Consistent Learning-to-Defer with Expert-Conditional Advice
The research introduces a method to improve decision-making in expert systems by jointly learning which expert to consult and what specific advice that expert should receive. It addresses a flaw in previous methods where advice was assumed fixed after an expert was chosen. The new approach uses an augmented surrogate loss function that proves it converges to the theoretically optimal policy, leading to better performance across different cost regimes.