3 citations · 3 across the 9 of their papers we have counts for
9 papers
Beyond Manual Planning: Seating Allocation for Large Organizations
Anton Ipsen, Michael Cashmore, Kirsty Fielding +4
We introduce the Hierarchical Seating Allocation Problem (HSAP) which addresses the optimal assignment of hierarchically structured organizational teams to physical seating arrange…
Capacity Planning and Scheduling for Jobs with Uncertainty in Resource Usage and Duration
Sunandita Patra, Mehtab Pathan, Mahmoud Mahfouz +4
Organizations around the world schedule jobs (programs) regularly to perform various tasks dictated by their end users. With the major movement towards using a cloud computing infr…
Sequential Harmful Shift Detection Without Labels
Salim I. Amoukou, Tom Bewley, Saumitra Mishra +3
We introduce a novel approach for detecting distribution shifts that negatively impact the performance of machine learning models in continuous production environments, which requi…
Temporal Fairness in Decision Making Problems
Manuel R. Torres, Parisa Zehtabi, Michael Cashmore +2
In this work we consider a new interpretation of fairness in decision making problems. Building upon existing fairness formulations, we focus on how to reason over fairness from a…
Are Logistic Models Really Interpretable?
Danial Dervovic, Freddy Lécué, Nicolás Marchesotti +1
The demand for open and trustworthy AI models points towards widespread publishing of model weights. Consumers of these model weights must be able to act accordingly with the infor…
Progressive Inference: Explaining Decoder-Only Sequence Classification Models Using Intermediate Predictions
Sanjay Kariyappa, Freddy Lécué, Saumitra Mishra +3
This paper proposes Progressive Inference - a framework to compute input attributions to explain the predictions of decoder-only sequence classification models. Our work is based o…
Counterfactual Metarules for Local and Global Recourse
Tom Bewley, Salim I. Amoukou, Saumitra Mishra +2
We introduce T-CREx, a novel model-agnostic method for local and global counterfactual explanation (CE), which summarises recourse options for both individuals and groups in the fo…
Privacy-Preserving Algorithmic Recourse
Sikha Pentyala, Shubham Sharma, Sanjay Kariyappa +2
When individuals are subject to adverse outcomes from machine learning models, providing a recourse path to help achieve a positive outcome is desirable. Recent work has shown that…
Comparing Apples to Oranges: Learning Similarity Functions for Data Produced by Different Distributions
Leonidas Tsepenekas, Ivan Brugere, Freddy Lecue +1
Similarity functions measure how comparable pairs of elements are, and play a key role in a wide variety of applications, e.g., notions of Individual Fairness abiding by the semina…