss function learning, which aims to learn loss functions that significantly improve the performance of the models trained und…
cs.LG2022★ 10 cited
Multi-scale Wasserstein Shortest-path Graph Kernels for Graph Classification
Wei Ye, Hao Tian, Qijun Chen
Graph kernels are conventional methods for computing graph similarities. However, the existing R-convolution graph kernels cannot resolve both of the two challenges: 1) Comparing g…