AI RESEARCH
FlowSDR: Sufficient Dimension Reduction via Conditional Normalizing Flows
arXiv stat.ML
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ArXi:2606.01346v1 Announce Type: cross Sufficient dimension reduction (SDR) seeks a low-dimensional linear projection of predictors that preserves the conditional distribution of the response. Existing methods target this conditional distribution indirectly, via inverse moments, local forward regression, or neural ensemble regression. We propose FlowSDR, a likelihood-based framework that jointly learns the projection and the conditional density by maximizing a conditional log-likelihood, with the density parameterized by monotone rational-quadratic spline flows.