Learning-to-Defer (L2D) systems decide whether to have a model answer a query or defer to a human expert. Prior work assumes a fixed batch setting with known experts, but real deployments face streaming data and changing expert availability. The authors introduce the first online L2D algorithm for multiclass classification with bandit feedback (you only see outcomes for the chosen option) and a time-varying pool of experts. Their method achieves regret bounds that scale with the time horizon, number of labels, and number of distinct experts, with tighter bounds under low-noise conditions.
Main takeaways:
- Existing L2D methods assume batch data and fixed expert availability; real systems need to handle streaming data and experts coming and going
- The proposed online algorithm works with bandit feedback (partial observability) and dynamically varying expert pools
- Achieves regret guarantees of O((n+n_e)T^(2/3)) in general, O((n+n_e)√T) under low noise (n=labels, n_e=distinct experts, T=time horizon)
- The analysis combines novel consistency bounds for the online setting with online convex optimization techniques
- Experiments show the approach handles varying expert availability and reliability effectively