10 citations · 12 across the 9 of their papers we have counts for
9 papers
Towards Counting Markov Equivalence Classes with Logical Constraints
Davide Bizzaro, Luciano Serafini, Sagar Malhotra
We initiate the study of counting Markov Equivalence Classes (MEC) under logical constraints. MECs are equivalence classes of Directed Acyclic Graphs (DAGs) that encode the same co…
Understanding Domain-Size Generalization in Markov Logic Networks
Florian Chen, Felix Weitkämper, Sagar Malhotra
We study the generalization behavior of Markov Logic Networks (MLNs) across relational structures of different sizes. Multiple works have noticed that MLNs learned on a given domai…
Simple and Effective Transfer Learning for Neuro-Symbolic Integration
Alessandro Daniele, Tommaso Campari, Sagar Malhotra +1
Deep Learning (DL) techniques have achieved remarkable successes in recent years. However, their ability to generalize and execute reasoning tasks remains a challenge. A potential…
Lifted Inference beyond First-Order Logic
Sagar Malhotra, Davide Bizzaro, Luciano Serafini
Weighted First Order Model Counting (WFOMC) is fundamental to probabilistic inference in statistical relational learning models. As WFOMC is known to be intractable in general ($\#…
Weighted First Order Model Counting with Directed Acyclic Graph Axioms
Sagar Malhotra, Luciano Serafini
Statistical Relational Learning (SRL) integrates First-Order Logic (FOL) and probability theory for learning and inference over relational data. Probabilistic inference and learnin…
Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions
Alessandro Daniele, Tommaso Campari, Sagar Malhotra +1
Neuro-Symbolic (NeSy) integration combines symbolic reasoning with Neural Networks (NNs) for tasks requiring perception and reasoning. Most NeSy systems rely on continuous relaxati…
On Projectivity in Markov Logic Networks
Sagar Malhotra, Luciano Serafini
Markov Logic Networks (MLNs) define a probability distribution on relational structures over varying domain sizes. Many works have noticed that MLNs, like many other relational mod…
Weighted Model Counting in FO2 with Cardinality Constraints and Counting Quantifiers: A Closed Form Formula
Sagar Malhotra, Luciano Serafini
Weighted First-Order Model Counting (WFOMC) computes the weighted sum of the models of a first-order logic theory on a given finite domain. First-Order Logic theories that admit po…
Weighted Model Counting in the two variable fragment with Cardinality Constraints: A Closed Form Formula
Sagar Malhotra, Luciano Serafini
Weighted First-Order Model Counting (WFOMC) computes the weighted sum of the models of a first-order theory on a given finite domain. WFOMC has emerged as a fundamental tool for pr…