1k citations · 1.4k across the 27 of their papers we have counts for
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Deep Neural Networks and Tabular Data: A Survey
Vadim Borisov, Tobias Leemann, Kathrin Seßler +3
Heterogeneous tabular data are the most commonly used form of data and are essential for numerous critical and computationally demanding applications. On homogeneous data sets, dee…
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Martin Pawelczyk, Sascha Bielawski, Johannes van den Heuvel +2
Counterfactual explanations provide means for prescriptive model explanations by suggesting actionable feature changes (e.g., increase income) that allow individuals to achieve fav…
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis
Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi +2
As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explan…
Gaussian Experts Selection using Graphical Models
Hamed Jalali, Martin Pawelczyk, Gjergji Kasneci
Local approximations are popular methods to scale Gaussian processes (GPs) to big data. Local approximations reduce time complexity by dividing the original dataset into subsets an…