65 citations · 89 across the 6 of their papers we have counts for
6 papers
Robust Counterfactual Explanations for Tree-Based Ensembles
Sanghamitra Dutta, Jason Long, Saumitra Mishra +2
Counterfactual explanations inform ways to achieve a desired outcome from a machine learning model. However, such explanations are not robust to certain real-world changes in the u…
Reductive MDPs: A Perspective Beyond Temporal Horizons
Thomas Spooner, Rui Silva, Joshua Lockhart +2
Solving general Markov decision processes (MDPs) is a computationally hard problem. Solving finite-horizon MDPs, on the other hand, is highly tractable with well known polynomial-t…
Optimal Admission Control for Multiclass Queues with Time-Varying Arrival Rates via State Abstraction
Marc Rigter, Danial Dervovic, Parisa Hassanzadeh +3
We consider a novel queuing problem where the decision-maker must choose to accept or reject randomly arriving tasks into a no buffer queue which are processed by identical ser…
A Survey on the Robustness of Feature Importance and Counterfactual Explanations
Saumitra Mishra, Sanghamitra Dutta, Jason Long +1
There exist several methods that aim to address the crucial task of understanding the behaviour of AI/ML models. Arguably, the most popular among them are local explanations that f…
Counterfactual Shapley Additive Explanations
Emanuele Albini, Jason Long, Danial Dervovic +1
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. In the actionable r…
Counterfactual Explanations for Arbitrary Regression Models
Thomas Spooner, Danial Dervovic, Jason Long +3
We present a new method for counterfactual explanations (CFEs) based on Bayesian optimisation that applies to both classification and regression models. Our method is a globally co…