Manifold Sampling via Entropy Maximization
Read the original on arXiv Machine Learning →The paper introduces MASEM, a method for uniform sampling on manifolds that may have multiple disconnected components defined by smooth constraints. It employs a resampling scheme that maximizes the entropy of the empirical distribution using k‑nearest neighbor density estimation, and shows that this approach reduces KL‑divergence exponentially with the number of resampling steps. Experiments on synthetic and robotics benchmarks demonstrate that MASEM achieves faster mixing and improves Sinkhorn distance by an order of magnitude compared to existing alternatives while maintaining competitive runtime.
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