FP-IRL recovers MDP reward, transition, and policy from trajectories alone by using variational system identification on a Fokker-Planck potential that corresponds to reward maximization.
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FragileFlow formalizes margin-aware error flow and applies spectral control through a calibrated margin buffer and class-wise risk matrix, supported by a PAC-Bayes bound, to enhance worst-class robustness in foundation model adaptation while preserving clean accuracy.
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FP-IRL: Fokker--Planck Inverse Reinforcement Learning -- A Physics-Constrained Approach to Markov Decision Processes
FP-IRL recovers MDP reward, transition, and policy from trajectories alone by using variational system identification on a Fokker-Planck potential that corresponds to reward maximization.
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FragileFlow: Spectral Control of Correct-but-Fragile Predictions for Foundation Model Robustness
FragileFlow formalizes margin-aware error flow and applies spectral control through a calibrated margin buffer and class-wise risk matrix, supported by a PAC-Bayes bound, to enhance worst-class robustness in foundation model adaptation while preserving clean accuracy.