27 citations · 95 across the 61 of their papers we have counts for
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Pearl Causal Hierarchy on Image Data: Intricacies & Challenges
Matej Zečević, Moritz Willig, Devendra Singh Dhami +1
Many researchers have voiced their support towards Pearl's counterfactual theory of causation as a stepping stone for AI/ML research's ultimate goal of intelligent systems. As in a…
Neural Meta-Symbolic Reasoning and Learning
Zihan Ye, Hikaru Shindo, Devendra Singh Dhami +1
Deep neural learning uses an increasing amount of computation and data to solve very specific problems. By stark contrast, human minds solve a wide range of problems using a fixed…
LogicRank: Logic Induced Reranking for Generative Text-to-Image Systems
Björn Deiseroth, Patrick Schramowski, Hikaru Shindo +2
Text-to-image models have recently achieved remarkable success with seemingly accurate samples in photo-realistic quality. However as state-of-the-art language models still struggl…
Attributions Beyond Neural Networks: The Linear Program Case
Florian Peter Busch, Matej Zečević, Kristian Kersting +1
Linear Programs (LPs) have been one of the building blocks in machine learning and have championed recent strides in differentiable optimizers for learning systems. While there exi…
Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation
Jonas Seng, Matej Zečević, Devendra Singh Dhami +1
Simulations are ubiquitous in machine learning. Especially in graph learning, simulations of Directed Acyclic Graphs (DAG) are being deployed for evaluating new algorithms. In the…
Machines Explaining Linear Programs
David Steinmann, Matej Zečević, Devendra Singh Dhami +1
There has been a recent push in making machine learning models more interpretable so that their performance can be trusted. Although successful, these methods have mostly focused o…