Supervised Distributional Reduction via Optimal Transport and Dependence Maximization

In three linesSDR (Supervised Distributional Reduction) combines optimal transport and dependence maximization to learn target-aware representations. The algorithm extends the Fused Gromov-Wasserstein objective with an explicit dependence term, producing compact embeddings that capture both geometric structure and predictive signal. Application to Gaussian Process modelling with adaptive kernels.

Summary generated by Claude — human-verified