Derivative-Controlled Compact Surrogates for Predictable Sensitivity

TMLR Paper7308 Authors

Abstract: Compact neural models are frequently deployed as surrogates inside larger pipelines, where failures are driven less by raw accuracy than by instability and excessive sensitivity. This paper develops a derivative-controlled training approach for low-capacity models, treating derivatives as a primary interface for shaping behavior. We introduce a compact parameterization paired with a derivative-aware objective that discourages brittle sensitivity across depth. We evaluate the approach with property-driven tests—training stability, sensitivity diagnostics, and downstream settings where shape-consistent behavior matters—showing that derivative control can improve robustness while preserving useful predictive performance.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Pin-Yu_Chen1

Submission Number: 7308