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Found 6 papers, 3 papers with code
Coverage-Validity-Aware Algorithmic Recourse
Algorithmic recourse emerges as a prominent technique to promote the explainability, transparency and hence ethics of machine learning models.
Feasible Recourse Plan via Diverse Interpolation
The experimental results show that our method produces a set of recourses that are close to the data manifold while delivering a better cost-diversity trade-off than existing approaches.
Distributionally Robust Recourse Action
To redress this shortcoming, we propose the Distributionally Robust Recourse Action (DiRRAc) framework, which generates a recourse action that has a high probability of being valid under a mixture of model shifts.
A Deep Reinforcement Learning-based Adaptive Charging Policy for Wireless Rechargeable Sensor Networks
We then use a deep neural network to parametrize this charging policy, which will be trained by reinforcement learning techniques.
Robust Bayesian Recourse
Algorithmic recourse aims to recommend an informative feedback to overturn an unfavorable machine learning decision.
Counterfactual Plans under Distributional Ambiguity
Counterfactual explanations are attracting significant attention due to the flourishing applications of machine learning models in consequential domains.
