We design a predictive layer for structured-output prediction (SOP) that can be plugged into
any neural network guaranteeing its predictions are consistent with a set of predefined …

SHAP explanations are a popular feature-attribution mechanism for explainable AI. They
use game-theoretic notions to measure the influence of individual features on the prediction …

The ability of deep learning to process vast data and uncover concealed malicious patterns
has spurred the adoption of deep learning methods within the cybersecurity domain …

Probabilistic circuits (PCs) are a promising avenue for probabilistic modeling, as they permit
a wide range of exact and efficient inference routines. Recent “deep-learning-style” …

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 …

Circuit representations are becoming the lingua franca to express and reason about
tractable generative and discriminative models. In this paper, we show how complex …

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 …

Probabilistic circuits (PCs) are a tractable representation of probability distributions allowing
for exact and efficient computation of likelihoods and marginals. There has been significant …

Machine learning (ML) applications have been thriving recently, largely attributed to the
increasing availability of data. However, inconsistency and incomplete information are …

We present a comprehensive survey of the advancements and techniques in the field of
tractable probabilistic generative modeling, primarily focusing on Probabilistic Circuits …