Author

Claudia V. Goldman

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Preprint Jul 2026

SPOTting the Future: Lookahead Explanations for Deep Reinforcement Learning

This work introduces SPOT (Sampling Policy Observation Tree), a novel model-agnostic, sampling-based framework for interpreting DRL policies, and demonstrates how its tree-based representation can be used to inspect policy preferences, compare alternative future trajectories, and reveal downstream behaviors that are not visible through single-timestep feature-attribution methods.

Tamar Gozlan, Claudia V. Goldman · 0 citations

Preprint Jul 2026

Context-Aware Concept Distillation for Trustworthy Flood Prediction

Context-Aware Concept Distillation (CACD) is proposed, a framework developed in collaboration with domain experts to distill opaque LSTMs into interpretable, hydrology-aware surrogate models and a Residual Hypernetwork that dynamically modulates these concepts based on static basin characteristics.

Eli Levinkopf, E. Morin, Claudia V. Goldman · 0 citations