67 citations · 262 across the 30 of their papers we have counts for
9 papers · 1 filter
Learn to explain yourself, when you can: Equipping Concept Bottleneck Models with the ability to abstain on their concept predictions
Joshua Lockhart, Daniele Magazzeni, Manuela Veloso
The Concept Bottleneck Models (CBMs) of Koh et al. [2020] provide a means to ensure that a neural network based classifier bases its predictions solely on human understandable conc…
Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning
Danial Dervovic, Nicolas Marchesotti, Freddy Lecue +1
We introduce a family of interpretable machine learning models, with two broad additions: Linearised Additive Models (LAMs) which replace the ubiquitous logistic link function in G…
Towards learning to explain with concept bottleneck models: mitigating information leakage
Joshua Lockhart, Nicolas Marchesotti, Daniele Magazzeni +1
Concept bottleneck models perform classification by first predicting which of a list of human provided concepts are true about a datapoint. Then a downstream model uses these predi…
Feature Importance for Time Series Data: Improving KernelSHAP
Mattia Villani, Joshua Lockhart, Daniele Magazzeni
Feature importance techniques have enjoyed widespread attention in the explainable AI literature as a means of determining how trained machine learning models make their prediction…
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…
Global Counterfactual Explanations: Investigations, Implementations and Improvements
Dan Ley, Saumitra Mishra, Daniele Magazzeni
Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods emerging in fairness, recourse and model understanding. Howeve…
Asynchronous Collaborative Learning Across Data Silos
Tiffany Tuor, Joshua Lockhart, Daniele Magazzeni
Machine learning algorithms can perform well when trained on large datasets. While large organisations often have considerable data assets, it can be difficult for these assets to…
Explaining Preference-driven Schedules: the EXPRES Framework
Alberto Pozanco, Francesca Mosca, Parisa Zehtabi +2
Scheduling is the task of assigning a set of scarce resources distributed over time to a set of agents, who typically have preferences about the assignments they would like to get.…
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…