Search Results for author:
Found 5 papers, 5 papers with code
Analyzing and Improving the Robustness of Tabular Classifiers using Counterfactual Explanations
Through several experiments and evaluations, we demonstrate the efficacy of our approach in analyzing and improving the robustness of black-box tabular classifiers.
CARE: Coherent Actionable Recourse based on Sound Counterfactual Explanations
We believe an actionable recourse should be created based on sound counterfactual explanations originating from the distribution of the ground-truth data and linked to the domain knowledge.
Explainable Debugger for Black-box Machine Learning Models
In this paper, we propose a systematic debugging framework for the development of ML models that guides the data engineering process using the model's decision boundary.
EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood Generation
Defining a representative locality is an urgent challenge in perturbation-based explanation methods, which influences the fidelity and soundness of explanations.
Meaningful Data Sampling for a Faithful Local Explanation Method
Data sampling has an important role in the majority of local explanation methods.
