27 citations · 63 across the 19 of their papers we have counts for
8 papers · 1 filter
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…
On How AI Needs to Change to Advance the Science of Drug Discovery
Kieran Didi, Matej Zečević
Research around AI for Science has seen significant success since the rise of deep learning models over the past decade, even with longstanding challenges such as protein structure…
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…
Can Foundation Models Talk Causality?
Moritz Willig, Matej Zečević, Devendra Singh Dhami +1
Foundation models are subject to an ongoing heated debate, leaving open the question of progress towards AGI and dividing the community into two camps: the ones who see the arguabl…
Towards a Solution to Bongard Problems: A Causal Approach
Salahedine Youssef, Matej Zečević, Devendra Singh Dhami +1
Even though AI has advanced rapidly in recent years displaying success in solving highly complex problems, the class of Bongard Problems (BPs) yet remain largely unsolved by modern…
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…