Author

Nathan Kallus

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Preprint Aug 2026

Bellman Calibration for Marginalized Importance Weighting in Offline Reinforcement Learning

The introduction of isotonic Bellman calibration, a one-dimensional, model-agnostic post-processing method that reduces residual occupancy-balance violations while preserving the ranking information in any initial occupancy-ratio estimate, and establishes finite-sample calibration guarantees and a KL oracle inequality...

L. van der Laan, Nathan Kallus · 0 citations

#machine learning Preprint Dec 2025

Soft Fitted Q-Iteration without Bellman Completeness: Occupancy Reweighting and Temperature Annealing

Under \(Q\)-function realizability and local regularity, it is established that soft control locally inherits the contraction of policy evaluation in a discounted-occupancy norm, and local contraction and finite-sample convergence with estimated ratios are established.

L. van der Laan, Nathan Kallus · 3 citations

#machine learning Preprint Dec 2025

Fitted Q-Evaluation without Bellman Completeness via Occupancy Weighting

Combining occupancy-weighted FQE with fitted occupancy-ratio evaluation gives an end-to-end guarantee governed by the complexities and direct approximation errors of the value-function and occupancy-ratio classes, removing the need for Bellman completeness.

L. van der Laan, Nathan Kallus · 0 citations