Provides an AMP-based asymptotic analysis of SLOPE, characterizing iterate dynamics via state evolution and proving asymptotic convergence to the SLOPE solution.
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CPCANet unrolls the Flury-Gautschi algorithm for Common Principal Component Analysis into differentiable layers to learn a shared invariant subspace across domains, reporting SOTA zero-shot transfer on four DG benchmarks.
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Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing
Provides an AMP-based asymptotic analysis of SLOPE, characterizing iterate dynamics via state evolution and proving asymptotic convergence to the SLOPE solution.
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CPCANet: Deep Unfolding Common Principal Component Analysis for Domain Generalization
CPCANet unrolls the Flury-Gautschi algorithm for Common Principal Component Analysis into differentiable layers to learn a shared invariant subspace across domains, reporting SOTA zero-shot transfer on four DG benchmarks.