Kernelized Heterogeneous Risk Minimization
This addresses the challenge of reliable generalization under distributional shifts for machine learning applications, but it is incremental as it builds on existing invariant learning methods.
The paper tackles the problem of out-of-distribution generalization in machine learning when datasets lack explicit source labels, proposing the KerHRM algorithm that achieves latent heterogeneity exploration and invariant learning in kernel space, with empirical validation showing effectiveness.
The ability to generalize under distributional shifts is essential to reliable machine learning, while models optimized with empirical risk minimization usually fail on non-$i.i.d$ testing data. Recently, invariant learning methods for out-of-distribution (OOD) generalization propose to find causally invariant relationships with multi-environments. However, modern datasets are frequently multi-sourced without explicit source labels, rendering many invariant learning methods inapplicable. In this paper, we propose Kernelized Heterogeneous Risk Minimization (KerHRM) algorithm, which achieves both the latent heterogeneity exploration and invariant learning in kernel space, and then gives feedback to the original neural network by appointing invariant gradient direction. We theoretically justify our algorithm and empirically validate the effectiveness of our algorithm with extensive experiments.