2025
Deep Clustering via Probabilistic Ratio-Cut Optimization
AISTATS 2025poster
We propose a novel approach for optimizing the graph ratio-cut by modeling the binary assignments as random variables. We provide an upper bound on the expected ratio-cut, as well as an unbiased estimate of its gradient, to learn the parameters of the assignment variables in an online setting. The c…