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This paper introduces L2D-SLDS, a switching state-space model designed to optimize forecasting costs in non-stationary time series by dynamically deciding between internal predictions and consulting external experts. The approach leverages historical archives to learn how expert behaviors evolve over time, achieving sublinear regret without requiring active exploration phases. Experimental results on real-world datasets demonstrate that this method outperforms eight other bandit routers in minimizing total consultation costs while adapting to varying fee structures.
Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models
arXiv:2601.22538v4 Announce Type: replace Abstract: Learning-to-defer (L2D) lets a predictor decide, at each round, whether to issue its own forecast or pay for an expert's. In non-stationary time series this decision must keep adapting, although deployment reveals only the consulted expert's forecast while a historical archive records every expert with the target. L2D-SLDS learns from this archive a switching state-space model of the target and all expert forecasts, whose shared and expert-specific states describe how experts move together and apart. Its predictive law supplies the internal forecast and the expected cost of every consultation, which a greedy router minimizes, and one consultation also updates the beliefs about unconsulted and unavailable experts. We prove sublinear regret against a changing conditional-risk oracle without exploration, when the candidate models are accurate and either the archive separates them or live feedback reveals cost differences. On three real datasets, L2D-SLDS has the lowest cost among eight bandit routers and adapts its consultation rate to the fee.
Abstract:Learning-to-defer (L2D) lets a predictor decide, at each round, whether to issue its own forecast or pay for an expert's. In non-stationary time series this decision must keep adapting, although deployment reveals only the consulted expert's forecast while a historical archive records every expert with the target. L2D-SLDS learns from this archive a switching state-space model of the target and all expert forecasts, whose shared and expert-specific states describe how experts move together and apart. Its predictive law supplies the internal forecast and the expected cost of every consultation, which a greedy router minimizes, and one consultation also updates the beliefs about unconsulted and unavailable experts. We prove sublinear regret against a changing conditional-risk oracle without exploration, when the candidate models are accurate and either the archive separates them or live feedback reveals cost differences. On three real datasets, L2D-SLDS has the lowest cost among eight bandit routers and adapts its consultation rate to the fee.
Machine Learning (cs.LG); Applications (stat.AP)
arXiv:2601.22538 [cs.LG]
(or arXiv:2601.22538v4 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2601.22538 Focus to learn more arXiv-issued DOI via DataCite
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