SGD Provably Prioritizes a Shortcut Spurious Feature in the XOR Model
Neural networks are known to be susceptible to over-reliance on spurious correlations. However, the precise mechanism by which models exploit shortcut features is not fully understood, and algorithms to mitigate this behavior rely on as yet unjustified assumptions about the learned representations. In this work, we provide the first end-to-end theoretical characterization of spurious feature learning for two-layer ReLU neural networks trained by online minibatch SGD on the logistic loss. We consider data drawn from the high-dimensional Boolean hypercube with a quadratic signal function (namely XOR) and a linear spurious correlation. We show that SGD learns the spurious feature first, and ...
| Authors | Tyler LaBonte, Vidya Muthukumar |
|---|---|
| Primary category | stat.ML |
| Published | 29 June 2026 |
| Last updated at source | 3 July 2026 |
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