Despite the success of autoregressive large language models in text generation, it remains
a major challenge to generate text that satisfies complex constraints: sampling from the …

We study the problem of combining neural networks with symbolic reasoning. Recently
introduced frameworks for Probabilistic Neurosymbolic Learning (PNL), such as …

Abstract Neuro-Symbolic (NeSy) predictive models hold the promise of improved
compliance with given constraints, systematic generalization, and interpretability, as they …

Numerous neuro-symbolic approaches have recently been proposed typically with the goal
of adding symbolic knowledge to the output layer of a neural network. Ideally, such losses …

Safe Reinforcement learning (Safe RL) aims at learning optimal policies while staying safe.
A popular solution to Safe RL is shielding, which uses a logical safety specification to …

High-quality labels are often very scarce, whereas unlabeled data with inferred weak labels
occurs more naturally. In many cases, these weak labels dictate the frequency of each …

We introduce Neuro-Symbolic Continual Learning, where a model has to solve a sequence
of neuro-symbolic tasks, that is, it has to map sub-symbolic inputs to high-level concepts and …

For their outstanding ability of finding hidden patterns in data, deep learning models have
been extensively applied in many different domains. However, recent works have shown …

Bayesian neural networks (BNNs) provide a formalism to quantify and calibrate uncertainty
in deep learning. Current inference approaches for BNNs often resort to few-sample …

Neuro-symbolic AI bridges the gap between purely symbolic and neural approaches to
learning. This often requires maximizing the likelihood of a symbolic constraint wrt the neural …