302 citations · 1.3k across the 62 of their papers we have counts for
67 papers
Gradient-based Counterfactual Explanations using Tractable Probabilistic Models
Xiaoting Shao, Kristian Kersting
Counterfactual examples are an appealing class of post-hoc explanations for machine learning models. Given input of class , its counterfactual is a contrastive example $x^…
Finding Structure and Causality in Linear Programs
Matej Zečević, Florian Peter Busch, Devendra Singh Dhami +1
Linear Programs (LP) are celebrated widely, particularly so in machine learning where they have allowed for effectively solving probabilistic inference tasks or imposing structure…
SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming
Arseny Skryagin, Wolfgang Stammer, Daniel Ochs +2
The goal of combining the robustness of neural networks and the expressivity of symbolic methods has rekindled the interest in neuro-symbolic AI. Recent advancements in neuro-symbo…
Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash
Lukas Struppek, Dominik Hintersdorf, Daniel Neider +1
Apple recently revealed its deep perceptual hashing system NeuralHash to detect child sexual abuse material (CSAM) on user devices before files are uploaded to its iCloud service.…
The Causal Loss: Driving Correlation to Imply Causation
Moritz Willig, Matej Zečević, Devendra Singh Dhami +1
Most algorithms in classical and contemporary machine learning focus on correlation-based dependence between features to drive performance. Although success has been observed in ma…
Relating Graph Neural Networks to Structural Causal Models
Matej Zečević, Devendra Singh Dhami, Petar Veličković +1
Causality can be described in terms of a structural causal model (SCM) that carries information on the variables of interest and their mechanistic relations. For most processes of…