Some argue scale is all what is needed to achieve AI, covering even caus...
Recent years have seen a growing interest in Scene Graph Generation (SGG...
The goal of combining the robustness of neural networks and the
expressi...
Many researchers have voiced their support towards Pearl's counterfactua...
Automated machine learning (AutoML) is an important step to make machine...
Linear Programs (LPs) have been one of the building blocks in machine
le...
Foundation models are subject to an ongoing heated debate, leaving open ...
To date, Bongard Problems (BP) remain one of the few fortresses of AI hi...
Simulations are ubiquitous in machine learning. Especially in graph lear...
There has been a recent push in making machine learning models more
inte...
Linear Programs (LP) are celebrated widely, particularly so in machine
l...
Most algorithms in classical and contemporary machine learning focus on
...
Roth (1996) proved that any form of marginal inference with probabilisti...
Reasoning is an essential part of human intelligence and thus has been a...
The goal of combining the robustness of neural networks and the expressi...
Human mental processes allow for qualitative reasoning about causality i...
Probabilistic circuits (PCs) have become the de-facto standard for learn...
Causality can be described in terms of a structural causal model (SCM) t...
In recent years there has been a lot of focus on adversarial attacks,
es...
Predicting and discovering drug-drug interactions (DDIs) using machine
l...
While probabilistic models are an important tool for studying causality,...
We consider the problem of learning Graph Convolutional Networks (GCNs) ...
We consider the problem of structure learning for Gaifman models and lea...
Predicting and discovering drug-drug interactions (DDIs) is an important...
Recently, deep models have had considerable success in several tasks,
es...
Recently, deep models have been successfully applied in several applicat...