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Found 7 papers, 1 papers with code
Robust Counterfactual Explanations for Tree-Based Ensembles
In this work, we propose a novel strategy -- that we call RobX -- to generate robust counterfactuals for tree-based ensembles, e. g., XGBoost.
Reductive MDPs: A Perspective Beyond Temporal Horizons
Solving general Markov decision processes (MDPs) is a computationally hard problem.
Optimal Admission Control for Multiclass Queues with Time-Varying Arrival Rates via State Abstraction
To improve the scalability of our approach to a greater number of task classes, we present an approximation based on state abstraction.
A Survey on the Robustness of Feature Importance and Counterfactual Explanations
There exist several methods that aim to address the crucial task of understanding the behaviour of AI/ML models.
Counterfactual Shapley Additive Explanations
Feature attributions are a common paradigm for model explanations due to their simplicity in assigning a single numeric score for each input feature to a model.
Counterfactual Explanations for Arbitrary Regression Models
We present a new method for counterfactual explanations (CFEs) based on Bayesian optimisation that applies to both classification and regression models.
Partial associativity and rough approximate groups
We prove that this is indeed the case: there must be a proportional-sized subset of the multiplication table that approximately agrees with part of the multiplication table of a metric group.
Combinatorics
